From 130a3168b9fe4b5fec3167978adb6275f433a9f7 Mon Sep 17 00:00:00 2001 From: Phil Bouchet Date: Thu, 11 Feb 2021 11:21:49 +0000 Subject: [PATCH] Update website pages --- DESCRIPTION | 2 +- R/zzz.R | 2 +- docs/404.html | 2 +- docs/LICENSE-text.html | 2 +- docs/articles/dsmextra-bigdata.html | 59 +++--- .../anchor-sections-1.0/anchor-sections.css | 4 + .../anchor-sections-1.0/anchor-sections.js | 33 ++++ .../header-attrs-2.5/header-attrs.js | 12 ++ .../articles/dsmextra-covariateselection.html | 183 +++++++++++++----- .../anchor-sections-1.0/anchor-sections.css | 4 + .../anchor-sections-1.0/anchor-sections.js | 33 ++++ .../figure-html/unnamed-chunk-5-4.png | Bin 102404 -> 102415 bytes .../header-attrs-2.5/header-attrs.js | 12 ++ docs/articles/dsmextra.css | 3 + docs/articles/dsmextra.html | 121 ++++++------ .../anchor-sections-1.0/anchor-sections.css | 4 + .../anchor-sections-1.0/anchor-sections.js | 33 ++++ .../header-attrs-2.5/header-attrs.js | 12 ++ docs/articles/index.html | 2 +- docs/authors.html | 2 +- docs/index.html | 2 +- docs/pkgdown.yml | 4 +- docs/reference/ExDet.html | 2 +- docs/reference/acacia.html | 2 +- docs/reference/compare_covariates.html | 2 +- docs/reference/compute_extrapolation.html | 4 +- docs/reference/compute_nearby.html | 2 +- docs/reference/dsmextra.html | 2 +- docs/reference/extrapolation_analysis.html | 4 +- docs/reference/index.html | 2 +- docs/reference/map_extrapolation.html | 4 +- .../print.extrapolation_results_summary.html | 2 +- docs/reference/spermwhales.html | 2 +- docs/reference/summarise_extrapolation.html | 2 +- .../summary.extrapolation_results.html | 2 +- docs/reference/whatif.html | 2 +- docs/reference/whatif.opt.html | 2 +- vignettes/dsmextra.css | 3 + 38 files changed, 407 insertions(+), 163 deletions(-) create mode 100644 docs/articles/dsmextra-bigdata_files/anchor-sections-1.0/anchor-sections.css create mode 100644 docs/articles/dsmextra-bigdata_files/anchor-sections-1.0/anchor-sections.js create mode 100644 docs/articles/dsmextra-bigdata_files/header-attrs-2.5/header-attrs.js create mode 100644 docs/articles/dsmextra-covariateselection_files/anchor-sections-1.0/anchor-sections.css create mode 100644 docs/articles/dsmextra-covariateselection_files/anchor-sections-1.0/anchor-sections.js create mode 100644 docs/articles/dsmextra-covariateselection_files/header-attrs-2.5/header-attrs.js create mode 100644 docs/articles/dsmextra_files/anchor-sections-1.0/anchor-sections.css create mode 100644 docs/articles/dsmextra_files/anchor-sections-1.0/anchor-sections.js create mode 100644 docs/articles/dsmextra_files/header-attrs-2.5/header-attrs.js diff --git a/DESCRIPTION b/DESCRIPTION index f47a3fe..15c8154 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: dsmextra Title: A Toolkit for Extrapolation Assessments in Density Surface Models -Version: 1.1.3 +Version: 1.1.4 Authors@R: c(person("Phil", "Bouchet", email = "pjbouchet@gmail.com", role = c("aut", "cre")), person("David", "Miller", email = "dlm22@st-andrews.ac.uk", role = c("aut")), person("Laura", "Mannocci", email = "laura.mannocci@duke.edu", role = c("aut")), person("Jason", "Roberts", email = "jason.roberts@duke.edu", role = c("aut")), person("Catriona", "Harris", email = "Catriona.Harris@st-andrews.ac.uk", role = c("aut")), person("Len", "Thomas", email = "len.thomas@st-andrews.ac.uk", role = c("aut"))) Description: User-friendly tools for the detection and assessment of extrapolation in multivariate environmental space (Bouchet et al. 2019), with applications to density surface models of line transect data. Provides convenience functions for summarising and visualising extrapolation in prediction areas. License: GPL-3 + file LICENSE diff --git a/R/zzz.R b/R/zzz.R index 38b1e73..2051b5e 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -1,6 +1,6 @@ .onAttach <- function(libname, pkgname) { packageStartupMessage("-----------------------------------------------\n", - "dsmextra: version 1.1.3:\n", + "dsmextra: version 1.1.4:\n", "-----------------------------------------------\n", "* Please cite as:\nBouchet PJ, Miller DL, Roberts JJ, Mannocci L, Harris CM, Thomas L (2020). dsmextra: Extrapolation assessment tools for density surface models. Methods in Ecology and Evolution. DOI: 10.1111/2041-210X.13469\n", "\n* Quick start guide:\nA vignette is available at:\nhttps://densitymodelling.github.io/dsmextra/\n", diff --git a/docs/404.html b/docs/404.html index 28ef759..b8185cb 100644 --- a/docs/404.html +++ b/docs/404.html @@ -71,7 +71,7 @@ dsmextra - 1.1.2 + 1.1.4 diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index 87fbebb..655120f 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -71,7 +71,7 @@ dsmextra - 1.1.2 + 1.1.4 diff --git a/docs/articles/dsmextra-bigdata.html b/docs/articles/dsmextra-bigdata.html index 37616fe..918753a 100644 --- a/docs/articles/dsmextra-bigdata.html +++ b/docs/articles/dsmextra-bigdata.html @@ -31,7 +31,7 @@ dsmextra - 1.1.2 + 1.1.4 @@ -75,7 +75,8 @@ - + +
@@ -84,7 +85,7 @@

Using dsmextra with ‘big data’: An example, with a note on covariate transformations

Phil J. Bouchet, David L. Miller, Jason Roberts, Laura Mannocci, Catriona M Harris, Len Thomas

- Centre for Research into Ecological & Environmental Modelling, University of St Andrews

2020-10-14

+ Centre for Research into Ecological & Environmental Modelling, University of St Andrews

2021-02-11

@@ -138,7 +139,7 @@

Data

-

The data for this example are stored in a list object called ziphius. These are fully described in Roberts et al. (2016) and Mannocci et al. (2017), and remain very similar in nature to the sperm whale data showcased in the introductory vignette.

+

The data for this example are stored in a list object called ziphius. These are fully described in Roberts et al. (2016) and Mannocci et al. (2017), and remain very similar in nature to the sperm whale data showcased in the introductory vignette.

str(ziphius, max.level = 2) # Inspect the list
## List of 2
 ##  $ segs   :'data.frame': 124995 obs. of  4 variables:
@@ -385,7 +386,7 @@ 

 

 

 Finding nearby data

-
The whatif function from the Whatif package (Gandrud et al. 2017), which is called internally by compute_nearby, may not run on very large datasets. While this was not an issue with the sperm whale case study in the introductory vignette, the beaked whale dataset is several orders of magnitude larger.

+
The whatif function from the Whatif package (Gandrud et al. 2017), which is called internally by compute_nearby, may not run on very large datasets. While this was not an issue with the sperm whale case study in the introductory vignette, the beaked whale dataset is several orders of magnitude larger.

 
data("spermwhales")
 size.df <- tibble(species = c("Sperm whales", "Beaked whales"), size = c(format(prod(nrow(spermwhales$segs),
     nrow(spermwhales$predgrid)), nsmall = 0, big.mark = ","), format(prod(nrow(ziphius$segs),
@@ -414,7 +415,7 @@ 

 
  • 
 no.partitions: Number of required partitions.
    • 
 
-
    In practice, a run of compute_nearby begins with a quick assessment of the dimensions of the input data, i.e., the reference and target data.frames. If the product of their dimensions (i.e., number of segments multiplied by number of prediction grid cells) exceeds the value set for max.size, then no.partitions subsets of the data will be created and the computations run on each using map functions from the purrr package (Henry and Wickham 2019). This means that a smaller max.size will trigger partitioning on correspondingly smaller datasets. By default, max.size is set to 1e7. This value was chosen arbitrarily, and is sufficiently large as to obviate the need for partitioning on the sperm whale dataset that is shipped with dsmextra. That said, note that max.size makes little difference to computation time in the analysis of the sperm whale surveys.
    
