From 61a4fbee7c528e10b47bc3aac940225872d839c1 Mon Sep 17 00:00:00 2001 From: Patrick Callahan Date: Fri, 31 May 2024 16:48:59 +0200 Subject: [PATCH] Built site for gh-pages --- .nojekyll | 1 + assets/1000hist10N01.png | Bin 0 -> 15793 bytes assets/24hist1000N01.png | Bin 0 -> 20721 bytes assets/24hist10N01.png | Bin 0 -> 11586 bytes assets/conclude.png | Bin 0 -> 74424 bytes assets/define.png | Bin 0 -> 131208 bytes assets/download.PNG | Bin 0 -> 86606 bytes assets/existing-directory.PNG | Bin 0 -> 38617 bytes assets/explore.png | Bin 0 -> 50078 bytes assets/files-list.PNG | Bin 0 -> 91131 bytes assets/find-directory.PNG | Bin 0 -> 41025 bytes assets/generate.png | Bin 0 -> 177815 bytes assets/hist-power.png | Bin 0 -> 13955 bytes assets/hist10N01.png | Bin 0 -> 11910 bytes assets/logreg-results.png | Bin 0 -> 183932 bytes assets/musd-24-10-N01.png | Bin 0 -> 16933 bytes assets/musd-24-1000-N01.png | Bin 0 -> 14867 bytes assets/replicate1.png | Bin 0 -> 36080 bytes 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site_libs/quarto-search/fuse.min.js | 9 + site_libs/quarto-search/quarto-search.js | 1286 ++++++++++ styles.css | 5 + tutorial_pages/basic-principles.html | 701 ++++++ tutorial_pages/check-alpha.html | 763 ++++++ tutorial_pages/check-power.html | 764 ++++++ tutorial_pages/definition.html | 699 ++++++ tutorial_pages/download-repo.html | 723 ++++++ tutorial_pages/dry-rule.html | 756 ++++++ tutorial_pages/general-structure.html | 703 ++++++ tutorial_pages/limitations.html | 701 ++++++ .../number-of-simulations-nrep.html | 713 ++++++ tutorial_pages/purpose.html | 722 ++++++ tutorial_pages/random-numbers-generators.html | 717 ++++++ tutorial_pages/real-life-example.html | 727 ++++++ tutorial_pages/repeat.html | 703 ++++++ tutorial_pages/resources.html | 746 ++++++ tutorial_pages/sample-size-n.html | 726 ++++++ tutorial_pages/seed.html | 706 ++++++ .../simulate-for-preregistration.html | 724 ++++++ 60 files changed, 18187 insertions(+) create mode 100644 .nojekyll create mode 100644 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    Introduction to Simulations in R

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    About this work

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    This tutorial was created by Malika Ihle based on materials from Joel Pick, Hadley Wickham, Kevin Hallgren, and with contributions from James Smith.
    +It is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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    Prerequisites

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    Prior to the session: optional preparation to get familiarised with the subject

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    1. Watch this 30 min introduction to credible research, which contextualise the importance of simulations for reliable research.

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    2. Read Hallgren A. K. 2013. Conducting simulation studies in the R programming environment. Tutor Quant Methods Psychol. ; 9(2): 43–60.

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    Self-paced workshop

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    How it works

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    The self-paced tutorial (pages linked below) will alternate presentation of concepts and simple exercises for you to try to apply them in R. Each time you see written YOUR TURN, switch to your local copy of the exercise script (you can chose between a file with or without the solutions depending on e.g. your level of familiarity with R), review the examples if needed, complete the exercise, and check out the proposed answer (which often contains additional tips). Come back to the online tutorial and after finishing one page, you can click ‘next’ at the bottom to continue. The exercise script contains code for all the exercises and code that generates the plots that appear in the online tutorial, all in order of appearance in the tutorial.

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    It is necessary that you work through the sections of the tutorial in order. Please read the blurbs of each sections below to get an overview of this workshop. Then click on the first page ‘Download the material’ and follow along by clicking ‘Next’ at the bottom of each page! You can get back to this overview at any time by clicking on the title ‘Introduction-Simulations-in-R’ at the top of each page.