+
    In practice, a run of compute_nearby begins with a quick assessment of the dimensions of the input data, i.e., the reference and target data.frames. If the product of their dimensions (i.e., number of segments multiplied by number of prediction grid cells) exceeds the value set for max.size, then no.partitions subsets of the data will be created and the computations run on each using map functions from the purrr package (Henry and Wickham 2019). This means that a smaller max.size will trigger partitioning on correspondingly smaller datasets. By default, max.size is set to 1e7. This value was chosen arbitrarily, and is sufficiently large as to obviate the need for partitioning on the sperm whale dataset that is shipped with dsmextra. That said, note that max.size makes little difference to computation time in the analysis of the sperm whale surveys.
    
 
    It is quite crucial, however, to use partitioning on the beaked whale data. Given the large size of the dataset, the default value of max.size is just fine in this instance, but we could lower it if we wanted. No sensitivity analysis has been conducted on no.partitions, but manual testing suggests that 10 provides a reasonable all-rounder value.
    
 
    The below code ran in a couple of hours on a recent machine.
    
 
    
@@ -426,49 +427,49 @@ 
    
                   extrapolation.object = ziphius.extrapolation$untransformed,
                   base.layer = "gray")
    -
    -

    +

    +

    map_extrapolation(map.type = "extrapolation",
                   extrapolation.object = ziphius.extrapolation$transformed,
                   base.layer = "gray")
    -
    -

    +

    +

    Most influential covariates:

    map_extrapolation(map.type = "mic", extrapolation.object = ziphius.extrapolation$untransformed)
    -
    -

    +

    +

    map_extrapolation(map.type = "mic", extrapolation.object = ziphius.extrapolation$transformed)
    -
    -

    +

    +

    Proportion of data nearby (%N):

    map_extrapolation(map.type = "nearby", extrapolation.object = ziphius.nearby$untransformed)
    -
    -

    +

    +

    map_extrapolation(map.type = "nearby", extrapolation.object = ziphius.nearby$transformed)
    -
    -

    +

    +

    Difference between the two rasters:

    map_extrapolation(map.type = "nearby", extrapolation.object = ziphius.nearby$difference)
    -
    -

    +

    +

    breakpoints <- c(-35,-0.5,0.5,10)
@@ -483,18 +484,18 @@ 
    
 
    
 
    
 References
    
-
    
-
    
-
    Gandrud C, King G, Stoll H, Zeng L (2017). ‘WhatIf: Evaluate counterfactuals. R package version 1.5-9.’ Available at: https://CRAN.R-project.org/package=WhatIf
    
+
    
+
    
+Gandrud C, King G, Stoll H, Zeng L (2017). ‘WhatIf: Evaluate counterfactuals. R package version 1.5-9.’. Available at: https://CRAN.R-project.org/package=WhatIf
 
    
-
    
-
    Henry L, Wickham H (2019). ‘Purrr: Functional programming tools. R package version 0.3.0.’ Available at: https://CRAN.R-project.org/package=purrr
    
+
    
+Henry L, Wickham H (2019). ‘Purrr: Functional programming tools. R package version 0.3.0.’. Available at: https://CRAN.R-project.org/package=purrr
 
    
-
    
-
    Mannocci L, Roberts JJ, Miller DL, Halpin PN (2017). Extrapolating cetacean densities to quantitatively assess human impacts on populations in the high seas. Conservation Biology 31, 601–614. DOI: 10.1111/cobi.12856
    
+
    
+Mannocci L, Roberts JJ, Miller DL, Halpin PN (2017). Extrapolating cetacean densities to quantitatively assess human impacts on populations in the high seas. Conservation Biology 31, 601–614. DOI: 10.1111/cobi.12856
 
    
-
    
-
    Roberts JJ, Best BD, Mannocci L, Fujioka E, Halpin PN, Palka DL, Garrison LP, Mullin KD, Cole TVN, Khan CB, McLellan WA, Pabst DA, Lockhart GG (2016). Habitat-based cetacean density models for the u.s. Atlantic and gulf of mexico. Scientific Reports 6, art22615. DOI: 10.1038/srep22615
    
+
    
+Roberts JJ, Best BD, Mannocci L, Fujioka E, Halpin PN, Palka DL, Garrison LP, Mullin KD, Cole TVN, Khan CB, McLellan WA, Pabst DA, Lockhart GG (2016). Habitat-based cetacean density models for the u.s. Atlantic and gulf of mexico. Scientific Reports 6, art22615. DOI: 10.1038/srep22615
 
    
 
    
 
    
diff --git a/docs/articles/dsmextra-bigdata_files/anchor-sections-1.0/anchor-sections.css b/docs/articles/dsmextra-bigdata_files/anchor-sections-1.0/anchor-sections.css
new file mode 100644
index 0000000..07aee5f
--- /dev/null
+++ b/docs/articles/dsmextra-bigdata_files/anchor-sections-1.0/anchor-sections.css
@@ -0,0 +1,4 @@
+/* Styles for section anchors */
+a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
+a.anchor-section::before {content: '#';}
+.hasAnchor:hover a.anchor-section {visibility: visible;}
diff --git a/docs/articles/dsmextra-bigdata_files/anchor-sections-1.0/anchor-sections.js b/docs/articles/dsmextra-bigdata_files/anchor-sections-1.0/anchor-sections.js
new file mode 100644
index 0000000..570f99a
--- /dev/null
+++ b/docs/articles/dsmextra-bigdata_files/anchor-sections-1.0/anchor-sections.js
@@ -0,0 +1,33 @@
+// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
+document.addEventListener('DOMContentLoaded', function() {
+  // Do nothing if AnchorJS is used
+  if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
+    return;
+  }
+
+  const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
+
+  // Do nothing if sections are already anchored
+  if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
+    return null;
+  }
+
+  // Use section id when pandoc runs with --section-divs
+  const section_id = function(x) {
+    return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
+            ? x.id : '');
+  };
+
+  // Add anchors
+  h.forEach(function(x) {
+    const id = x.id || section_id(x.parentElement);
+    if (id === '') {
+      return null;
+    }
+    let anchor = document.createElement('a');
+    anchor.href = '#' + id;
+    anchor.classList = ['anchor-section'];
+    x.classList.add('hasAnchor');
+    x.appendChild(anchor);
+  });
+});
diff --git a/docs/articles/dsmextra-bigdata_files/header-attrs-2.5/header-attrs.js b/docs/articles/dsmextra-bigdata_files/header-attrs-2.5/header-attrs.js
new file mode 100644
index 0000000..dd57d92
--- /dev/null
+++ b/docs/articles/dsmextra-bigdata_files/header-attrs-2.5/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+  var i, h, a;
+  for (i = 0; i < hs.length; i++) {
+    h = hs[i];
+    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
+    a = h.attributes;
+    while (a.length > 0) h.removeAttribute(a[0].name);
+  }
+});
diff --git a/docs/articles/dsmextra-covariateselection.html b/docs/articles/dsmextra-covariateselection.html
index cdc2397..db5f60e 100644
--- a/docs/articles/dsmextra-covariateselection.html
+++ b/docs/articles/dsmextra-covariateselection.html
@@ -31,7 +31,7 @@
       
       
         dsmextra
-        1.1.2
+        1.1.4
       
     
    
 
@@ -75,7 +75,8 @@
 
       
 
-      
+      
+
 
 
 
    
@@ -84,7 +85,7 @@
       
    A priori covariate selection using dsmextra
    
                         
    Phil J. Bouchet, David L. Miller, Jason Roberts, Laura Mannocci, Catriona M Harris, Len Thomas
    
             
    
-      Centre for Research into Ecological & Environmental Modelling, University of St Andrews
    2020-10-14
    
+      Centre for Research into Ecological & Environmental Modelling, University of St Andrews
    2021-02-11
    
       
       
       
@@ -97,7 +98,7 @@ 
    Phil J. Bouchet, David L. Miller, Jason Roberts, Laura Mannoc
 
    
 
    
 Preamble
    
-
    This vignette demonstrates how dsmextra can be used to guide covariate selection a priori, before model fitting. Full details on the case study can be found in the Supplementay Material accompanying Bouchet et al. (2020).
    
+
    This vignette demonstrates how dsmextra can be used to guide covariate selection a priori, before model fitting. Full details on the case study can be found in the Supplementay Material accompanying Bouchet et al. (2020).
    
 
    
 
    
 
    
@@ -159,7 +160,7 @@ 
    
 
    
 
    
 Data
    
-
    The data and custom functions required to run this example can be downloaded as a ZIP archive from the Supplementary Information section of Bouchet et al. (2020), available at https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13469. The below assumes that the data files have been saved in the working directory.
    
+
    The data and custom functions required to run this example can be downloaded as a ZIP archive from the Supplementary Information section of Bouchet et al. (2020), available at https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13469. The below assumes that the data files have been saved in the working directory.
    