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    Tutorial

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    + + Back to top
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    + + + + + + \ No newline at end of file diff --git a/search.json b/search.json new file mode 100644 index 0000000..1fcdabb --- /dev/null +++ b/search.json @@ -0,0 +1,316 @@ +[ + { + "objectID": "tutorial_pages/simulate-for-preregistration.html", + "href": "tutorial_pages/simulate-for-preregistration.html", + "title": "Simulating data to check or preregister code", + "section": "", + "text": "Simulating data to check or preregister code\nOne of the simplest uses of simulations is to make a dataset on which you can run and therefore pre-specify your analytic code.\nFor example, let’s say you plan to collect an observational dataset and want to look at the effect of smoking on lung cancer. A typical protocol might state that the data will be analysed by logistic regression, adjusting for confounders. But which confounders exactly? How will each variable be coded? What type of logistic regression will you use?\nMaking a dataset that has the variables you expect your real dataset will have allows you to exactly state (in code rather than potentially ambiguous words) what you will do.\nThis is really useful if you want input from e.g. a statistician - they can look at your code and more clearly see what you are trying to do. It has the added benefit of forcing you to really think about what your dataset will look like! I’ve found this to be very useful in the past.\nIt’s very easy to make a dataset. Let’s take a simple example. We are interested in only 4 variables:\n\nsmoking status\nlung cancer\nsex\nage\n\nHere is the top of the simulated dataset.\n \n\n\nYOUR TURN:\n\nCan you recreate it? Try it yourself!\nHINT: use the data.frame(), sample(), and rnorm() functions.\nNow you’ve made a dataset, try to run a logistic regression on your data with lung cancer as the outcome.\nDo you face any error message? Does the data need to be in a specific format for statistical models to run?\n\n\nOnce we have a working model, we can look at the summary and make sure it behaves as we expected. And we can share the code with collaborators or reviewers.\nYou could combine this approach with the sampling approaches you learned in the power/alpha simulation session to look at power for more complex analyses like this.\n \n\nAnd that’s it! We have written down our code in a way that is totally unambiguous. This practice is useful for any project: you can include the exact code that you plan to use in a preregistration, and reviewers will be able to verify that you did what you planned.\nA real example of a simple simulation like this, used in the submission of a registered report, can be found in the rmd file here.\n\n\n\n\n\n Back to top", + "crumbs": [ + "Tutorial", + "Simulate to prepare a preregistration" + ] + }, + { + "objectID": "tutorial_pages/sample-size-n.html", + "href": "tutorial_pages/sample-size-n.html", + "title": "Sample size n", + "section": "", + "text": "Sample size n\nHow many values should you generate within a simulation? Let’s explore.\nIf I draw 10 data points from a normal distribution of mean 0 and sd 1, or N(0,1), after setting the seed to 10 (for no specific reason), here is the distribution of the values I get:\n1 sim of N(0,1) with n=10\n\n\nand if I replicate this simulation 24 times, here are the distributions of the 10 values pseudo-randomly sampled from N(0,1):\n24 sims of N(0,1) with n=10\n \n\nNote that because we are drawing from N(0,1), we expect the mean of the values drawn (mean(x), blue lines) to be very closed to 0, the mean of the normal distribution we sample from (red dashed lines).\n\nHow distributed are the means and standard deviations of the 24 simulations of 10 sampled values from N(0,1)?\nDistributions of the means and SDs from 24 sims N(0,1) with n=10\n \n\nNow, let’s do the same with a sample size n of 1000.\n24 sims of the same distribution N(0,1) with n=1000\n \n\nDistributions of the means and SDs from 24 sims N(0,1) with n=1000\n \n\n\nConclusion\nThe sample size within a simulation affects the precision with which the parameters of that distribution can be estimated.\nWhat should determine the sample size within your simulation?\n–> a sample size that is relevant to the context of the simulation, e.g. the sample size you will be able to reach in your study, the minimum sample size that would allow you to detect the smallest effect of interest (as determined by a power analysis, which we will cover in a moment).\n\n\n\n\n\n\n Back to top", + "crumbs": [ + "Tutorial", + "Sample size `n`" + ] + }, + { + "objectID": "tutorial_pages/repeat.html", + "href": "tutorial_pages/repeat.html", + "title": "Repetition", + "section": "", + "text": "Repetition\nThe function * replicate(nrep, expression) repeats the expression provided nrep times.\ne.g. replicate(10, mean(rnorm(100))) reads: ‘draw 100 values from a normal distribution with a mean 0 and sd 1 (the default values), calculate the mean of these 100 values, and do all that 10 times.’\n\nYOUR TURN:\nIn your local exercise script:\nReplicate 1000 times the mean of 10 values drawn from a unifrom distribution between 0 and 10.\nReplicate 100 times the mean of 50 values drawn from a normal distribution of mean 10 and standard deviation 5.\nMake a histogram of each of your results, are the distributions looking as expected?\n\n\n\n\n\n Back to top", + "crumbs": [ + "Tutorial", + "Repeat" + ] + }, + { + "objectID": "tutorial_pages/random-numbers-generators.html", + "href": "tutorial_pages/random-numbers-generators.html", + "title": "Random number generators", + "section": "", + "text": "Random number generators\nR contains several functions to generate random numbers.\nType ?function in your consol to get information on the function’s arguments (i.e. the values that must be provided to obtain the function’s result).\nThe function\n* sample(x, n, replace=FALSE) draws n values from a given vector x without replacement (by default) .\nSampling without replacement means that when you repeatedly draw e.g. 1 item from a pool of items, any item selected during the first draw is not available for selection during the second draw, and the first and second selected items are not in the pool to select from during the third draw, etc. Sampling with replacement means that all the original options are available at each draw.\n\nYOUR TURN:\nSample 100 values between 3 and 103 with replacement.\nFor this, open the file ./exercise_script.R from the root of your local repository (with or without answers), review the examples if needed, complete the exercise, and check out the proposed answer.\n\nThe following functions draw n values from distributions with the specified parameters\n* runif(n, min, max) draws n values from a uniform distribution with the specified min and max\n* rpois(n, lambda) draws n values from a poisson distribution with the specified lambda\n* rnorm(n, mean, sd) draws n values from a normal distribution with the specified mean and standard deviation\n* rbinom(n, prob) draws n values from a binomial distribution with the specified probability\n\nYOUR TURN:\nDraw 100 values from a normal distribution with a mean of 0 and a sd of 1.\nDraw 50 values from a normal distribution with a mean of 10 and sd of 5.\nDraw 1000 values from a poisson distribution with a lambda of 50.\nDraw 30 values from a uniform distribution between 0 and 10.\nTry it out in your local exercise script.\n\n\n\n\n\n Back to top", + "crumbs": [ + "Tutorial", + "Random Number Generators" + ] + }, + { + "objectID": "tutorial_pages/number-of-simulations-nrep.html", + "href": "tutorial_pages/number-of-simulations-nrep.html", + "title": "Number of simulations nrep", + "section": "", + "text": "Number of simulations nrep\nSampling theory applies to the number of simulations nrep just as much as the sample size n within a simulation.\nmeans and SDs from 24 sims N(0,1) with n=10\n \n\nNow, let’s do the same with a number of repeats nrep of 1000.\nmeans and SDs from 1000 sims N(0,1) with n=10\n \n\n\nConclusion\nThe number of simulations needs to be a big enough number to obtain a good representation of the distribution of the simulation results, e.g. 1000.\n\nYOUR TURN:\nThe code generating the data and plots presented above are included in your exercice script. Feel free to modify the parameters of the functions that simulate data and plot the results to better understand the principles presented in these two pages, but, at this stage, there is no need to fully understand the code that generates the plots.\n\n\n\n\n\n\n Back to top", + "crumbs": [ + "Tutorial", + "Number of simulations `nrep`" + ] + }, + { + "objectID": "tutorial_pages/general-structure.html", + "href": "tutorial_pages/general-structure.html", + "title": "General structure of a simulation", + "section": "", + "text": "General structure of a simulation\n\ndefine what type of data and variables need to be simulated, i.e. their distribution, their class (e.g. factor vs numerical value), sample sizes (within a dataset, and number of replicates), what will need to vary (e.g. the strength of relationship)\ngenerate data, random data or data including an effect (e.g. an imposed correlation between two variables)\nrun the statistical test you think is appropriate, and record the relevant statistic (e.g. p-value)\nreplicate step 2 and 3 to get the distribution of the statistic of interest\ntry out different parameter sets (explore the parameter space for which results are similar)\nanalyze and interpret the combined results of many simulations within each set of parameters. For instance, check that you only get a significant result in 5% of the simulations (if alpha = 0.05) when you simulated no effect; and that you get at a significant result in 80% of the simulations (if you targeted a power of 80%) when you simulated an effect\n\n\n\n\n\n\n Back to top", + "crumbs": [ + "Tutorial", + "General structure" + ] + }, + { + "objectID": "tutorial_pages/download-repo.html", + "href": "tutorial_pages/download-repo.html", + "title": "Make this repository a local RStudio project", + "section": "", + "text": "You have two options to fetch this material:\n\n\nFork and clone this repository (here is a reminder on how to do this)\n\n\n\n1) Download the repository\nPlease download the GitHub repository that we are using today: https://github.com/MalikaIhle/Introduction-Simulations-in-R\n \n\nOnce the .zip file downloaded, extract it and place the folder in the desired directory (e.g. Documents).\n2) Turn it into a RStudio project\nIf you do not have R or RStudio installed, please follow these instructions first.\nOpen RStudio, and go to ‘File’, ‘New Project…’, and select ‘Existing Directory’.\n \n\nSelect the downloaded (and extracted) folder, by clicking on ‘Browse’, then select ‘Create Project’.\n \n\nIn the panel containing the ‘Files’ tab, find the exercise sheet, and open it by double clicking on it.", + "crumbs": [ + "Tutorial", + "Download the material" + ] + }, + { + "objectID": "tutorial_pages/download-repo.html#a.-you-know-version-control-with-git-and-github", + "href": "tutorial_pages/download-repo.html#a.-you-know-version-control-with-git-and-github", + "title": "Make this repository a local RStudio project", + "section": "", + "text": "Fork and clone this repository (here is a reminder on how to do this)", + "crumbs": [ + "Tutorial", + "Download the material" + ] + }, + { + "objectID": "tutorial_pages/download-repo.html#b.-you-do-not-know-version-control-andor-simply-want-to-download-a-copy-of-this-material", + "href": "tutorial_pages/download-repo.html#b.-you-do-not-know-version-control-andor-simply-want-to-download-a-copy-of-this-material", + "title": "Make this repository a local RStudio project", + "section": "", + "text": "1) Download the repository\nPlease download the GitHub repository that we are using today: https://github.com/MalikaIhle/Introduction-Simulations-in-R\n \n\nOnce the .zip file downloaded, extract it and place the folder in the desired directory (e.g. Documents).\n2) Turn it into a RStudio project\nIf you do not have R or RStudio installed, please follow these instructions first.\nOpen RStudio, and go to ‘File’, ‘New Project…’, and select ‘Existing Directory’.\n \n\nSelect the downloaded (and extracted) folder, by clicking on ‘Browse’, then select ‘Create Project’.\n \n\nIn the panel containing the ‘Files’ tab, find the exercise sheet, and open it by double clicking on it.", + "crumbs": [ + "Tutorial", + "Download the material" + ] + }, + { + "objectID": "tutorial_pages/check-power.html", + "href": "tutorial_pages/check-power.html", + "title": "Checking power through simulations", + "section": "", + "text": "Checking power through simulations\nThe power of a statistical test tells us the probability that the test correctly rejects the null hypothesis. In other words, if we only examine true effects, the power is the proportion of tests that will (correctly) reject the null hypothesis. Often, the power is set to 0.8, though, as with alpha = 0.05, this is an arbitrary choice.\nGenerally, we want to do power analysis before collecting data, to work out the sample size we need to detect some effect. If we are calculating a required sample size, the power analysis can also be called a sample size calculation.\nTaking the example of a t-test, we need to understand a few parameters:\n\nn, the sample size\ndelta, the difference in means that you want to be able to detect. Deciding what this value should be is tricky. You might rely on estimates from the literature (though bear in mind they are likely to be inflated), or you can use a ‘minimally important difference’: you specify the threshold below which you do not think a difference is interesting enough to be worth detecting. In a clinical trial, this might be the smallest difference that a patient would care about, for example.\nsd, the standard deviation. Usually this needs to be estimated from the literature or from pilot studies.\nsig.level, the alpha, as discussed previously.\npower, the power as defined above.\n\nYou can calculate any one of these parameters, given all of the others. We usually want to specify, delta, sd, sig.level and power and calculate the required sample size.\nWe can calculate the required sample size for a t.test using:\npower.t.test(n = NULL, delta = 0.5, sd = 1, sig.level = 0.05, power = 0.8)\nNotice that n = NULL, so this parameter is calculated.\nThe sample size n we need given this set of parameters, is 64 per group.\nJust as we can check the alpha of our test by sampling from the same distribution (i.e. simulating data without an effect), we can check the power by sampling from different distributions (i.e. simulating data with an effect).\nIf we sample values from two normal ditributions with different means (e.g. N(0,1) and N(0.5,1)), what is the minimum sample size we need to detect a significant difference in means with a t.test, 80% of the time?\n\nYOUR TURN:\nUse your simulation skills to work out the power through simulation. Write a function which:\n\nDraws n values from a random normal distribution with mean1, and another n values from a normal distribution with mean2\nCompares the means of these two samples with a t-test and extracts the p.value\n\nReplicate the function 1000 times using the parameters used in the power calculation above (that used the power.t.test function)\nCalculate the proportion of p-values that are <0.05\n\np-values of t tests comparing means from 1000 sims N(0,1) and N(0.5, 1) with n=64\n \n\nThe proportion of correctly rejected null hypotheses in the simulation is close to 0.8, which is what we would expect.\nUsing simulations for power analysis is not really necessary for simple examples like a t-test, though it is useful to check your understanding.\nWhen analyses become complex and it is hard or impossible to determine a sample size analytically (i.e. you can’t calculate it, or there’s no suitable function to use), then simulations are an indispensible tool.