 
    The environmental covariates of interest include:
    
 
      
 
    1. Bathymetric depth [static]
      2. 
@@ -181,18 +182,32 @@ 
      
 pred.grid <- raster::stack(env.rasters) %>%
   raster::projectRaster(from = ., crs = GulfMexico_proj) %>%
   raster::as.data.frame(., xy = TRUE) %>%
-  na.omit()
-
-#'---------------------------------------------
+  na.omit()
    +
    ## Warning in wkt(projfrom): CRS object has no comment
    +
    ## Warning in wkt(pfrom): CRS object has no comment
    +
    ## Warning in rgdal::rawTransform(projfrom, projto, nrow(xy), xy[, 1], xy[, : Using
+## PROJ not WKT2 strings
    +
    ## Warning in rgdal::rawTransform(fromcrs, crs, nrow(xy), xy[, 1], xy[, 2]): Using
+## PROJ not WKT2 strings
    +
    ## Warning in rgdal::rawTransform(projto_int, projfrom, nrow(xy), xy[, 1], : Using
+## PROJ not WKT2 strings
    +
    #'---------------------------------------------
 # Retrieve covariate values for each segment
 #'---------------------------------------------
 survey.segdata <- raster::projectRaster(from = raster::stack(env.rasters), crs = GulfMexico_proj) %>%
   raster::extract(x = ., y = sp::SpatialPointsDataFrame(coords = survey.segdata[, c("x", "y")],
                          data = survey.segdata, proj4string = GulfMexico_proj), sp = TRUE) %>%
   raster::as.data.frame(.) %>%
-  dplyr::select(-c(x.1, y.1))
-
-#'---------------------------------------------
+  dplyr::select(-c(x.1, y.1))
    +
    ## Warning in wkt(projfrom): CRS object has no comment
    +
    ## Warning in wkt(pfrom): CRS object has no comment
    +
    ## Warning in rgdal::rawTransform(projfrom, projto, nrow(xy), xy[, 1], xy[, : Using
+## PROJ not WKT2 strings
    +
    ## Warning in rgdal::rawTransform(fromcrs, crs, nrow(xy), xy[, 1], xy[, 2]): Using
+## PROJ not WKT2 strings
    +
    ## Warning in rgdal::rawTransform(projto_int, projfrom, nrow(xy), xy[, 1], : Using
+## PROJ not WKT2 strings
    +
    #'---------------------------------------------
 # Check collinearity between covariates
 #'---------------------------------------------
 survey.segdata[, names(env.rasters)[1:6]] %>%
@@ -205,7 +220,7 @@ 
    
 
    
 
    
 Extrapolation assessment
    
-
    # Define environmental covariates of interest
+
    # Define environmental covariates of interest
 stenella.covariates <- names(env.rasters)[1:6]
 
 # Univariate + combinatorial extrapolation (ExDet)
@@ -215,19 +230,19 @@ 
    
                                                   coordinate.system = GulfMexico_proj)
    
 
    ## Computing ...
    
 
    ## Done!
    
-
    summary(stenella.exdet)
    
+
    summary(stenella.exdet)
    
 
    ## 
 ## 
 ## Table: Extrapolation
 ## 
 ## Type            Count         Percentage  
 ## --------------  ------------  ------------
-## Univariate      7521          48.17       
+## Univariate      7515          48.13       
 ## Combinatorial   2178          13.95       
 ## -----------     -----------   ----------- 
-## Sub-total       9699          62.12       
+## Sub-total       9693          62.08       
 ## -----------     -----------   ----------- 
-## Analogue        5915          37.88       
+## Analogue        5921          37.92       
 ## -----------     -----------   ----------- 
 ## Total           15614         100         
 ## 
@@ -236,14 +251,14 @@ 
    
 ## 
 ## Type            Covariate     Count         Percentage  
 ## --------------  ------------  ------------  ------------
-## Univariate      sst           2695          17          
+## Univariate      sst           2693          17          
 ## Univariate      depth         2536          16          
 ## Univariate      dcanseam      1415          9.1         
-## Univariate      dist_coast    559           3.6         
-## Univariate      curspeed      197           1.3         
-## Univariate      slope         119           0.76        
+## Univariate      dist_coast    557           3.6         
+## Univariate      curspeed      196           1.3         
+## Univariate      slope         118           0.76        
 ## -----------     -----------   -----------   ----------- 
-## Sub-total                     7521          48          
+## Sub-total                     7515          48          
 ## -----------     -----------   -----------   ----------- 
 ## Combinatorial   depth         1633          10          
 ## Combinatorial   slope         503           3.2         
@@ -254,8 +269,8 @@ 
    
 ## -----------     -----------   -----------   ----------- 
 ## Sub-total                     2178          14          
 ## -----------     -----------   -----------   ----------- 
-## Total                         9699          62
    
-
    # Percentage of data nearby (%N)
+## Total                         9693          62
    
+
    # Percentage of data nearby (%N)
 stenella.nearby <- dsmextra::compute_nearby(samples = survey.segdata,
                                             covariate.names = stenella.covariates,
                                             prediction.grid = pred.grid,
@@ -267,11 +282,49 @@ 
    
 
    ## Calculating cumulative frequencies ...
    
 
    ## Finishing up ...
    
 
    ## Done!
    
-
    # Maps
+
    # Maps
 
 # (1) Univariate + combinatorial extrapolation (ExDet)
 dsmextra::map_extrapolation(map.type = "extrapolation",
                             extrapolation.object = stenella.exdet)
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
+## = prefer_proj): Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137
+## +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null
+## +wktext +no_defs +type=crs
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
+## prefer_proj): Discarded datum World Geodetic System 1984 in CRS definition
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
+## = prefer_proj): Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137
+## +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null
+## +wktext +no_defs +type=crs
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
+## prefer_proj): Discarded datum World Geodetic System 1984 in CRS definition
    
+
    ## Warning in raster::projectRaster(from = .x, to = ll$all, method = "ngb"): input
+## and ouput crs are the same
+
+## Warning in raster::projectRaster(from = .x, to = ll$all, method = "ngb"): input
+## and ouput crs are the same
+
+## Warning in raster::projectRaster(from = .x, to = ll$all, method = "ngb"): input
+## and ouput crs are the same
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
+## = prefer_proj): Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137
+## +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null
+## +wktext +no_defs +type=crs
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
+## prefer_proj): Discarded datum World Geodetic System 1984 in CRS definition
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
+## = prefer_proj): Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137
+## +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null
+## +wktext +no_defs +type=crs
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
+## prefer_proj): Discarded datum World Geodetic System 1984 in CRS definition
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
+## = prefer_proj): Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137
+## +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null
+## +wktext +no_defs +type=crs
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
+## prefer_proj): Discarded datum World Geodetic System 1984 in CRS definition
    
 
    ## Warning in dsmextra::map_extrapolation(map.type = "extrapolation",
 ## extrapolation.object = stenella.exdet): map_extrapolation relies on the leaflet
 ## package, which is built around a Web Mercator projection (EPSG:3857), and
@@ -280,11 +333,29 @@ 
    
 ## and the underlying raw data. The latter can be accessed directly from the
 ## extrapolation.values or gower.values objects and visualised using alternative
 ## packages such as ggplot2.
    
-
    
-
    # (2) Most influential covariates (MIC)
+
    
+
    # (2) Most influential covariates (MIC)
 dsmextra::map_extrapolation(map.type = "mic",
                             extrapolation.object = stenella.exdet,
                             base.layer = "gray")
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
+## = prefer_proj): Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137
+## +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null
+## +wktext +no_defs +type=crs
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
+## prefer_proj): Discarded datum World Geodetic System 1984 in CRS definition
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
+## = prefer_proj): Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137
+## +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null
+## +wktext +no_defs +type=crs
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
+## prefer_proj): Discarded datum World Geodetic System 1984 in CRS definition
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
+## = prefer_proj): Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137
+## +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null
+## +wktext +no_defs +type=crs
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
+## prefer_proj): Discarded datum World Geodetic System 1984 in CRS definition
    
 
    ## Warning in dsmextra::map_extrapolation(map.type = "mic", extrapolation.object
 ## = stenella.exdet, : map_extrapolation relies on the leaflet package, which
 ## is built around a Web Mercator projection (EPSG:3857), and therefore requires
@@ -292,10 +363,28 @@ 
    
 ## occur between the interactive maps shown in the viewer, and the underlying
 ## raw data. The latter can be accessed directly from the extrapolation.values or
 ## gower.values objects and visualised using alternative packages such as ggplot2.
    