\nA simple example of a power analysis like the one you’ve just done can be found in the “Power analysis” section of this paper: * Blanco, David, et al (2020). “Effect of an editorial intervention to improve the completeness of reporting of randomised trials: a randomised controlled trial.” BMJ open 10.5: e036799. https://doi.org/10.1136/bmjopen-2020-036799\nA complete self-paced tutorial to simulate data for power analysis of complex statistical designs can be found here: * https://malikaihle.github.io/Simulations-for-Advanced-Power-Analyses/\n\n\n\n\n\n Back to top", + "crumbs": [ + "Tutorial", + "Simulate to check power" + ] + }, + { + "objectID": "tutorial_pages/basic-principles.html", + "href": "tutorial_pages/basic-principles.html", + "title": "Basic principles", + "section": "", + "text": "Basic principles\nBasically, a simulation consist in\n1) Generating n random numbers from a known distribution\n2) Repeating this nrep number of times\nOnce you know how to do this, the questions we will explore are:\n1) what sample size n should we use within a simulation?\n2) how many simulations nrep should we run?\n\n\n\n\n\n Back to top", + "crumbs": [ + "Tutorial", + "Basic Principles" + ] + }, + { + "objectID": "index.html", + "href": "index.html", + "title": "Introduction to Simulations in R", + "section": "", + "text": "This tutorial was created by Malika Ihle based on materials from Joel Pick, Hadley Wickham, Kevin Hallgren, and with contributions from James Smith.\nIt is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.\n\n\n\n\nHave R and RStudio installed. If you don’t, follow these instructions\n\nKnow some R basics (e.g. how to select a value in a data.frame, how to create a vector). If you don’t, visit the tutorial: https://www.tutorialspoint.com/r/index.htm\n\n\n\n\n\nWatch this 30 min introduction to credible research, which contextualise the importance of simulations for reliable research.\nRead Hallgren A. K. 2013. Conducting simulation studies in the R programming environment. Tutor Quant Methods Psychol. ; 9(2): 43–60.\n\n\n\n\n\n\nThe self-paced tutorial (pages linked below) will alternate presentation of concepts and simple exercises for you to try to apply them in R. Each time you see written YOUR TURN, switch to your local copy of the exercise script (you can chose between a file with or without the solutions depending on e.g. your level of familiarity with R), review the examples if needed, complete the exercise, and check out the proposed answer (which often contains additional tips). Come back to the online tutorial and after finishing one page, you can click ‘next’ at the bottom to continue. The exercise script contains code for all the exercises and code that generates the plots that appear in the online tutorial, all in order of appearance in the tutorial.\nIt is necessary that you work through the sections of the tutorial in order. Please read the blurbs of each sections below to get an overview of this workshop. Then click on the first page ‘Download the material’ and follow along by clicking ‘Next’ at the bottom of each page! You can get back to this overview at any time by clicking on the title ‘Introduction-Simulations-in-R’ at the top of each page.\n\n\n\n\nDownload the material - Get this tutorial onto your machine\nDefinition - what are simulations?\nPurpose - what can we use simulations for?\nBasic Principles - what do we need to create a simulation?\nRandom Number Generators - how to generate random numbers in R?\nRepeat - how to repeat the generation of random numbers multiple times?\nSetting the seed - how can you generate the same random numbers?\nSample size n - how many values should you generate within a simulation?\nNumber of simulations nrep - how many repeats of a simulation should you run?\nDry rule - how to write your own functions?\nSimulate to check alpha - write your first simulation and check the rate of false-positive findings.\n\nSimulate to check power - simulate data to perform a power analysis.\n\nSimulate to prepare a preregistration - simulate data to test statistical analyses before preregistering them.\n\nGeneral structure - what is the general structure of a simulation?\nLimitations - what are the limitations to simulations?\nReal-life example - what are real life examples of simulations?\nAdditional resources - what resource can help you write your own simulation?", + "crumbs": [ + "Home" + ] + }, + { + "objectID": "index.html#about-this-work", + "href": "index.html#about-this-work", + "title": "Introduction to Simulations in R", + "section": "", + "text": "This tutorial was created by Malika Ihle based on materials from Joel Pick, Hadley Wickham, Kevin Hallgren, and with contributions from James Smith.\nIt is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.", + "crumbs": [ + "Home" + ] + }, + { + "objectID": "index.html#prerequisites", + "href": "index.html#prerequisites", + "title": "Introduction to Simulations in R", + "section": "", + "text": "Have R and RStudio installed. If you don’t, follow these instructions\n\nKnow some R basics (e.g. how to select a value in a data.frame, how to create a vector). If you don’t, visit the tutorial: https://www.tutorialspoint.com/r/index.htm", + "crumbs": [ + "Home" + ] + }, + { + "objectID": "index.html#prior-to-the-session-optional-preparation-to-get-familiarised-with-the-subject", + "href": "index.html#prior-to-the-session-optional-preparation-to-get-familiarised-with-the-subject", + "title": "Introduction to Simulations in R", + "section": "", + "text": "Watch this 30 min introduction to credible research, which contextualise the importance of simulations for reliable research.\nRead Hallgren A. K. 2013. Conducting simulation studies in the R programming environment. Tutor Quant Methods Psychol. ; 9(2): 43–60.", + "crumbs": [ + "Home" + ] + }, + { + "objectID": "index.html#self-paced-workshop", + "href": "index.html#self-paced-workshop", + "title": "Introduction to Simulations in R", + "section": "", + "text": "The self-paced tutorial (pages linked below) will alternate presentation of concepts and simple exercises for you to try to apply them in R. Each time you see written YOUR TURN, switch to your local copy of the exercise script (you can chose between a file with or without the solutions depending on e.g. your level of familiarity with R), review the examples if needed, complete the exercise, and check out the proposed answer (which often contains additional tips). Come back to the online tutorial and after finishing one page, you can click ‘next’ at the bottom to continue. The exercise script contains code for all the exercises and code that generates the plots that appear in the online tutorial, all in order of appearance in the tutorial.\nIt is necessary that you work through the sections of the tutorial in order. Please read the blurbs of each sections below to get an overview of this workshop. Then click on the first page ‘Download the material’ and follow along by clicking ‘Next’ at the bottom of each page! You can get back to this overview at any time by clicking on the title ‘Introduction-Simulations-in-R’ at the top of each page.\n\n\n\n\nDownload the material - Get this tutorial onto your machine\nDefinition - what are simulations?\nPurpose - what can we use simulations for?\nBasic Principles - what do we need to create a simulation?\nRandom Number Generators - how to generate random numbers in R?\nRepeat - how to repeat the generation of random numbers multiple times?\nSetting the seed - how can you generate the same random numbers?\nSample size n - how many values should you generate within a simulation?\nNumber of simulations nrep - how many repeats of a simulation should you run?\nDry rule - how to write your own functions?\nSimulate to check alpha - write your first simulation and check the rate of false-positive findings.\n\nSimulate to check power - simulate data to perform a power analysis.\n\nSimulate to prepare a preregistration - simulate data to test statistical analyses before preregistering them.\n\nGeneral structure - what is the general structure of a simulation?\nLimitations - what are the limitations to simulations?\nReal-life example - what are real life examples of simulations?\nAdditional resources - what resource can help you write your own simulation?", + "crumbs": [ + "Home" + ] + }, + { + "objectID": "tutorial_pages/check-alpha.html", + "href": "tutorial_pages/check-alpha.html", + "title": "Using simulations to check alpha", + "section": "", + "text": "Using simulations to check alpha\nIn most quantitative sciences we accept a type 1 error rate of “0.05”, which is often called the alpha or significance level. This value tells us the probability of rejecting the null hypothesis (i.e. of finding an effect) given that the null hypothesis is true.\nIn other words, if there is no true effect (e.g. no difference between two groups), we would expect our null hypothesis of no effect to be rejected (incorrectly), alpha% of the time.\nIf you draw from the same normal distribution twice, will the mean of the two samples differ significantly in 5% of the cases?\n\nYOUR TURN:\nFigure out how to do a t-test in R\nGenerate two vectors of 10 values drawn from N(0,1) and compare them with a t-test\nFigure out how to extract the p-value from that object (explore your R object with the functions str or names)\nWrite a function simT that generates two vectors of n values drawn from a N(0,1), compare them with a t-test and return the p-value\nRepeat with nrep=20 and draw a histogram for n=10\nRepeat with nrep=100 and draw a histogram for n=10\n\np-values of t-tests comparing means from 20 or 100 sims N(0,1) with n=10\n \n\nIn the first case, where nrep = 20, we expect 1 out of the 20 tests to be significant (5%). In my case, I did! How many did you get?\nIn the second case, where nrep = 100, we expect 5 out of the 100 tests to be significant. In my case, I got 6. How many did you get?\nAre those deviations meaningful? Significant?\n\nYOUR TURN:\nPlot the output of the function simT with nrep=1000 and n=10 Plot the output of the function simT with nrep=1000 and n=100\n\np-values of t-tests comparing means from 1000 sims N(0,1) with n=10 or n=100\n \n\nIn both cases, we expect 50 out of the 1000 tests to be significant by chance (i.e. with a p value under 0.05). In my simulations, I get 40 and 45 false positive results, for n=10 and 100, respectively. How many did you get?\nThese proportions are not significantly different from 5%.\nprop.test(45, 1000, p = 0.05, alternative = \"two.sided\", correct = TRUE)\n1-sample proportions test with continuity correction\ndata: 45 out of 1000, null probability 0.05\nX-squared = 0.42632, df = 1, p-value = 0.5138\nIt is important to note that, although alpha = 0.05 is commonly used, this is an arbitrary choice and you should consider what is an appropriate type 1 error rate for your particular investigation.\nAltough it isn’t necessary to check that a statistical analysis as simple as a t-test does not yield more than 5% of false-positive results, when the structure of the data is complex and analysed with more advanced models (e.g. when explanatory variables are mathematically linked to each other, and are combined in a mixed-effect models), this may allow to compare different modelling approach and select the one that does not produce more than 5% false positive results.\nSuch complex example where simulations is the only viable approach to construct a statistical model that does not lead to spurious effects can be found in this paper: * Ihle, Malika, et al (2020). “Measuring Up to Reality: Null Models and Analysis Simulations to Study Parental Coordination Over Provisioning Offspring.” Frontiers in Ecology and Evolution. https://doi.org/10.3389/fevo.2019.00142\n\n\n\n\n\n Back to top", + "crumbs": [ + "Tutorial", + "Simulate to check alpha" + ] + }, + { + "objectID": "tutorial_pages/definition.html", + "href": "tutorial_pages/definition.html", + "title": "Definition", + "section": "", + "text": "Definition\n“A computer simulation (or”sim”) is an attempt to model a real-life or hypothetical situation on a computer so that it can be studied to see how the system works. By changing variables in the simulation, predictions may be made about the behavior of the system. It is a tool to virtually investigate the behavior of the system under study”\n      Wikipedia\n“A computer simulation is the generation of random data to build up an understanding of the real data and the statistical models we use to analyze them”\n      Malika\n\n\n\n\n\n Back to top", + "crumbs": [ + "Tutorial", + "Definition" + ] + }, + { + "objectID": "tutorial_pages/dry-rule.html", + "href": "tutorial_pages/dry-rule.html", + "title": "Do not Repeat Yourself - DRY rule", + "section": "", + "text": "Following the WET rule will:\n\nIncrease the difficulty of change\n\nDecrease clarity\n\nLeads to opportunities for inconsistency\n To prevent duplication and follow the DRY rule, we can write custom functions.\n\nFunctions are ‘self contained’ sets of commands that accomplish a specific task.\nFunctions usually ‘take in’ data or parameter values (these inputs are called ‘function arguments’), process it, and ‘return’ a result. You’ve already used several functions in this tutorial; for example rnorm(n, mean, sd), where n, mean, and sd are inputs and the result is a random sample from the normal distribution. The only difference here is that you are writing the function yourself. Once a function is written, it can be used over and over again by calling its name, just like other functions such as rnorm().\nTo write your own function, use the function function:\nAwesomeFunctionName <- function(argument1, argument2,…) { \n do stuff here \n}\nTo build up a function, start by writing the “stuff” to test that it works outside the function.\n\nYOUR TURN:\nCreate a function that draws a histogram of nrep mean(rnorm(100))\nModify your function to draw a histogram of nrep mean(rnorm(n))\n\nNote that it is useful to define nrep outside of the function, so users of your script can more easily change that value e.g. from a low number (to verify the script runs without error) to a large number (to obtain reliable results).", + "crumbs": [ + "Tutorial", + "Dry rule" + ] + }, + { + "objectID": "tutorial_pages/dry-rule.html#vs.-write-everything-twice---wet-rule", + "href": "tutorial_pages/dry-rule.html#vs.-write-everything-twice---wet-rule", + "title": "Do not Repeat Yourself - DRY rule", + "section": "", + "text": "Following the WET rule will:\n\nIncrease the difficulty of change\n\nDecrease clarity\n\nLeads to opportunities for inconsistency\n To prevent duplication and follow the DRY rule, we can write custom functions.\n\nFunctions are ‘self contained’ sets of commands that accomplish a specific task.\nFunctions usually ‘take in’ data or parameter values (these inputs are called ‘function arguments’), process it, and ‘return’ a result. You’ve already used several functions in this tutorial; for example rnorm(n, mean, sd), where n, mean, and sd are inputs and the result is a random sample from the normal distribution. The only difference here is that you are writing the function yourself. Once a function is written, it can be used over and over again by calling its name, just like other functions such as rnorm().\nTo write your own function, use the function function:\nAwesomeFunctionName <- function(argument1, argument2,…) { \n do stuff here \n}\nTo build up a function, start by writing the “stuff” to test that it works outside the function.\n\nYOUR TURN:\nCreate a function that draws a histogram of nrep mean(rnorm(100))\nModify your function to draw a histogram of nrep mean(rnorm(n))\n\nNote that it is useful to define nrep outside of the function, so users of your script can more easily change that value e.g. from a low number (to verify the script runs without error) to a large number (to obtain reliable results).", + "crumbs": [ + "Tutorial", + "Dry rule" + ] + }, + { + "objectID": "tutorial_pages/limitations.html", + "href": "tutorial_pages/limitations.html", + "title": "Limitations to simulations", + "section": "", + "text": "Limitations to simulations\n\nassumptions made regarding variables might not be true, e.g. the distribution of supposedly normally distributed data may not be quite normal (have a back-up analysis plan!)