-
    
-
    # (3) Percentage of data nearby (%N)
+
    
+
    # (3) Percentage of data nearby (%N)
 dsmextra::map_extrapolation(map.type = "nearby",
                             extrapolation.object = stenella.nearby)
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
+## = prefer_proj): Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137
+## +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null
+## +wktext +no_defs +type=crs
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
+## prefer_proj): Discarded datum World Geodetic System 1984 in CRS definition
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
+## = prefer_proj): Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137
+## +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null
+## +wktext +no_defs +type=crs
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
+## prefer_proj): Discarded datum World Geodetic System 1984 in CRS definition
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
+## = prefer_proj): Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137
+## +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null
+## +wktext +no_defs +type=crs
    
+
    ## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
+## prefer_proj): Discarded datum World Geodetic System 1984 in CRS definition
    
 
    ## Warning in dsmextra::map_extrapolation(map.type = "nearby", extrapolation.object
 ## = stenella.nearby): map_extrapolation relies on the leaflet package, which
 ## is built around a Web Mercator projection (EPSG:3857), and therefore requires
@@ -303,8 +392,8 @@ 
    
 ## occur between the interactive maps shown in the viewer, and the underlying
 ## raw data. The latter can be accessed directly from the extrapolation.values or
 ## gower.values objects and visualised using alternative packages such as ggplot2.
    
-
    
-
    # Compare the extent of univariate and combinatorial extrapolation 
+
    
+
    # Compare the extent of univariate and combinatorial extrapolation 
 # (ExDet metric) associated with different combinations of covariates
 dsmextra::compare_covariates(extrapolation.type = "both",
                              extrapolation.object = stenella.exdet,
@@ -321,29 +410,29 @@ 
    
 ## 
 ## Extrapolation   Minimum       n_min  Maximum                                             n_max 
 ## --------------  -----------  ------  --------------------------------------------------  ------
-## Univariate      curspeed        393  depth, slope, dist_coast, dcanseam, curspeed, sst   7521  
-## Combinatorial   depth             0  curspeed, sst                                       3542  
+## Univariate      curspeed        392  depth, slope, dist_coast, dcanseam, curspeed, sst   7515  
+## Combinatorial   depth             0  curspeed, sst                                       3541  
 ##                 slope             0  -                                                   -     
 ##                 dist_coast        0  -                                                   -     
 ##                 dcanseam          0  -                                                   -     
 ##                 curspeed          0  -                                                   -     
 ##                 sst               0  -                                                   -     
-## Both            curspeed        393  depth, dist_coast, dcanseam, curspeed, sst          9818
    
+## Both            curspeed        392  depth, dist_coast, dcanseam, curspeed, sst          9812
    
 
    
 
    
 
    
 Density surface model
    
-
    # Fit a simple hazard-rate detection model to the distance data (no covariates)
+
    # Fit a simple hazard-rate detection model to the distance data (no covariates)
 detfc.hr.null <- Distance::ds(data = distdata, truncation = max(distdata$distance),
                               key = "hr", adjustment = NULL)
    
 
    ## Fitting hazard-rate key function
    
 
    ## Key only model: not constraining for monotonicity.
    
 
    ## AIC= 841.253
    
 
    ## No survey area information supplied, only estimating detection function.
    
-
    # Plot the fitted model on top of a histogram of distances
+
    # Plot the fitted model on top of a histogram of distances
 plot(detfc.hr.null, showpoints = FALSE, lwd = 2, pl.col = "lightblue")
    
 
    
-
    # Create model formulae with and without chosen covariates 
+
    # Create model formulae with and without chosen covariates 
 
 dsm.formulas <- make_formulas(remove.covariates = c("depth", "sst"))
 
@@ -364,20 +453,20 @@ 
    
 
 AIC(dsm.0, dsm.1) # AIC scores
    
 
    ##             df      AIC
-## dsm.0 21.19302 850.0096
-## dsm.1 17.79569 858.5654
    
-
    # Perform residual checks
+## dsm.0 21.19301 850.0096
+## dsm.1 17.79570 858.5654
    
+
    # Perform residual checks
 model_checks(dsm.0)
 model_checks(dsm.1)
    
-
    # Model predictions
+
    # Model predictions
 dsm.0.pred <- predict(object = dsm.0, pred.grid, pred.grid$area)
 dsm.1.pred <- predict(object = dsm.1, pred.grid, pred.grid$area)
 
 # Abundance estimates
 sum(dsm.0.pred); sum(dsm.1.pred)
    
-
    ## [1] 4258410
    
+
    ## [1] 4258441
    
 
    ## [1] 59320.63
    
-
    # Variance estimation - there are no covariates in the detection function
+
    # Variance estimation - there are no covariates in the detection function
 # so we use dsm.var.gam rather than dsm.var.prop
 # The below code may take several minutes, so is commented out.
 
@@ -391,18 +480,18 @@ 
    
 # Plot predicted density surfaces
 plot_predictions(dsm.predictions = dsm.0.pred)
    
 
    
-
    plot_predictions(dsm.predictions = dsm.1.pred)
    
+
    plot_predictions(dsm.predictions = dsm.1.pred)
    
 
    
-
    # Plot uncertainty surfaces
+
    # Plot uncertainty surfaces
 # plot_uncertainty(varprop.output = dsm.0.var, cutpoints = c(0,1,1.5,2,3,4))
 # plot_uncertainty(varprop.output = dsm.1.var, cutpoints = c(0,1,1.5,2,3,4))
    
 
    
 
    
 
    
 References
    
-
    
-
    
-
    Bouchet P, Miller D, Roberts J, Mannocci L, Harris C, Thomas L (2020). dsmextra: Extrapolation assessment tools for density surface models. Methods in Ecology and Evolution. DOI: 10.1111/2041-210X.13469
    
+
    
+
    
+Bouchet P, Miller D, Roberts J, Mannocci L, Harris C, Thomas L (2020). dsmextra: Extrapolation assessment tools for density surface models. Methods in Ecology and Evolution. DOI: 10.1111/2041-210X.13469
 
    
 
    
 
    
diff --git a/docs/articles/dsmextra-covariateselection_files/anchor-sections-1.0/anchor-sections.css b/docs/articles/dsmextra-covariateselection_files/anchor-sections-1.0/anchor-sections.css
new file mode 100644
index 0000000..07aee5f
--- /dev/null
+++ b/docs/articles/dsmextra-covariateselection_files/anchor-sections-1.0/anchor-sections.css
@@ -0,0 +1,4 @@
+/* Styles for section anchors */
+a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
+a.anchor-section::before {content: '#';}
+.hasAnchor:hover a.anchor-section {visibility: visible;}
diff --git a/docs/articles/dsmextra-covariateselection_files/anchor-sections-1.0/anchor-sections.js b/docs/articles/dsmextra-covariateselection_files/anchor-sections-1.0/anchor-sections.js
new file mode 100644
index 0000000..570f99a
--- /dev/null
+++ b/docs/articles/dsmextra-covariateselection_files/anchor-sections-1.0/anchor-sections.js
@@ -0,0 +1,33 @@
+// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
+document.addEventListener('DOMContentLoaded', function() {
+  // Do nothing if AnchorJS is used
+  if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
+    return;
+  }
+
+  const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
+
+  // Do nothing if sections are already anchored
+  if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
+    return null;
+  }
+
+  // Use section id when pandoc runs with --section-divs
+  const section_id = function(x) {
+    return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
+            ? x.id : '');
+  };
+
+  // Add anchors
+  h.forEach(function(x) {
+    const id = x.id || section_id(x.parentElement);
+    if (id === '') {
+      return null;
+    }
+    let anchor = document.createElement('a');
+    anchor.href = '#' + id;
+    anchor.classList = ['anchor-section'];
+    x.classList.add('hasAnchor');
+    x.appendChild(anchor);
+  });
+});
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diff --git a/docs/articles/dsmextra-covariateselection_files/header-attrs-2.5/header-attrs.js b/docs/articles/dsmextra-covariateselection_files/header-attrs-2.5/header-attrs.js
new file mode 100644
index 0000000..dd57d92
--- /dev/null
+++ b/docs/articles/dsmextra-covariateselection_files/header-attrs-2.5/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+  var i, h, a;
+  for (i = 0; i < hs.length; i++) {
+    h = hs[i];
+    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
+    a = h.attributes;
+    while (a.length > 0) h.removeAttribute(a[0].name);
+  }
+});
diff --git a/docs/articles/dsmextra.css b/docs/articles/dsmextra.css
index b4f85dd..6e326a3 100644
--- a/docs/articles/dsmextra.css
+++ b/docs/articles/dsmextra.css
@@ -96,3 +96,6 @@ ul{padding-left: 1.2em;}
 .figure{
   display: block;
   margin: 0 auto;}
+
+.csl-entry{
+  margin-bottom: 10px !important;}
diff --git a/docs/articles/dsmextra.html b/docs/articles/dsmextra.html
index 4994d65..1ffccf7 100644
--- a/docs/articles/dsmextra.html
+++ b/docs/articles/dsmextra.html
@@ -31,7 +31,7 @@
       
       
         dsmextra
-        1.1.2
+        1.1.4
       
     
    
 
@@ -75,7 +75,8 @@
 
 
 
-      
+      
+
 
 
 
    
@@ -84,7 +85,7 @@
       
    A short introduction to dsmextra
    
                         
    Phil J. Bouchet, David L. Miller, Jason Roberts, Laura Mannocci, Catriona M Harris, Len Thomas
    
             
    
-      Centre for Research into Ecological & Environmental Modelling, University of St Andrews
    2020-10-14
    
+      Centre for Research into Ecological & Environmental Modelling, University of St Andrews
    2021-02-11
    
 
 
       
@@ -97,10 +98,10 @@ 
    Phil J. Bouchet, David L. Miller, Jason Roberts, Laura Mannoc
 
    
 
    
 Preamble
    
-
    This vignette illustrates the use of the dsmextra R package for quantifying and visualising model extrapolation in environmental space. The package has its roots in density surface modelling (DSM) — as implemented in package dsm (Miller et al. 2015) — but can be easily applied to any kind of spatially-explicit ecological data. The underlying theory behind extrapolation detection is covered at length in Mesgaran et al. (2014) and King and Zeng (2007). A full description of the package is given in Bouchet et al. (2020).
    
-
    dsmextra builds upon the functions available in the ecospat (Broennimann et al. 2016) and WhatIf (Gandrud et al. 2017) packages to provide user-friendly tools relevant to the analysis of ecological data, including distance sampling data. Specifically, dsmextra enables an a priori evaluation of environmental extrapolation, when using a model built in a reference system to make predictions in a separate target system (e.g., a different geographical area and/or a past/future time period) (Sequeira et al. 2018).
    
-
    Here, we showcase the features of dsmextra with a case study on sperm whales (Physeter macrocephalus) in the Mid-Atlantic. The data come from two shipboard line transect surveys conducted by the National Oceanic and Atmospheric Administration (NOAA)’s Northeast and Southeast Fisheries Science Centers in 2004, and represent a subset of the survey effort shown in Roberts et al. (2016) and Mannocci et al. (2017). A basic level of familiarity with density surface modelling is assumed - for technical details, see Miller et al. (2013).
    
-
    Note that the vignette accompanies a detailed technical report on extrapolation in cetacean density surface models. See Bouchet et al. (2019) for more information.
    
+
    This vignette illustrates the use of the dsmextra R package for quantifying and visualising model extrapolation in environmental space. The package has its roots in density surface modelling (DSM) — as implemented in package dsm (Miller et al. 2015) — but can be easily applied to any kind of spatially-explicit ecological data. The underlying theory behind extrapolation detection is covered at length in Mesgaran et al. (2014) and King and Zeng (2007). A full description of the package is given in Bouchet et al. (2020).
    
+
    dsmextra builds upon the functions available in the ecospat (Broennimann et al. 2016) and WhatIf (Gandrud et al. 2017) packages to provide user-friendly tools relevant to the analysis of ecological data, including distance sampling data. Specifically, dsmextra enables an a priori evaluation of environmental extrapolation, when using a model built in a reference system to make predictions in a separate target system (e.g., a different geographical area and/or a past/future time period) (Sequeira et al. 2018).
    
+
    Here, we showcase the features of dsmextra with a case study on sperm whales (Physeter macrocephalus) in the Mid-Atlantic. The data come from two shipboard line transect surveys conducted by the National Oceanic and Atmospheric Administration (NOAA)’s Northeast and Southeast Fisheries Science Centers in 2004, and represent a subset of the survey effort shown in Roberts et al. (2016) and Mannocci et al. (2017). A basic level of familiarity with density surface modelling is assumed - for technical details, see Miller et al. (2013).
    
+
    Note that the vignette accompanies a detailed technical report on extrapolation in cetacean density surface models. See Bouchet et al. (2019) for more information.
    
 
    
 
    
 
    
@@ -142,7 +143,7 @@ 
    
 
  • Generate maps of extrapolation using map_extrapolation.
    • 
 
  • Run a full assessment using extrapolation_analysis.
    • 
 
-
    The data for this example are included in dsmextra as a list containing information about both (1) the surveyed area (i.e., here, the line transects ‘chopped’ into segments) and (2) the prediction area. We refer to segs as the reference (or calibration) data/system/conditions, and to predgridas the target data/system/conditions (Sequeira et al. 2018). Note that predgrid holds the coordinates and covariate values for grid cells over which density surface model predictions are sought (although model fitting is outside the scope of this vignette).
    
+
    The data for this example are included in dsmextra as a list containing information about both (1) the surveyed area (i.e., here, the line transects ‘chopped’ into segments) and (2) the prediction area. We refer to segs as the reference (or calibration) data/system/conditions, and to predgridas the target data/system/conditions (Sequeira et al. 2018). Note that predgrid holds the coordinates and covariate values for grid cells over which density surface model predictions are sought (although model fitting is outside the scope of this vignette).
    
 
    #'---------------------------------------------
 # Load and extract the data
 #'---------------------------------------------
@@ -366,7 +367,7 @@ 
    
 Fig. 1: Distribution of sperm whale (Physeter macrocephalus) sightings within the study area off the US East coast. Surveyed transects are shown as solid lines.
 
    
 
    
-
    First, we define the projected coordinate system appropriate to the study area (i.e., Albers Equal Area, +proj=aea). Our explanatory covariates of interest are: seabed depth (Depth), sea surface temperature (SST), primary productivity (NPP), distance to the nearest canyons and seamounts (DistToCAS), and eddy kinetic energy (EKE) – see Roberts et al. (2016) for details.
    
+
    First, we define the projected coordinate system appropriate to the study area (i.e., Albers Equal Area, +proj=aea). Our explanatory covariates of interest are: seabed depth (Depth), sea surface temperature (SST), primary productivity (NPP), distance to the nearest canyons and seamounts (DistToCAS), and eddy kinetic energy (EKE) – see Roberts et al. (2016) for details.
    
 
    #'---------------------------------------------
 # Define projected coordinate system
 #'---------------------------------------------
@@ -380,7 +381,7 @@ 
    
 
    
 
    
 Quantifying extrapolation
    
-
    To assess extrapolation in environmental space, we can run the extrapolation detection (ExDet) tool proposed by Mesgaran et al. (2014) using the function compute_extrapolation. Note that this tool was originally implemented in the ecospat package, although with more limited capabilities.
    
+
    To assess extrapolation in environmental space, we can run the extrapolation detection (ExDet) tool proposed by Mesgaran et al. (2014) using the function compute_extrapolation. Note that this tool was originally implemented in the ecospat package, although with more limited capabilities.
    
 
    The following arguments are required:
    
 
      
 
    • 
@@ -454,15 +455,15 @@ 
      
 
    • 
 Univariate extrapolation occurs when ExDet values < 0. This is also known as mathematical, strict, or Type 1 extrapolation, and represents conditions outside the range of individual covariates in the reference sample.
      • 
 
    • 
-Combinatorial extrapolation occurs when ExDet values > 1. This is also known as multivariate or Type 2 extrapolation, and describes novel combinations of values encountered within the univariate range of reference covariates. Such combinations are identified based on the Mahalanobis distance metric (D2), a well-known and scale-invariant measure of multivariate outliers (Rousseeuw and Zomeren 1990).
      • 
+Combinatorial extrapolation occurs when ExDet values > 1. This is also known as multivariate or Type 2 extrapolation, and describes novel combinations of values encountered within the univariate range of reference covariates. Such combinations are identified based on the Mahalanobis distance metric (D2), a well-known and scale-invariant measure of multivariate outliers (Rousseeuw and Zomeren 1990).
      • 
 
    • Lastly, values between 0 and 1 denote predictions made in analogue conditions. These correspond to what is commonly referred to as interpolation, although if found in a different region in space or past/future period in time, then the terms geographical/temporal extrapolation are sometimes also used.
      • 
 
    
 
    
 <b>Fig. 2:</b> Schematic representation of extrapolation in multivariate environmental space, based on two hypothetical covariates. Reference data points are represented as grey circles. Shaded areas correspond to different types of extrapolation outside the envelope of the reference data. Univariate extrapolation occurs beyond the range of individual covariates. Combinatorial extrapolation occurs within this range, but outside the reference hyperspace/hypervolume. <u>Source</u>: @Bouchet2019, adapted from @Mesgaran2014.
    