\nparameter space unknown (explore it a bit, use previous observations to be at least in a relevant range)\ncomputational power and time (use research software engineer staff on campus to optimize code, use parallel core processing, use server services on campus, etc.)\nsimulations might be redundant with mathematical demonstrations (I don’t mind, still useful for me!)\n\n\n\n\n\n\n Back to top", + "crumbs": [ + "Tutorial", + "Limitations" + ] + }, + { + "objectID": "tutorial_pages/purpose.html", + "href": "tutorial_pages/purpose.html", + "title": "Purpose", + "section": "", + "text": "Purpose\nYou can use computer simulations to:\n\nTest your statistical intuition or demonstrate mathematical properties you cannot easily anticipate\n\ne.g. test whether when supposedly random data are generated, there is no more than 5% of significant effects for a variable in a model\n\n\nUnderstand sampling theory, probability distributions or test whether you understand the underlying processes of your system\n\ne.g. see whether simulated data drawn from specific distribution are comparable to real data\n\n\nPerform power analyses\n\ne.g. assess whether the sample size (within a replicate) is high enough to detect an effect simulated, in more than 80% of the cases\n\n\nPerform bootstrapping to get a confidence interval around a parameter estimate\n\ni.e. bootstrapping means to sample with replacement (i.e. all the original options to draw from are available at each draw) in an observed dataset. Doing this generates new ‘simulated’ datasets. With each of them, one can run the statistical analysis made on the observed dataset, saving each time the parameter estimate of interest. After doing this multiple time, you will obtain a confidence interval for the parameter of interest\n\n\nPrepare a pre-analysis plan\n\ne.g. in order to be confident about the (confirmatory) statistical analyses you may wish to commit to before data collection, through a preregistration or registered report, practicing the analyses on a simulated dataset is very helpful! If you are still unsure about the most appropriate statistical test to apply to your data, providing a simulated dataset to a statistician or mentor will allow them to provide concrete suggestions! The simulation code containing the analyses of simulated data can be submitted along your preregistration or registered report for reviewers to exactly understand what analyses you intend on performing. Once you get your real data, you may simply plug them in this code and get the results of your confirmatory analyses immediatly!\n\n\n\n\n\n\n\n Back to top", + "crumbs": [ + "Tutorial", + "Purpose" + ] + }, + { + "objectID": "tutorial_pages/real-life-example.html", + "href": "tutorial_pages/real-life-example.html", + "title": "Real life example", + "section": "", + "text": "Real life example\nThis is a walk through one relatively simple simulation written to check whether a\ngeneralized linear model on a contingency table of counts (poisson distribution) would provide the same results as a\ngeneralized linear model with one line per observation and the occurence of the variable of interest coded as Yes/No (binomial distribution).\nI created this code while preparing my preregistration for a simple behavioral ecology experiment:\nMethods for independently manipulating palatability and color in small insect prey (article, OSF preregistration)\nThe R script screenshoted below can be found in the folder Ihle2020.\n\nThis walk through will use the steps as defined in the page ‘general structure’\n\ndefine sample sizes (within a dataset, and number of replicates), experimental design (fixed dataset structure, e.g. treatment groups, factors) and parameters that will need to vary (here, the strength of the effect)\n\n\ngenerate data (here, using sample() and the probabilities defined in step 1 and format it in two different ways to accomodate the two statistical tests to be compared.\n\n\nrun the statistical test and save the parameter estimate of interest for that iteration. Here, this is done for both statistical tests to be compared.\n\n\nreplicate step 2 (data simulation) and 3 (data analyses) to get the distribution of the parameter estimates by wrapping these steps in a function\ndefinition of the function at the beginning: \n output returned from the function at the end:\n \n replicate the function nrep number of times. Here pbreplicate is used to provide a bar of progress for R to run this command.\n \n\nexplore the parameter space. Here, vary the probabilities of sampling between 0 and 1 depending on the treatment group category.\n\n\nanalyze and interpret the combine results of many simulations. In this case, the results of the two models were qualitatively the same (comparison of results for a few simulations), and both models gave the same expected 5% false positive results when no effect were simulated. Varying the effect (the probability of sampling 0 or 1 depending on the experimental treatment) allowed to find the minimum effect size for which the number of positive results is over 80% of the tests.\n\n\n\n\n\n\n\n\n Back to top", + "crumbs": [ + "Tutorial", + "Real-life example" + ] + }, + { + "objectID": "tutorial_pages/resources.html", + "href": "tutorial_pages/resources.html", + "title": "Resources", + "section": "", + "text": "https://malikaihle.github.io/Simulations-for-Advanced-Power-Analyses/\n\n\n\n\nThe article suggested for getting familiarised with the topic prior to the session, i.e. Hallgren A. K. 2013. Conducting simulation studies in the R programming environment. Tutor Quant Methods Psychol. ; 9(2): 43–60, contains accompanying R scripts and CSV data files which you can peruse in the Hallgren2013 folder of this repository. It contains:\n\nAnnotated R syntax file for Example 1: “novel question.R”\n\nAnnotated R syntax file for Example 2: “power analysis.R”\n\nAnnotated R syntax file for Example 3: “bootstrapping.R”\nCSV dataset generated in Example 1, which is also used later in Example 2: “novel_question_output.csv”\n\nCSV dataset used in Example 3: “mediation_raw_data.csv”\n\n\n\n\nDepending on the type of simulation that would be useful for you, these articles may be of interest:\n\nJohnson, P.C.D., Barry, S.J.E., Ferguson, H.M. and Müller, P. (2015). Power analysis for generalized linear mixed models in ecology and evolution. Methods Ecol Evol, 6: 133-142. https://doi.org/10.1111/2041-210X.12306\nBlanco, David, et al (2020). “Effect of an editorial intervention to improve the completeness of reporting of randomised trials: a randomised controlled trial.” BMJ open 10.5: e036799. https://doi.org/10.1136/bmjopen-2020-036799\n\nIn the “Power analysis” section, there is a simple example of a power simulation. R code is provided in the supplementary material.\n\nGetting started simulating data in R: some helpful functions and how to use them. https://aosmith.rbind.io/2018/08/29/getting-started-simulating-data/\n\nThis blog gives a great overview of how to start simulating more complex datasets, including step by step explanations of relevant R functions\n\nPrive, F., Aschard, H., Ziyatdinov, A. and Blum, M.G.B. (2018). Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr. Bioinformatics, 34(16), 2018, 2781–2787. https://doi.org/10.1093/bioinformatics/bty185\nRonnegard, L., et al. (2016). Increasing the power of genome wide association studies in natural populations using repeated measures – evaluation and implementation. Methods in Ecology and Evolution 2016, 7, 792–799. https://doi.org/10.1111/2041-210X.12535\nDalpiaz, D. (2020) Applied Statistics with R, Chapter 7 Simple Linear Regression, section 6 Simulating SLR. https://daviddalpiaz.github.io/appliedstats/simple-linear-regression.html#simulating-slr\n\n\n\n\n\nhttps://rpubs.com/bbolker/simpower\nhttps://cran.r-project.org/web/packages/simstudy/vignettes/simstudy.html\nhttps://debruine.github.io/faux/\nhttps://github.com/simsem/simsem/wiki/Vignette\nhttps://cran.r-project.org/web/packages/simglm/vignettes/tidy_simulation.html\nhttps://rpsychologist.com/therapists-effects-longitudinal", + "crumbs": [ + "Tutorial", + "Additional resources" + ] + }, + { + "objectID": "tutorial_pages/resources.html#follow-up-self-paced-tutorial-on-simulation-of-data-analyses-for-advanced-power-analyses", + "href": "tutorial_pages/resources.html#follow-up-self-paced-tutorial-on-simulation-of-data-analyses-for-advanced-power-analyses", + "title": "Resources", + "section": "", + "text": "https://malikaihle.github.io/Simulations-for-Advanced-Power-Analyses/", + "crumbs": [ + "Tutorial", + "Additional resources" + ] + }, + { + "objectID": "tutorial_pages/resources.html#hallgren-2013", + "href": "tutorial_pages/resources.html#hallgren-2013", + "title": "Resources", + "section": "", + "text": "The article suggested for getting familiarised with the topic prior to the session, i.e. Hallgren A. K. 2013. Conducting simulation studies in the R programming environment. Tutor Quant Methods Psychol. ; 9(2): 43–60, contains accompanying R scripts and CSV data files which you can peruse in the Hallgren2013 folder of this repository. It contains:\n\nAnnotated R syntax file for Example 1: “novel question.R”\n\nAnnotated R syntax file for Example 2: “power analysis.R”\n\nAnnotated R syntax file for Example 3: “bootstrapping.R”\nCSV dataset generated in Example 1, which is also used later in Example 2: “novel_question_output.csv”\n\nCSV dataset used in Example 3: “mediation_raw_data.csv”", + "crumbs": [ + "Tutorial", + "Additional resources" + ] + }, + { + "objectID": "tutorial_pages/resources.html#other-articles", + "href": "tutorial_pages/resources.html#other-articles", + "title": "Resources", + "section": "", + "text": "Depending on the type of simulation that would be useful for you, these articles may be of interest:\n\nJohnson, P.C.D., Barry, S.J.E., Ferguson, H.M. and Müller, P. (2015). Power analysis for generalized linear mixed models in ecology and evolution. Methods Ecol Evol, 6: 133-142. https://doi.org/10.1111/2041-210X.12306\nBlanco, David, et al (2020). “Effect of an editorial intervention to improve the completeness of reporting of randomised trials: a randomised controlled trial.” BMJ open 10.5: e036799. https://doi.org/10.1136/bmjopen-2020-036799\n\nIn the “Power analysis” section, there is a simple example of a power simulation. R code is provided in the supplementary material.\n\nGetting started simulating data in R: some helpful functions and how to use them. https://aosmith.rbind.io/2018/08/29/getting-started-simulating-data/\n\nThis blog gives a great overview of how to start simulating more complex datasets, including step by step explanations of relevant R functions\n\nPrive, F., Aschard, H., Ziyatdinov, A. and Blum, M.G.B. (2018). Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr. Bioinformatics, 34(16), 2018, 2781–2787. https://doi.org/10.1093/bioinformatics/bty185\nRonnegard, L., et al. (2016). Increasing the power of genome wide association studies in natural populations using repeated measures – evaluation and implementation. Methods in Ecology and Evolution 2016, 7, 792–799. https://doi.org/10.1111/2041-210X.12535\nDalpiaz, D. (2020) Applied Statistics with R, Chapter 7 Simple Linear Regression, section 6 Simulating SLR. https://daviddalpiaz.github.io/appliedstats/simple-linear-regression.html#simulating-slr", + "crumbs": [ + "Tutorial", + "Additional resources" + ] + }, + { + "objectID": "tutorial_pages/resources.html#use-of-r-packages-to-run-simulations", + "href": "tutorial_pages/resources.html#use-of-r-packages-to-run-simulations", + "title": "Resources", + "section": "", + "text": "https://rpubs.com/bbolker/simpower\nhttps://cran.r-project.org/web/packages/simstudy/vignettes/simstudy.html\nhttps://debruine.github.io/faux/\nhttps://github.com/simsem/simsem/wiki/Vignette\nhttps://cran.r-project.org/web/packages/simglm/vignettes/tidy_simulation.html\nhttps://rpsychologist.com/therapists-effects-longitudinal", + "crumbs": [ + "Tutorial", + "Additional resources" + ] + }, + { + "objectID": "tutorial_pages/seed.html", + "href": "tutorial_pages/seed.html", + "title": "Setting the seed", + "section": "", + "text": "Setting the seed\n\nset.seed()\n\nComputers in general, and R specifically, can, in fact, only provide pseudo random number generators.\nA pseudorandom number generator’s number sequence is completely determined by its seed, i.e. a number used to initialize that sequence.\nThus, if a pseudorandom number generator is reinitialized with the same seed, it will produce the same sequence of numbers. You can set the seed (with any arbitrary number) at the beginning of a script, and, if commands drawing random numbers are ran in the exact same order, they will provide the same output in subsequent runs.\nThis is useful for sharing code and reproduce simulations, as well as for debugging code.\n\nYOUR TURN:\nIn your local exercise script:\nPlay around with the function set.seed() by running and modifying the examples provided (e.g. compare outputs with and without seeds, change the seed number)\n\n\n\n\n\n Back to top", + "crumbs": [ + "Tutorial", + "Setting the seed" + ] + } +] \ No newline at end of file diff --git a/site_libs/bootstrap/bootstrap-icons.css b/site_libs/bootstrap/bootstrap-icons.css new file mode 100644 index 0000000..285e444 --- /dev/null +++ b/site_libs/bootstrap/bootstrap-icons.css @@ -0,0 +1,2078 @@ +/*! + * Bootstrap Icons v1.11.1 (https://icons.getbootstrap.com/) + * Copyright 2019-2023 The Bootstrap Authors + * Licensed under MIT 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.nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-sm .navbar-nav-scroll{overflow:visible}.navbar-expand-sm .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-sm .navbar-toggler{display:none}.navbar-expand-sm .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-sm .offcanvas .offcanvas-header{display:none}.navbar-expand-sm .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 768px){.navbar-expand-md{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-md .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1200px){.navbar-expand-xl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xl .navbar-nav-scroll{overflow:visible}.navbar-expand-xl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xl .navbar-toggler{display:none}.navbar-expand-xl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #545555;--bs-navbar-hover-color: rgba(31, 78, 182, 0.8);--bs-navbar-disabled-color: rgba(84, 85, 85, 0.75);--bs-navbar-active-color: #1f4eb6;--bs-navbar-brand-color: #545555;--bs-navbar-brand-hover-color: #1f4eb6;--bs-navbar-toggler-border-color: rgba(84, 85, 85, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23545555' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23545555' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #fff;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #343a40;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23343a40'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2310335b'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #93c0f1;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #10335b;--bs-accordion-active-bg: #d4e6f9}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #1f4eb6;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #1f4eb6;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(52, 58, 64, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 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J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;iNumber.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu 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e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"

    "},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get 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i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else 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t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get 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e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))z.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of z.parents(t,".nav, .list-group"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(window,gs,(()=>{for(const t of z.find('[data-bs-spy="scroll"]'))Es.getOrCreateInstance(t)})),m(Es);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",Ms="Home",js="End",Fs="active",Hs="fade",Ws="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Rs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,qs=`.${Fs}[data-bs-toggle="tab"], .${Fs}[data-bs-toggle="pill"], .${Fs}[data-bs-toggle="list"]`;class Vs extends W{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),N.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?N.trigger(e,Cs,{relatedTarget:t}):null;N.trigger(t,xs,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Fs),this._activate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),N.trigger(t,ks,{relatedTarget:e})):t.classList.add(Ws)}),t,t.classList.contains(Hs)))}_deactivate(t,e){t&&(t.classList.remove(Fs),t.blur(),this._deactivate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),N.trigger(t,Os,{relatedTarget:e})):t.classList.remove(Ws)}),t,t.classList.contains(Hs)))}_keydown(t){if(![$s,Is,Ns,Ps,Ms,js].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!l(t)));let i;if([Ms,js].includes(t.key))i=e[t.key===Ms?0:e.length-1];else{const n=[Is,Ps].includes(t.key);i=b(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Vs.getOrCreateInstance(i).show())}_getChildren(){return z.find(Rs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=z.getElementFromSelector(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`${t.id}`))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=z.findOne(t,i);s&&s.classList.toggle(n,e)};n(".dropdown-toggle",Fs),n(".dropdown-menu",Ws),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains(Fs)}_getInnerElement(t){return t.matches(Rs)?t:z.findOne(Rs,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Vs.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(document,Ls,zs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this)||Vs.getOrCreateInstance(this).show()})),N.on(window,Ds,(()=>{for(const t of z.find(qs))Vs.getOrCreateInstance(t)})),m(Vs);const Ks=".bs.toast",Qs=`mouseover${Ks}`,Xs=`mouseout${Ks}`,Ys=`focusin${Ks}`,Us=`focusout${Ks}`,Gs=`hide${Ks}`,Js=`hidden${Ks}`,Zs=`show${Ks}`,to=`shown${Ks}`,eo="hide",io="show",no="showing",so={animation:"boolean",autohide:"boolean",delay:"number"},oo={animation:!0,autohide:!0,delay:5e3};class ro extends W{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return oo}static get DefaultType(){return so}static get NAME(){return"toast"}show(){N.trigger(this._element,Zs).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(eo),d(this._element),this._element.classList.add(io,no),this._queueCallback((()=>{this._element.classList.remove(no),N.trigger(this._element,to),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(N.trigger(this._element,Gs).defaultPrevented||(this._element.classList.add(no),this._queueCallback((()=>{this._element.classList.add(eo),this._element.classList.remove(no,io),N.trigger(this._element,Js)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(io),super.dispose()}isShown(){return 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b/site_libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,203 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/site_libs/quarto-html/tippy.css b/site_libs/quarto-html/tippy.css new file mode 100644 index 0000000..e6ae635 --- /dev/null +++ b/site_libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 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b/site_libs/quarto-nav/headroom.min.js new file mode 100644 index 0000000..b08f1df --- /dev/null +++ b/site_libs/quarto-nav/headroom.min.js @@ -0,0 +1,7 @@ +/*! + * headroom.js v0.12.0 - Give your page some headroom. Hide your header until you need it + * Copyright (c) 2020 Nick Williams - http://wicky.nillia.ms/headroom.js + * License: MIT + */ + +!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?module.exports=n():"function"==typeof define&&define.amd?define(n):(t=t||self).Headroom=n()}(this,function(){"use strict";function t(){return"undefined"!=typeof window}function d(t){return function(t){return t&&t.document&&function(t){return 9===t.nodeType}(t.document)}(t)?function(t){var n=t.document,o=n.body,s=n.documentElement;return{scrollHeight:function(){return Math.max(o.scrollHeight,s.scrollHeight,o.offsetHeight,s.offsetHeight,o.clientHeight,s.clientHeight)},height:function(){return t.innerHeight||s.clientHeight||o.clientHeight},scrollY:function(){return void 0!==t.pageYOffset?t.pageYOffset:(s||o.parentNode||o).scrollTop}}}(t):function(t){return{scrollHeight:function(){return Math.max(t.scrollHeight,t.offsetHeight,t.clientHeight)},height:function(){return 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b/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,289 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function dashboardOffset() { + const dashboardNavEl = window.document.getElementById( + "quarto-dashboard-header" + ); + if (dashboardNavEl !== null) { + return dashboardNavEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset() + dashboardOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + window.addEventListener( + "hashchange", + function (e) { + if ( + getComputedStyle(document.documentElement).scrollBehavior !== "smooth" + ) { + window.scrollTo(0, window.pageYOffset - headerOffset()); + } + }, + false + ); + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver(() => { + setTimeout(updateDocumentOffsetWithoutAnimation, 0); + }); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + if (links[i].href) { + links[i].dataset.originalHref = links[i].href; + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item, a.quarto-navigation-tool, a.quarto-navbar-tools, a.quarto-navbar-tools-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git a/site_libs/quarto-search/autocomplete.umd.js b/site_libs/quarto-search/autocomplete.umd.js new file mode 100644 index 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a/site_libs/quarto-search/quarto-search.js b/site_libs/quarto-search/quarto-search.js new file mode 100644 index 0000000..5f723d7 --- /dev/null +++ b/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1286 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query && mainEl) { + // perform any highlighting + highlight(escapeRegExp(query), mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + placeholder: language["search-text-placeholder"], + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // If this is a file URL, note that + + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${ + hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + document.addEventListener("keyup", (event) => { + const { key } = event; + const kbds = quartoSearchOptions["keyboard-shortcut"]; + const focusedEl = document.activeElement; + + const isFormElFocused = [ + "input", + "select", + "textarea", + "button", + "option", + ].find((tag) => { + return focusedEl.tagName.toLowerCase() === tag; + }); + + if ( + kbds && + kbds.includes(key) && + !isFormElFocused && + !document.activeElement.isContentEditable + ) { + event.preventDefault(); + window.quartoOpenSearch(); + } + }); + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = throttle(() => { + // Only do this if we're not detached + // Bug #7117 + // This will happen when the keyboard is shown on ios (resulting in a scroll) + // which then closed the search UI + if (!window.matchMedia(detachedMediaQuery).matches) { + setIsOpen(false); + } + }, 50); + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.flatMap((event) => { + // This API limits the number of items per event to 20 + const chunkSize = 20; + const itemChunks = []; + const eventItems = event.items; + for (let i = 0; i < eventItems.length; i += chunkSize) { + itemChunks.push(eventItems.slice(i, i + chunkSize)); + } + // Split the items into multiple events that can be sent + const events = itemChunks.map((items) => { + return { + ...event, + items, + }; + }); + return events; + }); + + for (const event of events) { + insights.viewedObjectIDs(event); + } + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +var shownWarning = false; + +// fuse index options +const kFuseIndexOptions = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, +}; + +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + if (window.location.protocol === "file:" && !shownWarning) { + window.alert( + "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server." + ); + shownWarning = true; + return; + } + const fuse = new window.Fuse([], kFuseIndexOptions); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href, + item.crumbs, + quartoSearchOptions + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard( + createElement, + icon, + title, + section, + text, + href, + crumbs, + quartoSearchOptions +) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContents = [iconEl, titleEl]; + const showParent = quartoSearchOptions["show-item-context"]; + if (crumbs && showParent) { + let crumbsOut = undefined; + const crumbClz = ["search-result-crumbs"]; + if (showParent === "root") { + crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined; + } else if (showParent === "parent") { + crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined; + } else { + crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined; + crumbClz.push("search-result-crumbs-wrap"); + } + + const crumbEl = createElement( + "p", + { class: crumbClz.join(" ") }, + crumbsOut + ); + titleContents.push(crumbEl); + } + + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + titleContents + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string +} + +// highlight matches +function highlight(term, el) { + const termRegex = new RegExp(term, "ig"); + const childNodes = el.childNodes; + + // walk back to front avoid mutating elements in front of us + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + + if (node.nodeType === Node.TEXT_NODE) { + // Search text nodes for text to highlight + const text = node.nodeValue; + + let startIndex = 0; + let matchIndex = text.search(termRegex); + if (matchIndex > -1) { + const markFragment = document.createDocumentFragment(); + while (matchIndex > -1) { + const prefix = text.slice(startIndex, matchIndex); + markFragment.appendChild(document.createTextNode(prefix)); + + const mark = document.createElement("mark"); + mark.appendChild( + document.createTextNode( + text.slice(matchIndex, matchIndex + term.length) + ) + ); + markFragment.appendChild(mark); + + startIndex = matchIndex + term.length; + matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); + if (matchIndex > -1) { + matchIndex = startIndex + matchIndex; + } + } + if (startIndex < text.length) { + markFragment.appendChild( + document.createTextNode(text.slice(startIndex, text.length)) + ); + } + + el.replaceChild(markFragment, node); + } + } else if (node.nodeType === Node.ELEMENT_NODE) { + // recurse through elements + highlight(term, node); + } + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text", "crumbs"].forEach( + (keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + } + ); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +let subSearchTerm = undefined; +let subSearchFuse = undefined; +const kFuseMaxWait = 125; + +async function fuseSearch(query, fuse, fuseOptions) { + let index = fuse; + // Fuse.js using the Bitap algorithm for text matching which runs in + // O(nm) time (no matter the structure of the text). In our case this + // means that long search terms mixed with large index gets very slow + // + // This injects a subIndex that will be used once the terms get long enough + // Usually making this subindex is cheap since there will typically be + // a subset of results matching the existing query + if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) { + // Use the existing subSearchFuse + index = subSearchFuse; + } else if (subSearchFuse !== undefined) { + // The term changed, discard the existing fuse + subSearchFuse = undefined; + subSearchTerm = undefined; + } + + // Search using the active fuse + const then = performance.now(); + const resultsRaw = await index.search(query, fuseOptions); + const now = performance.now(); + + const results = resultsRaw.map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + crumbs: result.item.crumbs, + }; + }); + + // If we don't have a subfuse and the query is long enough, go ahead + // and create a subfuse to use for subsequent queries + if (now - then > kFuseMaxWait && subSearchFuse === undefined) { + subSearchTerm = query; + subSearchFuse = new window.Fuse([], kFuseIndexOptions); + resultsRaw.forEach((rr) => { + subSearchFuse.add(rr.item); + }); + } + return results; +} diff --git a/styles.css b/styles.css new file mode 100644 index 0000000..42bf444 --- /dev/null +++ b/styles.css @@ -0,0 +1,5 @@ +/* css styles */ + +.nav-page .nav-page-text { + font-size: 15pt; +} diff --git a/tutorial_pages/basic-principles.html b/tutorial_pages/basic-principles.html new file mode 100644 index 0000000..4b2d5af --- /dev/null +++ b/tutorial_pages/basic-principles.html @@ -0,0 +1,701 @@ + + + + + + + + + +Introduction to Simulations in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +     + +
    + + +
    + + + +
    + + + + +
    +