-Fig. 2: Schematic representation of extrapolation in multivariate environmental space, based on two hypothetical covariates. Reference data points are represented as grey circles. Shaded areas correspond to different types of extrapolation outside the envelope of the reference data. Univariate extrapolation occurs beyond the range of individual covariates. Combinatorial extrapolation occurs within this range, but outside the reference hyperspace/hypervolume. Source: Bouchet et al. (2019), adapted from Mesgaran et al. (2014).
+Fig. 2: Schematic representation of extrapolation in multivariate environmental space, based on two hypothetical covariates. Reference data points are represented as grey circles. Shaded areas correspond to different types of extrapolation outside the envelope of the reference data. Univariate extrapolation occurs beyond the range of individual covariates. Combinatorial extrapolation occurs within this range, but outside the reference hyperspace/hypervolume. Source: Bouchet et al. (2019), adapted from Mesgaran et al. (2014).
 
    
 
    
-
    compute_extrapolation also determines which covariate makes the largest contribution to extrapolation for any given grid cell, i.e., the most influential covariate (mic) (Mesgaran et al. 2014). In univariate extrapolation, this is the covariate that leads to the highest negative univariate distance from the initial covariate range. In combinatorial extrapolation, this corresponds to the covariate whose omission (while retaining all others) makes the largest reduction in the Mahalanobis distance to the centroid of the reference data. See Mesgaran et al. (2014) for formulae and technical explanations.
    
+
    compute_extrapolation also determines which covariate makes the largest contribution to extrapolation for any given grid cell, i.e., the most influential covariate (mic) (Mesgaran et al. 2014). In univariate extrapolation, this is the covariate that leads to the highest negative univariate distance from the initial covariate range. In combinatorial extrapolation, this corresponds to the covariate whose omission (while retaining all others) makes the largest reduction in the Mahalanobis distance to the centroid of the reference data. See Mesgaran et al. (2014) for formulae and technical explanations.
    
 
    ExDet values can be retrieved from the returned list, as below:
    
 
    # Example from combinatorial extrapolation
 head(spermwhale.extrapolation$data$combinatorial)
    
@@ -580,17 +581,17 @@ 
    
 
    
 Finding nearby data
    
 
    While extrapolation is often seen as a binary concept (i.e., it either does or does not take place), it is reasonable to expect that predictions made at target points situated just outside the sampled environmental space may be more reliable than those made at points far outside it. The ExDet tool available through compute_extrapolation inherently quantifies this notion of ‘distance’ from the envelope (solid line) of the reference data (grey circles) (Figure 3A).
    
-
    However, the multivariate distribution of reference data points is often far from homogeneous. It is possible, therefore, for target points representing analogue conditions to fall within sparsely sampled regions of the reference space; or conversely, for two target points reflecting an equal degree of extrapolation to have very different amounts of reference data within their vicinity (Virgili et al. 2017; Mannocci et al. 2018).
    
+
    However, the multivariate distribution of reference data points is often far from homogeneous. It is possible, therefore, for target points representing analogue conditions to fall within sparsely sampled regions of the reference space; or conversely, for two target points reflecting an equal degree of extrapolation to have very different amounts of reference data within their vicinity (Virgili et al. 2017; Mannocci et al. 2018).
    
 
    An example of this is shown in Figure 3B, where three target points \(x_1\), \(x_2\) and \(x_3\) are located equally close to the envelope of the reference data. In essence, these reflect identical degrees of extrapolation, as defined under the ExDet framework. However, given the shape of the data cloud in multivariate space, it is clear that predictions made at target point \(x_1\) will far ‘better informed’ by the sample than those made at \(x_2\) or \(x_3\), as a bigger cluster of reference points lies in their vicinity (green circle around \(x_1\)).
    
 
    
 <b>Fig. 3:</b> Conceptual representation of two key extrapolation metrics. (A) Distance from the sampled environmental space. A target point far from the envelope of the reference data (eg. falling in the yellow area) is arguably 'more of an extrapolation' than one close to it (eg. falling in the purple area).  (B) Neighbourhood (syn. percentage of data nearby). Due to the shape of the reference data cloud, the amount of sample information available to 'inform' predictions made at target points can vary considerably. <u>Source</u>: @Bouchet2019.
    
-Fig. 3: Conceptual representation of two key extrapolation metrics. (A) Distance from the sampled environmental space. A target point far from the envelope of the reference data (eg. falling in the yellow area) is arguably ‘more of an extrapolation’ than one close to it (eg. falling in the purple area). (B) Neighbourhood (syn. percentage of data nearby). Due to the shape of the reference data cloud, the amount of sample information available to ‘inform’ predictions made at target points can vary considerably. Source: Bouchet et al. (2019).
+Fig. 3: Conceptual representation of two key extrapolation metrics. (A) Distance from the sampled environmental space. A target point far from the envelope of the reference data (eg. falling in the yellow area) is arguably ‘more of an extrapolation’ than one close to it (eg. falling in the purple area). (B) Neighbourhood (syn. percentage of data nearby). Due to the shape of the reference data cloud, the amount of sample information available to ‘inform’ predictions made at target points can vary considerably. Source: Bouchet et al. (2019).
 
    
 
    
-
    The notion of neighbourhood (or percentage/proportion of data nearby, hereafter %N) captures this very idea, and provides an additional measure of the reliability of extrapolations in multivariate environmental space (Virgili et al. 2017; Mannocci et al. 2018). In practice, %N for any target point is taken as the proportion of reference data within a radius of one geometric mean Gower’s distance (G2, calculated between all pairs of reference points) of that point (King and Zeng 2007). The Gower’s distance between two points \(i\) and \(j\) defined along the axes of \(K\) covariates is calculated as the average absolute distance between the values of these two points in each dimension, divided by the range of the data, such that:
    
+
    The notion of neighbourhood (or percentage/proportion of data nearby, hereafter %N) captures this very idea, and provides an additional measure of the reliability of extrapolations in multivariate environmental space (Virgili et al. 2017; Mannocci et al. 2018). In practice, %N for any target point is taken as the proportion of reference data within a radius of one geometric mean Gower’s distance (G2, calculated between all pairs of reference points) of that point (King and Zeng 2007). The Gower’s distance between two points \(i\) and \(j\) defined along the axes of \(K\) covariates is calculated as the average absolute distance between the values of these two points in each dimension, divided by the range of the data, such that:
    
 
    \[G_{ij}^2=\frac{1}{K}\sum_{k=1}^{K}\frac{\left|x_{ik}-x_{jk}\right|}{\textrm{max}(X_k)-\textrm{min}(X_k)}\]
    
-
    The compute_nearby function is adapted from the code given in Mannocci et al. (2018), and allows the calculation of Gower’s distances (G2) as a basis for defining the neighbourhood.
    
-
    The nearby argument to this function corresponds to the radius within which neighbouhood calculations will occur. It has a default value of 1, as per Mannocci et al. (2018) and Virgili et al. (2017). Increasing its value (e.g., to say, 2) would mean doubling the size of the green circles in Figure 3B, and would lead to larger percentages of data nearby per target point.
    
+
    The compute_nearby function is adapted from the code given in Mannocci et al. (2018), and allows the calculation of Gower’s distances (G2) as a basis for defining the neighbourhood.
    
+
    The nearby argument to this function corresponds to the radius within which neighbouhood calculations will occur. It has a default value of 1, as per Mannocci et al. (2018) and Virgili et al. (2017). Increasing its value (e.g., to say, 2) would mean doubling the size of the green circles in Figure 3B, and would lead to larger percentages of data nearby per target point.
    
 
    Important: The sperm whale dataset used here is sufficiently small that compute_nearby should run in a matter of seconds on most machines. However, applying the function to larger datasets may burn out memory allocation and lead to an R session failure. Additional arguments can be passed to compute_nearby to accommodate such ‘big’ data. Their use is unnecessary here, but further details can be found in the package documentation.
    
 
    #'---------------------------------------------
 # Calculate Gower's distances and %N
@@ -618,12 +619,12 @@ 
    
 
  • 
 extrapolation.object: output object from compute_extrapolation or compute_nearby.
    • 
 
-
    sightings and tracks are optional arguments, and only plotted if specified. If either is provided as a matrix, then coordinates must be labelled x and y, lest a warning be returned. Tracks can also be given as a SpatialLinesDataFrame object. The resulting maps can be panned, zoomed, and all layers toggled on and off independently. However, note that maps rely on the leaflet package (Cheng et al. 2018), and thus require an internet connection; they will not work offline. Facilities to save map tiles locally for offline viewing are currently not implemented but may be rolled into a future version of the code. Finally, note that prediction.grid is only used here to set the map extent when plotting.
    