    Basic principles

    +

    Basically, a simulation consist in
    +1) Generating n random numbers from a known distribution
    +2) Repeating this nrep number of times

    +

    Once you know how to do this, the questions we will explore are:
    +1) what sample size n should we use within a simulation?
    +2) how many simulations nrep should we run?

    +
    + + +
    + + Back to top
    + + +
    + + + + + + \ No newline at end of file diff --git a/tutorial_pages/check-alpha.html b/tutorial_pages/check-alpha.html new file mode 100644 index 0000000..944aca8 --- /dev/null +++ b/tutorial_pages/check-alpha.html @@ -0,0 +1,763 @@ + + + + + + + + + +Introduction to Simulations in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Using simulations to check alpha

    +

    In most quantitative sciences we accept a type 1 error rate of “0.05”, which is often called the alpha or significance level. This value tells us the probability of rejecting the null hypothesis (i.e. of finding an effect) given that the null hypothesis is true.

    +

    In other words, if there is no true effect (e.g. no difference between two groups), we would expect our null hypothesis of no effect to be rejected (incorrectly), alpha% of the time.

    +

    If you draw from the same normal distribution twice, will the mean of the two samples differ significantly in 5% of the cases?

    +
    +

    YOUR TURN:
    +Figure out how to do a t-test in R
    +Generate two vectors of 10 values drawn from N(0,1) and compare them with a t-test
    +Figure out how to extract the p-value from that object (explore your R object with the functions str or names)
    +Write a function simT that generates two vectors of n values drawn from a N(0,1), compare them with a t-test and return the p-value
    +Repeat with nrep=20 and draw a histogram for n=10
    +Repeat with nrep=100 and draw a histogram for n=10

    +
    +

    p-values of t-tests comparing means from 20 or 100 sims N(0,1) with n=10
    +

    +

    +

    In the first case, where nrep = 20, we expect 1 out of the 20 tests to be significant (5%). In my case, I did! How many did you get?
    +In the second case, where nrep = 100, we expect 5 out of the 100 tests to be significant. In my case, I got 6. How many did you get?
    +Are those deviations meaningful? Significant?

    +
    +

    YOUR TURN:
    +Plot the output of the function simT with nrep=1000 and n=10 Plot the output of the function simT with nrep=1000 and n=100

    +
    +

    p-values of t-tests comparing means from 1000 sims N(0,1) with n=10 or n=100
    +

    +

    +

    In both cases, we expect 50 out of the 1000 tests to be significant by chance (i.e. with a p value under 0.05). In my simulations, I get 40 and 45 false positive results, for n=10 and 100, respectively. How many did you get?

    +

    These proportions are not significantly different from 5%.

    +
    prop.test(45, 1000, p = 0.05, alternative = "two.sided", correct = TRUE)
    +

    1-sample proportions test with continuity correction
    +data: 45 out of 1000, null probability 0.05
    +X-squared = 0.42632, df = 1, p-value = 0.5138

    +

    It is important to note that, although alpha = 0.05 is commonly used, this is an arbitrary choice and you should consider what is an appropriate type 1 error rate for your particular investigation.

    +

    Altough it isn’t necessary to check that a statistical analysis as simple as a t-test does not yield more than 5% of false-positive results, when the structure of the data is complex and analysed with more advanced models (e.g. when explanatory variables are mathematically linked to each other, and are combined in a mixed-effect models), this may allow to compare different modelling approach and select the one that does not produce more than 5% false positive results.

    +

    Such complex example where simulations is the only viable approach to construct a statistical model that does not lead to spurious effects can be found in this paper: * Ihle, Malika, et al (2020). “Measuring Up to Reality: Null Models and Analysis Simulations to Study Parental Coordination Over Provisioning Offspring.” Frontiers in Ecology and Evolution. https://doi.org/10.3389/fevo.2019.00142

    +
    + + +
    + + Back to top
    + + +
    + + + + + + \ No newline at end of file diff --git a/tutorial_pages/check-power.html b/tutorial_pages/check-power.html new file mode 100644 index 0000000..d31e427 --- /dev/null +++ b/tutorial_pages/check-power.html @@ -0,0 +1,764 @@ + + + + + + + + + +Introduction to Simulations in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + +
    + + +
    + + + +
    + + + + +
    +

    Checking power through simulations

    +

    The power of a statistical test tells us the probability that the test correctly rejects the null hypothesis. In other words, if we only examine true effects, the power is the proportion of tests that will (correctly) reject the null hypothesis. Often, the power is set to 0.8, though, as with alpha = 0.05, this is an arbitrary choice.

    +

    Generally, we want to do power analysis before collecting data, to work out the sample size we need to detect some effect. If we are calculating a required sample size, the power analysis can also be called a sample size calculation.

    +

    Taking the example of a t-test, we need to understand a few parameters:

    +
      +
    • n, the sample size
      • +
    • delta, the difference in means that you want to be able to detect. Deciding what this value should be is tricky. You might rely on estimates from the literature (though bear in mind they are likely to be inflated), or you can use a ‘minimally important difference’: you specify the threshold below which you do not think a difference is interesting enough to be worth detecting. In a clinical trial, this might be the smallest difference that a patient would care about, for example.
      • +
    • sd, the standard deviation. Usually this needs to be estimated from the literature or from pilot studies.
      • +
    • sig.level, the alpha, as discussed previously.
      • +
    • power, the power as defined above.
      • +
    +

    You can calculate any one of these parameters, given all of the others. We usually want to specify, delta, sd, sig.level and power and calculate the required sample size.

    +

    We can calculate the required sample size for a t.test using:

    +
    power.t.test(n = NULL, delta = 0.5, sd = 1, sig.level = 0.05, power = 0.8)
    +

    Notice that n = NULL, so this parameter is calculated.

    +

    The sample size n we need given this set of parameters, is 64 per group.

    +

    Just as we can check the alpha of our test by sampling from the same distribution (i.e. simulating data without an effect), we can check the power by sampling from different distributions (i.e. simulating data with an effect).

    +

    If we sample values from two normal ditributions with different means (e.g. N(0,1) and N(0.5,1)), what is the minimum sample size we need to detect a significant difference in means with a t.test, 80% of the time?

    +
    +

    YOUR TURN:
    +Use your simulation skills to work out the power through simulation. Write a function which:

    +
      +

    1. Draws n values from a random normal distribution with mean1, and another n values from a normal distribution with mean2

      2. +

    2. Compares the means of these two samples with a t-test and extracts the p.value

      3. +
    +

    Replicate the function 1000 times using the parameters used in the power calculation above (that used the power.t.test function)

    +

    Calculate the proportion of p-values that are <0.05

    +
    +

    p-values of t tests comparing means from 1000 sims N(0,1) and N(0.5, 1) with n=64

    +



    +

    +

    The proportion of correctly rejected null hypotheses in the simulation is close to 0.8, which is what we would expect.

    +

    Using simulations for power analysis is not really necessary for simple examples like a t-test, though it is useful to check your understanding.

    +

    When analyses become complex and it is hard or impossible to determine a sample size analytically (i.e. you can’t calculate it, or there’s no suitable function to use), then simulations are an indispensible tool.