+
    sightings and tracks are optional arguments, and only plotted if specified. If either is provided as a matrix, then coordinates must be labelled x and y, lest a warning be returned. Tracks can also be given as a SpatialLinesDataFrame object. The resulting maps can be panned, zoomed, and all layers toggled on and off independently. However, note that maps rely on the leaflet package (Cheng et al. 2018), and thus require an internet connection; they will not work offline. Facilities to save map tiles locally for offline viewing are currently not implemented but may be rolled into a future version of the code. Finally, note that prediction.grid is only used here to set the map extent when plotting.
    
 
    #'---------------------------------------------
 # Rename coordinates and convert to SpatialPointsdf
 #'---------------------------------------------
 obs.sp <- obs %>%
-  dplyr::rename(., x = coords.x1, y = coords.x2) %>%
+  dplyr::rename(., x = coords.x1, y = coords.x2) %>%
   sp::SpatialPointsDataFrame(coords = cbind(.$x, .$y), data = ., proj4string = sp::CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")) %>%
   sp::spTransform(., CRSobj = aftt_crs)
    
 
    Here’s a map of extrapolation (ExDet) values:
    
@@ -632,8 +633,8 @@ 
    
                   sightings = obs.sp,
                   tracks = transects)
    
 
    
-
    
-
    
+
    
+
    
 
    
 
    
 
    Most influential covariates (MIC):
    
@@ -642,8 +643,8 @@ 
    
                   sightings = obs.sp,
                   tracks = transects)
    
 
    
-
    
-
    
+
    
+
    
 
    
 
    
 
    Proportion of data nearby (%N):
    
@@ -652,8 +653,8 @@ 
    
                   sightings = obs.sp,
                   tracks = transects)
    
 
    
-
    
-
    
+
    
+
    
 
    
 
    
 
    
@@ -695,59 +696,59 @@ 
    
 
    
 
    
 Conclusion
    
-
    This vignette has outlined the steps need to quantify extrapolation in environmental space using the dsmextra R package. Note that many possible models (and model types) can be fitted to distance sampling or related data, each potentially behaving differently outside reference conditions for any given level of extrapolation (Yates et al. 2018). Because of this, the tools presented herein are model-agnostic and only designed to provide an a priori assessment. This may, in turn, help inform modelling decisions and model interpretations, but caution should always be exercised when performing model selection, discrimination and criticism.
    
+
    This vignette has outlined the steps need to quantify extrapolation in environmental space using the dsmextra R package. Note that many possible models (and model types) can be fitted to distance sampling or related data, each potentially behaving differently outside reference conditions for any given level of extrapolation (Yates et al. 2018). Because of this, the tools presented herein are model-agnostic and only designed to provide an a priori assessment. This may, in turn, help inform modelling decisions and model interpretations, but caution should always be exercised when performing model selection, discrimination and criticism.
    
 
    
 
    
 
    
 References
    
-
    
-
    
-
    Bouchet PJ, Miller DL, Harris CM, Thomas L (2019). From here and now to there and then: Practical recommendations for the extrapolation of cetacean density surface models to novel conditions. CREEM Technical report 2019-01. University of St Andrews, St Andrews. Available at: https://research-repository.st-andrews.ac.uk/handle/10023/18509
    
+
    
+
    
+Bouchet PJ, Miller DL, Harris CM, Thomas L (2019). From here and now to there and then: Practical recommendations for the extrapolation of cetacean density surface models to novel conditions. CREEM Technical report 2019-01. University of St Andrews, St Andrews. Available at: https://research-repository.st-andrews.ac.uk/handle/10023/18509
 
    
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    Bouchet P, Miller D, Roberts J, Mannocci L, Harris C, Thomas L (2020). dsmextra: Extrapolation assessment tools for density surface models. Methods in Ecology and Evolution. DOI: 10.1111/2041-210X.13469
    
+
    
+Bouchet P, Miller D, Roberts J, Mannocci L, Harris C, Thomas L (2020). dsmextra: Extrapolation assessment tools for density surface models. Methods in Ecology and Evolution. DOI: 10.1111/2041-210X.13469
 
    
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    Broennimann O, Di Cola V, Guisan A (2016). ‘Ecospat: Spatial ecology miscellaneous methods. R package version 2.1.1’. Available at: https://CRAN.R-project.org/package=ecospat
    
+
    
+Broennimann O, Di Cola V, Guisan A (2016). ‘Ecospat: Spatial ecology miscellaneous methods. R package version 2.1.1’. Available at: https://CRAN.R-project.org/package=ecospat
 
    
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    Cheng J, Karambelkar B, Xie Y (2018). ‘Leaflet: Create interactive web maps with the javascript ’leaflet’ library. R package version 2.0.2.’ Available at: https://CRAN.R-project.org/package=leaflet
    
+
    
+Cheng J, Karambelkar B, Xie Y (2018). ‘Leaflet: Create interactive web maps with the JavaScript ’leaflet’ library. R package version 2.0.2.’. Available at: https://CRAN.R-project.org/package=leaflet
 
    
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    Gandrud C, King G, Stoll H, Zeng L (2017). ‘WhatIf: Evaluate counterfactuals. R package version 1.5-9.’ Available at: https://CRAN.R-project.org/package=WhatIf
    
+
    
+Gandrud C, King G, Stoll H, Zeng L (2017). ‘WhatIf: Evaluate counterfactuals. R package version 1.5-9.’. Available at: https://CRAN.R-project.org/package=WhatIf
 
    
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    King G, Zeng L (2007). When can history be our guide? The pitfalls of counterfactual inference. International Studies Quarterly 51, 183–210. DOI: 10.1111/j.1468-2478.2007.00445.x
    
+
    
+King G, Zeng L (2007). When can history be our guide? The pitfalls of counterfactual inference. International Studies Quarterly 51, 183–210. DOI: 10.1111/j.1468-2478.2007.00445.x
 
    
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    Mannocci L, Roberts JJ, Halpin PN, Authier M, Boisseau O, Bradai MN, Canãdas A, Chicote C, David L, Di-Méglio N, Fortuna CM, Frantzis A, Gazo M, Genov T, Hammond PS, Holcer D, Kaschner K, Kerem D, Lauriano G, Lewis T, Notarbartolo Di Sciara G, Panigada S, Raga JA, Scheinin A, Ridoux V, Vella A, Vella J (2018). Assessing cetacean surveys throughout the mediterranean sea: A gap analysis in environmental space. Scientific Reports 8, art3126. DOI: 10.5061/dryad.4pd33
    
+
    
+Mannocci L, Roberts JJ, Halpin PN, Authier M, Boisseau O, Bradai MN, Canãdas A, Chicote C, David L, Di-Méglio N, Fortuna CM, Frantzis A, Gazo M, Genov T, Hammond PS, Holcer D, Kaschner K, Kerem D, Lauriano G, Lewis T, Notarbartolo Di Sciara G, Panigada S, Raga JA, Scheinin A, Ridoux V, Vella A, Vella J (2018). Assessing cetacean surveys throughout the mediterranean sea: A gap analysis in environmental space. Scientific Reports 8, art3126. DOI: 10.5061/dryad.4pd33
 
    
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    Mannocci L, Roberts JJ, Miller DL, Halpin PN (2017). Extrapolating cetacean densities to quantitatively assess human impacts on populations in the high seas. Conservation Biology 31, 601–614. DOI: 10.1111/cobi.12856
    
+
    
+Mannocci L, Roberts JJ, Miller DL, Halpin PN (2017). Extrapolating cetacean densities to quantitatively assess human impacts on populations in the high seas. Conservation Biology 31, 601–614. DOI: 10.1111/cobi.12856
 
    
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    Mesgaran MB, Cousens RD, Webber BL (2014). Here be dragons: A tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models. Diversity and Distributions 20, 1147–1159. DOI: 10.1111/ddi.12209
    
+
    
+Mesgaran MB, Cousens RD, Webber BL (2014). Here be dragons: A tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models. Diversity and Distributions 20, 1147–1159. DOI: 10.1111/ddi.12209
 
    
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    Miller DL, Burt ML, Rexstad EA, Thomas L (2013). Spatial models for distance sampling data: Recent developments and future directions. Methods in Ecology and Evolution 4, 1001–1010. DOI: 10.1111/2041-210X.12105
    
+
    
+Miller DL, Burt ML, Rexstad EA, Thomas L (2013). Spatial models for distance sampling data: Recent developments and future directions. Methods in Ecology and Evolution 4, 1001–1010. DOI: 10.1111/2041-210X.12105
 
    
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    Miller DL, Rexstad E, Burt L, Bravington MV, Hedley. S (2015). ‘Dsm: Density surface modelling of distance sampling data. R package version 2.2.9.’ Available at: https://CRAN.R-project.org/package=dsm
    