    +

    A simple example of a power analysis like the one you’ve just done can be found in the “Power analysis” section of this paper: * Blanco, David, et al (2020). “Effect of an editorial intervention to improve the completeness of reporting of randomised trials: a randomised controlled trial.” BMJ open 10.5: e036799. https://doi.org/10.1136/bmjopen-2020-036799

    +

    A complete self-paced tutorial to simulate data for power analysis of complex statistical designs can be found here: * https://malikaihle.github.io/Simulations-for-Advanced-Power-Analyses/

    +
    + + +
    + + Back to top
    + + +
    + + + + + + \ No newline at end of file diff --git a/tutorial_pages/definition.html b/tutorial_pages/definition.html new file mode 100644 index 0000000..90ee2e7 --- /dev/null +++ b/tutorial_pages/definition.html @@ -0,0 +1,699 @@ + + + + + + + + + +Introduction to Simulations in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + +
    + + + + +
    +

    Definition

    +

    “A computer simulation (or”sim”) is an attempt to model a real-life or hypothetical situation on a computer so that it can be studied to see how the system works. By changing variables in the simulation, predictions may be made about the behavior of the system. It is a tool to virtually investigate the behavior of the system under study”
    +      Wikipedia

    +

    “A computer simulation is the generation of random data to build up an understanding of the real data and the statistical models we use to analyze them”
    +      Malika

    +
    + + +
    + + Back to top
    + + +
    + + + + + + \ No newline at end of file diff --git a/tutorial_pages/download-repo.html b/tutorial_pages/download-repo.html new file mode 100644 index 0000000..714a3e9 --- /dev/null +++ b/tutorial_pages/download-repo.html @@ -0,0 +1,723 @@ + + + + + + + + + +Introduction to Simulations in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + +
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    + + +
    + + + +
    + + + + +
    +

    Make this repository a local RStudio project

    +

    You have two options to fetch this material:

    +
    +

    A. You know version control with Git and GitHub

    +

    Fork and clone this repository (here is a reminder on how to do this)

    +
    +
    +

    B. You do not know version control, and/or simply want to download a copy of this material

    +

    1) Download the repository
    +Please download the GitHub repository that we are using today: https://github.com/MalikaIhle/Introduction-Simulations-in-R

    +



    +

    +

    Once the .zip file downloaded, extract it and place the folder in the desired directory (e.g. Documents).

    +

    2) Turn it into a RStudio project

    +

    If you do not have R or RStudio installed, please follow these instructions first.

    +

    Open RStudio, and go to ‘File’, ‘New Project…’, and select ‘Existing Directory’.
    +

    +

    +

    Select the downloaded (and extracted) folder, by clicking on ‘Browse’, then select ‘Create Project’.
    +

    +

    +

    In the panel containing the ‘Files’ tab, find the exercise sheet, and open it by double clicking on it.
    +

    +

    +
    + + +
    +
    + + Back to top
    + + +
    + + + + + + \ No newline at end of file diff --git a/tutorial_pages/dry-rule.html b/tutorial_pages/dry-rule.html new file mode 100644 index 0000000..7078bb5 --- /dev/null +++ b/tutorial_pages/dry-rule.html @@ -0,0 +1,756 @@ + + + + + + + + + +Introduction to Simulations in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + +
    + + + +
    + + + + +
    +

    Do not Repeat Yourself - DRY rule

    +
    +

    vs. Write Everything Twice - WET rule

    +


    Following the WET rule will:

    +
      +
    • Increase the difficulty of change
      +
      • +
    • Decrease clarity
      +
      • +
    • Leads to opportunities for inconsistency
      +
      To prevent duplication and follow the DRY rule, we can write custom functions.
      • +
    +

    Functions are ‘self contained’ sets of commands that accomplish a specific task.
    +Functions usually ‘take in’ data or parameter values (these inputs are called ‘function arguments’), process it, and ‘return’ a result. You’ve already used several functions in this tutorial; for example rnorm(n, mean, sd), where n, mean, and sd are inputs and the result is a random sample from the normal distribution. The only difference here is that you are writing the function yourself. Once a function is written, it can be used over and over again by calling its name, just like other functions such as rnorm().
    +To write your own function, use the function function:

    +
    AwesomeFunctionName <- function(argument1, argument2,…) {  
+  do stuff here  
+}
    +

    To build up a function, start by writing the “stuff” to test that it works outside the function.

    +
    +

    YOUR TURN:
    +Create a function that draws a histogram of nrep mean(rnorm(100))
    +Modify your function to draw a histogram of nrep mean(rnorm(n))

    +
    +

    Note that it is useful to define nrep outside of the function, so users of your script can more easily change that value e.g. from a low number (to verify the script runs without error) to a large number (to obtain reliable results).

    + + +
    +
    + + Back to top
    + + +
    + + + + + + \ No newline at end of file diff --git a/tutorial_pages/general-structure.html b/tutorial_pages/general-structure.html new file mode 100644 index 0000000..d3e336c --- /dev/null +++ b/tutorial_pages/general-structure.html @@ -0,0 +1,703 @@ + + + + + + + + + +Introduction to Simulations in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + +
    + +
    + + +
    + + + +
    + + + + +
    +

    General structure of a simulation

    +
      +

    1. define what type of data and variables need to be simulated, i.e. their distribution, their class (e.g. factor vs numerical value), sample sizes (within a dataset, and number of replicates), what will need to vary (e.g. the strength of relationship)

      2. +

    2. generate data, random data or data including an effect (e.g. an imposed correlation between two variables)

      3. +

    3. run the statistical test you think is appropriate, and record the relevant statistic (e.g. p-value)

      4. +

    4. replicate step 2 and 3 to get the distribution of the statistic of interest

      5. +

    5. try out different parameter sets (explore the parameter space for which results are similar)

      6. +

    6. analyze and interpret the combined results of many simulations within each set of parameters. For instance, check that you only get a significant result in 5% of the simulations (if alpha = 0.05) when you simulated no effect; and that you get at a significant result in 80% of the simulations (if you targeted a power of 80%) when you simulated an effect

      7. +
    +
    + + +
    + + Back to top
    + + +
    + + + + + + \ No newline at end of file diff --git a/tutorial_pages/limitations.html b/tutorial_pages/limitations.html new file mode 100644 index 0000000..17d7b2c --- /dev/null +++ b/tutorial_pages/limitations.html @@ -0,0 +1,701 @@ + + + + + + + + + +Introduction to Simulations in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Limitations to simulations

    +
      +

    1. assumptions made regarding variables might not be true, e.g. the distribution of supposedly normally distributed data may not be quite normal (have a back-up analysis plan!)

      2. +

    2. parameter space unknown (explore it a bit, use previous observations to be at least in a relevant range)

      3. +

    3. computational power and time (use research software engineer staff on campus to optimize code, use parallel core processing, use server services on campus, etc.)

      4. +

    4. simulations might be redundant with mathematical demonstrations (I don’t mind, still useful for me!)

      5. +
    +
    + + +
    + + Back to top
    + + +
    + + + + + + \ No newline at end of file diff --git a/tutorial_pages/number-of-simulations-nrep.html b/tutorial_pages/number-of-simulations-nrep.html new file mode 100644 index 0000000..0b4a411 --- /dev/null +++ b/tutorial_pages/number-of-simulations-nrep.html @@ -0,0 +1,713 @@ + + + + + + + + + +Introduction to Simulations in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
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    + + +
    + + + +
    + + + + +
    +

    Number of simulations nrep

    +

    Sampling theory applies to the number of simulations nrep just as much as the sample size n within a simulation.

    +

    means and SDs from 24 sims N(0,1) with n=10
    +

    +

    +

    Now, let’s do the same with a number of repeats nrep of 1000.

    +

    means and SDs from 1000 sims N(0,1) with n=10
    +

    +

    +
    +

    Conclusion

    +

    The number of simulations needs to be a big enough number to obtain a good representation of the distribution of the simulation results, e.g. 1000.

    +
    +

    YOUR TURN:
    +The code generating the data and plots presented above are included in your exercice script. Feel free to modify the parameters of the functions that simulate data and plot the results to better understand the principles presented in these two pages, but, at this stage, there is no need to fully understand the code that generates the plots.

    +
    + + +
    +
    + + Back to top
    + + +
    + + + + + + \ No newline at end of file diff --git a/tutorial_pages/purpose.html b/tutorial_pages/purpose.html new file mode 100644 index 0000000..27ad073 --- /dev/null +++ b/tutorial_pages/purpose.html @@ -0,0 +1,722 @@ + + + + + + + + + +Introduction to Simulations in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Purpose

    +

    You can use computer simulations to:

    +
      +
    • Test your statistical intuition or demonstrate mathematical properties you cannot easily anticipate +
        +
      • e.g. test whether when supposedly random data are generated, there is no more than 5% of significant effects for a variable in a model
        +
        • +
      • +
    • Understand sampling theory, probability distributions or test whether you understand the underlying processes of your system +
        +
      • e.g. see whether simulated data drawn from specific distribution are comparable to real data
        +
        • +
      • +
    • Perform power analyses +
        +
      • e.g. assess whether the sample size (within a replicate) is high enough to detect an effect simulated, in more than 80% of the cases
        +
        • +
      • +
    • Perform bootstrapping to get a confidence interval around a parameter estimate +
        +
      • i.e. bootstrapping means to sample with replacement (i.e. all the original options to draw from are available at each draw) in an observed dataset. Doing this generates new ‘simulated’ datasets. With each of them, one can run the statistical analysis made on the observed dataset, saving each time the parameter estimate of interest. After doing this multiple time, you will obtain a confidence interval for the parameter of interest
        +
        • +
      • +
    • Prepare a pre-analysis plan +
        +
      • e.g. in order to be confident about the (confirmatory) statistical analyses you may wish to commit to before data collection, through a preregistration or registered report, practicing the analyses on a simulated dataset is very helpful! If you are still unsure about the most appropriate statistical test to apply to your data, providing a simulated dataset to a statistician or mentor will allow them to provide concrete suggestions! The simulation code containing the analyses of simulated data can be submitted along your preregistration or registered report for reviewers to exactly understand what analyses you intend on performing. Once you get your real data, you may simply plug them in this code and get the results of your confirmatory analyses immediatly!
        • +
      • +
    +
    + + +
    + + Back to top
    + + +
    + + + + + + \ No newline at end of file diff --git a/tutorial_pages/random-numbers-generators.html b/tutorial_pages/random-numbers-generators.html new file mode 100644 index 0000000..f66eeb8 --- /dev/null +++ b/tutorial_pages/random-numbers-generators.html @@ -0,0 +1,717 @@ + + + + + + + + + +Introduction to Simulations in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + +
    + + + +
    + + + + +
    +

    Random number generators

    +

    R contains several functions to generate random numbers.
    +Type ?function in your consol to get information on the function’s arguments (i.e. the values that must be provided to obtain the function’s result).

    +

    The function
    +* sample(x, n, replace=FALSE) draws n values from a given vector x without replacement (by default) .

    +

    Sampling without replacement means that when you repeatedly draw e.g. 1 item from a pool of items, any item selected during the first draw is not available for selection during the second draw, and the first and second selected items are not in the pool to select from during the third draw, etc. Sampling with replacement means that all the original options are available at each draw.

    +
    +

    YOUR TURN:
    +Sample 100 values between 3 and 103 with replacement.
    +For this, open the file ./exercise_script.R from the root of your local repository (with or without answers), review the examples if needed, complete the exercise, and check out the proposed answer.

    +
    +

    The following functions draw n values from distributions with the specified parameters
    +* runif(n, min, max) draws n values from a uniform distribution with the specified min and max
    +* rpois(n, lambda) draws n values from a poisson distribution with the specified lambda
    +* rnorm(n, mean, sd) draws n values from a normal distribution with the specified mean and standard deviation
    +* rbinom(n, prob) draws n values from a binomial distribution with the specified probability

    +
    +

    YOUR TURN:
    +Draw 100 values from a normal distribution with a mean of 0 and a sd of 1.
    +Draw 50 values from a normal distribution with a mean of 10 and sd of 5.
    +Draw 1000 values from a poisson distribution with a lambda of 50.
    +Draw 30 values from a uniform distribution between 0 and 10.

    +

    Try it out in your local exercise script.

    +
    + + +
    + + Back to top
    + + +
    + + + + + + \ No newline at end of file diff --git a/tutorial_pages/real-life-example.html b/tutorial_pages/real-life-example.html new file mode 100644 index 0000000..a0be57b --- /dev/null +++ b/tutorial_pages/real-life-example.html @@ -0,0 +1,727 @@ + + + + + + + + + +Introduction to Simulations in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + +
    + + + +
    + + + + +
    +

    Real life example

    +

    This is a walk through one relatively simple simulation written to check whether a
    +generalized linear model on a contingency table of counts (poisson distribution) would provide the same results as a
    +generalized linear model with one line per observation and the occurence of the variable of interest coded as Yes/No (binomial distribution).

    +

    I created this code while preparing my preregistration for a simple behavioral ecology experiment:
    +Methods for independently manipulating palatability and color in small insect prey (article, OSF preregistration)

    +

    The R script screenshoted below can be found in the folder Ihle2020.

    +


    +

    This walk through will use the steps as defined in the page ‘general structure’

    +
      +

    1. define sample sizes (within a dataset, and number of replicates), experimental design (fixed dataset structure, e.g. treatment groups, factors) and parameters that will need to vary (here, the strength of the effect)

      +


      +

      2. +

    2. generate data (here, using sample() and the probabilities defined in step 1 and format it in two different ways to accomodate the two statistical tests to be compared.

      +


      +

      3. +

    3. run the statistical test and save the parameter estimate of interest for that iteration. Here, this is done for both statistical tests to be compared.

      +


      +

      4. +

    4. replicate step 2 (data simulation) and 3 (data analyses) to get the distribution of the parameter estimates by wrapping these steps in a function

      +

      definition of the function at the beginning:

      +
      output returned from the function at the end:
      +

      +
      replicate the function nrep number of times. Here pbreplicate is used to provide a bar of progress for R to run this command.
      +

      +

      5. +

    5. explore the parameter space. Here, vary the probabilities of sampling between 0 and 1 depending on the treatment group category.

      +


      +

      6. +

    6. analyze and interpret the combine results of many simulations. In this case, the results of the two models were qualitatively the same (comparison of results for a few simulations), and both models gave the same expected 5% false positive results when no effect were simulated. Varying the effect (the probability of sampling 0 or 1 depending on the experimental treatment) allowed to find the minimum effect size for which the number of positive results is over 80% of the tests.

      +


      +

      7. +
    +
    + + +
    + + Back to top
    + + +
    + + + + + + \ No newline at end of file diff --git a/tutorial_pages/repeat.html b/tutorial_pages/repeat.html new file mode 100644 index 0000000..165e79a --- /dev/null +++ b/tutorial_pages/repeat.html @@ -0,0 +1,703 @@ + + + + + + + + + +Introduction to Simulations in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + +
    + + + +
    + + + + +
    +

    Repetition

    +

    The function * replicate(nrep, expression) repeats the expression provided nrep times.

    +

    e.g. replicate(10, mean(rnorm(100))) reads: ‘draw 100 values from a normal distribution with a mean 0 and sd 1 (the default values), calculate the mean of these 100 values, and do all that 10 times.’

    +
    +

    YOUR TURN:
    +In your local exercise script:
    +Replicate 1000 times the mean of 10 values drawn from a unifrom distribution between 0 and 10.
    +Replicate 100 times the mean of 50 values drawn from a normal distribution of mean 10 and standard deviation 5.
    +Make a histogram of each of your results, are the distributions looking as expected?

    +
    + + +
    + + Back to top
    + + +
    + + + + + + \ No newline at end of file diff --git a/tutorial_pages/resources.html b/tutorial_pages/resources.html new file mode 100644 index 0000000..4944217 --- /dev/null +++ b/tutorial_pages/resources.html @@ -0,0 +1,746 @@ + + + + + + + + + +Introduction to Simulations in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + +
    + + + +
    + + + + +
    +

    Resources

    +
    +

    Follow-up self-paced tutorial on simulation of data analyses for advanced power analyses

    + +
    +
    +

    Hallgren 2013

    +

    The article suggested for getting familiarised with the topic prior to the session, i.e. Hallgren A. K. 2013. Conducting simulation studies in the R programming environment. Tutor Quant Methods Psychol. ; 9(2): 43–60, contains accompanying R scripts and CSV data files which you can peruse in the Hallgren2013 folder of this repository. It contains:

    +
      +

    • Annotated R syntax file for Example 1: “novel question.R”
      +

      • +

    • Annotated R syntax file for Example 2: “power analysis.R”
      +

      • +

    • Annotated R syntax file for Example 3: “bootstrapping.R”

      • +

    • CSV dataset generated in Example 1, which is also used later in Example 2: “novel_question_output.csv”
      +

      • +

    • CSV dataset used in Example 3: “mediation_raw_data.csv”

      • +
    +
    + +
    +

    use of R packages to run simulations

    + +
    + + +
    +
    + + Back to top
    + + +
    + + + + + + \ No newline at end of file diff --git a/tutorial_pages/sample-size-n.html b/tutorial_pages/sample-size-n.html new file mode 100644 index 0000000..c59982b --- /dev/null +++ b/tutorial_pages/sample-size-n.html @@ -0,0 +1,726 @@ + + + + + + + + + +Introduction to Simulations in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + +
    + + + +
    + + + + +
    +

    Sample size n

    +

    How many values should you generate within a simulation? Let’s explore.

    +

    If I draw 10 data points from a normal distribution of mean 0 and sd 1, or N(0,1), after setting the seed to 10 (for no specific reason), here is the distribution of the values I get:

    +

    1 sim of N(0,1) with n=10
    +
    +

    +

    and if I replicate this simulation 24 times, here are the distributions of the 10 values pseudo-randomly sampled from N(0,1):

    +

    24 sims of N(0,1) with n=10
    +

    +

    +

    Note that because we are drawing from N(0,1), we expect the mean of the values drawn (mean(x), blue lines) to be very closed to 0, the mean of the normal distribution we sample from (red dashed lines).
    +

    +

    How distributed are the means and standard deviations of the 24 simulations of 10 sampled values from N(0,1)?

    +

    Distributions of the means and SDs from 24 sims N(0,1) with n=10
    +

    +

    +

    Now, let’s do the same with a sample size n of 1000.

    +

    24 sims of the same distribution N(0,1) with n=1000
    +

    +

    +

    Distributions of the means and SDs from 24 sims N(0,1) with n=1000
    +

    +

    +
    +

    Conclusion

    +

    The sample size within a simulation affects the precision with which the parameters of that distribution can be estimated.

    +

    What should determine the sample size within your simulation?
    +–> a sample size that is relevant to the context of the simulation, e.g. the sample size you will be able to reach in your study, the minimum sample size that would allow you to detect the smallest effect of interest (as determined by a power analysis, which we will cover in a moment).

    +
    + + +
    +
    + + Back to top
    + + +
    + + + + + + \ No newline at end of file diff --git a/tutorial_pages/seed.html b/tutorial_pages/seed.html new file mode 100644 index 0000000..17929ce --- /dev/null +++ b/tutorial_pages/seed.html @@ -0,0 +1,706 @@ + + + + + + + + + +Introduction to Simulations in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + +
    + + + +
    + + + + +
    +

    Setting the seed

    +
      +
    • set.seed()
      • +
    +

    Computers in general, and R specifically, can, in fact, only provide pseudo random number generators.
    +A pseudorandom number generator’s number sequence is completely determined by its seed, i.e. a number used to initialize that sequence.

    +

    Thus, if a pseudorandom number generator is reinitialized with the same seed, it will produce the same sequence of numbers. You can set the seed (with any arbitrary number) at the beginning of a script, and, if commands drawing random numbers are ran in the exact same order, they will provide the same output in subsequent runs.

    +

    This is useful for sharing code and reproduce simulations, as well as for debugging code.

    +
    +

    YOUR TURN:
    +In your local exercise script:
    +Play around with the function set.seed() by running and modifying the examples provided (e.g. compare outputs with and without seeds, change the seed number)

    +
    + + +
    + + Back to top
    + + +
    + + + + + + \ No newline at end of file diff --git a/tutorial_pages/simulate-for-preregistration.html b/tutorial_pages/simulate-for-preregistration.html new file mode 100644 index 0000000..d78d4ef --- /dev/null +++ b/tutorial_pages/simulate-for-preregistration.html @@ -0,0 +1,724 @@ + + + + + + + + + +Introduction to Simulations in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + +
    + + + +
    + + + + +
    +

    Simulating data to check or preregister code

    +

    One of the simplest uses of simulations is to make a dataset on which you can run and therefore pre-specify your analytic code.

    +

    For example, let’s say you plan to collect an observational dataset and want to look at the effect of smoking on lung cancer. A typical protocol might state that the data will be analysed by logistic regression, adjusting for confounders. But which confounders exactly? How will each variable be coded? What type of logistic regression will you use?

    +

    Making a dataset that has the variables you expect your real dataset will have allows you to exactly state (in code rather than potentially ambiguous words) what you will do.

    +

    This is really useful if you want input from e.g. a statistician - they can look at your code and more clearly see what you are trying to do. It has the added benefit of forcing you to really think about what your dataset will look like! I’ve found this to be very useful in the past.

    +

    It’s very easy to make a dataset. Let’s take a simple example. We are interested in only 4 variables:

    +
      +
    • smoking status
      • +
    • lung cancer
      • +
    • sex
      • +
    • age
      • +
    +

    Here is the top of the simulated dataset.

    +



    +

    +
    +

    YOUR TURN:

    +
      +

    1. Can you recreate it? Try it yourself!

      +

      HINT: use the data.frame(), sample(), and rnorm() functions.

      2. +

    2. Now you’ve made a dataset, try to run a logistic regression on your data with lung cancer as the outcome.

      3. +

    3. Do you face any error message? Does the data need to be in a specific format for statistical models to run?

      4. +
    +
    +

    Once we have a working model, we can look at the summary and make sure it behaves as we expected. And we can share the code with collaborators or reviewers.

    +

    You could combine this approach with the sampling approaches you learned in the power/alpha simulation session to look at power for more complex analyses like this.

    +



    +

    +

    And that’s it! We have written down our code in a way that is totally unambiguous. This practice is useful for any project: you can include the exact code that you plan to use in a preregistration, and reviewers will be able to verify that you did what you planned.

    +

    A real example of a simple simulation like this, used in the submission of a registered report, can be found in the rmd file here.

    +
    + + +
    + + Back to top
    + + +
    + + + + + + \ No newline at end of file
    + +

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