+
    
+Miller DL, Rexstad E, Burt L, Bravington MV, Hedley. S (2015). ‘Dsm: Density surface modelling of distance sampling data. R package version 2.2.9.’. Available at: https://CRAN.R-project.org/package=dsm
 
    
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    Roberts JJ, Best BD, Mannocci L, Fujioka E, Halpin PN, Palka DL, Garrison LP, Mullin KD, Cole TVN, Khan CB, McLellan WA, Pabst DA, Lockhart GG (2016). Habitat-based cetacean density models for the u.s. Atlantic and gulf of mexico. Scientific Reports 6, art22615. DOI: 10.1038/srep22615
    
+
    
+Roberts JJ, Best BD, Mannocci L, Fujioka E, Halpin PN, Palka DL, Garrison LP, Mullin KD, Cole TVN, Khan CB, McLellan WA, Pabst DA, Lockhart GG (2016). Habitat-based cetacean density models for the u.s. Atlantic and gulf of mexico. Scientific Reports 6, art22615. DOI: 10.1038/srep22615
 
    
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    Rousseeuw PJ, Zomeren BC van (1990). Unmasking multivariate outliers and leverage points. Journal of the American Statistical Association 85, 633–639. DOI: 10.1080/01621459.1990.10474920
    
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+Rousseeuw PJ, Zomeren BC van (1990). Unmasking multivariate outliers and leverage points. Journal of the American Statistical Association 85, 633–639. DOI: 10.1080/01621459.1990.10474920
 
    
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    Sequeira A, Bouchet PJ, Yates K, Mengersen K, Caley J (2018). Transferring biodiversity models for conservation: Opportunities and challenges. Methods in Ecology and Evolution 9, 1250–1264. DOI: 10.1111/2041-210X.12998
    
+
    
+Sequeira A, Bouchet PJ, Yates K, Mengersen K, Caley J (2018). Transferring biodiversity models for conservation: Opportunities and challenges. Methods in Ecology and Evolution 9, 1250–1264. DOI: 10.1111/2041-210X.12998
 
    
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    Virgili A, Racine M, Authier M, Monestiez P, Ridoux V (2017). Comparison of habitat models for scarcely detected species. Ecological Modelling 346, 88–98. DOI: 10.1016/j.ecolmodel.2016.12.013
    
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+Virgili A, Racine M, Authier M, Monestiez P, Ridoux V (2017). Comparison of habitat models for scarcely detected species. Ecological Modelling 346, 88–98. DOI: 10.1016/j.ecolmodel.2016.12.013
 
    
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    Yates KL, Bouchet PJ, Caley MJ, Mengersen K, Randin CF, Parnell S, Fielding AH, Bamford AJ, Ban S, Barbosa AM, Dormann CF, Elith J, Embling CB, Ervin GN, Fisher R, Gould S, Graf RF, Gregr EJ, Halpin PN, Heikkinen RK, Heinänen S, Jones AR, Krishnakumar PK, Lauria V, Lozano-Montes H, Mannocci L, Mellin C, Mesgaran MB, Moreno-Amat E, Mormede S, Novaczek E, Oppel S, Ortuño Crespo G, Peterson AT, Rapacciuolo G, Roberts JJ, Ross RE, Scales KL, Schoeman D, Snelgrove P, Sundblad G, Thuiller W, Torres LG, Verbruggen H, Wang L, Wenger S, Whittingham MJ, Zharikov Y, Zurell D, Sequeira AMM (2018). Outstanding challenges in the transferability of ecological models. Trends in Ecology and Evolution 33, 790–802. DOI: 10.1016/j.tree.2018.08.001
    
+
    
+Yates KL, Bouchet PJ, Caley MJ, Mengersen K, Randin CF, Parnell S, Fielding AH, Bamford AJ, Ban S, Barbosa AM, Dormann CF, Elith J, Embling CB, Ervin GN, Fisher R, Gould S, Graf RF, Gregr EJ, Halpin PN, Heikkinen RK, Heinänen S, Jones AR, Krishnakumar PK, Lauria V, Lozano-Montes H, Mannocci L, Mellin C, Mesgaran MB, Moreno-Amat E, Mormede S, Novaczek E, Oppel S, Ortuño Crespo G, Peterson AT, Rapacciuolo G, Roberts JJ, Ross RE, Scales KL, Schoeman D, Snelgrove P, Sundblad G, Thuiller W, Torres LG, Verbruggen H, Wang L, Wenger S, Whittingham MJ, Zharikov Y, Zurell D, Sequeira AMM (2018). Outstanding challenges in the transferability of ecological models. Trends in Ecology and Evolution 33, 790–802. DOI: 10.1016/j.tree.2018.08.001
 
    
 
    
 
    
diff --git a/docs/articles/dsmextra_files/anchor-sections-1.0/anchor-sections.css b/docs/articles/dsmextra_files/anchor-sections-1.0/anchor-sections.css
new file mode 100644
index 0000000..07aee5f
--- /dev/null
+++ b/docs/articles/dsmextra_files/anchor-sections-1.0/anchor-sections.css
@@ -0,0 +1,4 @@
+/* Styles for section anchors */
+a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
+a.anchor-section::before {content: '#';}
+.hasAnchor:hover a.anchor-section {visibility: visible;}
diff --git a/docs/articles/dsmextra_files/anchor-sections-1.0/anchor-sections.js b/docs/articles/dsmextra_files/anchor-sections-1.0/anchor-sections.js
new file mode 100644
index 0000000..570f99a
--- /dev/null
+++ b/docs/articles/dsmextra_files/anchor-sections-1.0/anchor-sections.js
@@ -0,0 +1,33 @@
+// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
+document.addEventListener('DOMContentLoaded', function() {
+  // Do nothing if AnchorJS is used
+  if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
+    return;
+  }
+
+  const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
+
+  // Do nothing if sections are already anchored
+  if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
+    return null;
+  }
+
+  // Use section id when pandoc runs with --section-divs
+  const section_id = function(x) {
+    return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
+            ? x.id : '');
+  };
+
+  // Add anchors
+  h.forEach(function(x) {
+    const id = x.id || section_id(x.parentElement);
+    if (id === '') {
+      return null;
+    }
+    let anchor = document.createElement('a');
+    anchor.href = '#' + id;
+    anchor.classList = ['anchor-section'];
+    x.classList.add('hasAnchor');
+    x.appendChild(anchor);
+  });
+});
diff --git a/docs/articles/dsmextra_files/header-attrs-2.5/header-attrs.js b/docs/articles/dsmextra_files/header-attrs-2.5/header-attrs.js
new file mode 100644
index 0000000..dd57d92
--- /dev/null
+++ b/docs/articles/dsmextra_files/header-attrs-2.5/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+  var i, h, a;
+  for (i = 0; i < hs.length; i++) {
+    h = hs[i];
+    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
+    a = h.attributes;
+    while (a.length > 0) h.removeAttribute(a[0].name);
+  }
+});
diff --git a/docs/articles/index.html b/docs/articles/index.html
index 4cfb1c7..0d24b51 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -71,7 +71,7 @@
       
       
         dsmextra
-        1.1.2
+        1.1.4
       
     
    
 
diff --git a/docs/authors.html b/docs/authors.html
index f574360..784a0a8 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -71,7 +71,7 @@
       
       
         dsmextra
-        1.1.2
+        1.1.4
       
     
    
 
diff --git a/docs/index.html b/docs/index.html
index 08e0211..7b00490 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -31,7 +31,7 @@
       
       
         dsmextra
-        1.1.2
+        1.1.4
       
     
    
 
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index a80cea8..a551e41 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -1,9 +1,9 @@
-pandoc: 2.7.3
+pandoc: 2.11.0.4
 pkgdown: 1.5.1
 pkgdown_sha: ~
 articles:
   dsmextra-bigdata: dsmextra-bigdata.html
   dsmextra-covariateselection: dsmextra-covariateselection.html
   dsmextra: dsmextra.html
-last_built: 2020-10-14T10:56Z
+last_built: 2021-02-11T10:42Z
 
diff --git a/docs/reference/ExDet.html b/docs/reference/ExDet.html
index b1181a8..310e341 100644
--- a/docs/reference/ExDet.html
+++ b/docs/reference/ExDet.html
@@ -72,7 +72,7 @@
       
       
         dsmextra
-        1.1.2
+        1.1.4
       
     
    
 
diff --git a/docs/reference/acacia.html b/docs/reference/acacia.html
index 36e273e..cc3be2d 100644
--- a/docs/reference/acacia.html
+++ b/docs/reference/acacia.html
@@ -72,7 +72,7 @@
       
       
         dsmextra
-        1.1.2
+        1.1.4
       
     
    
 
diff --git a/docs/reference/compare_covariates.html b/docs/reference/compare_covariates.html
index b538f84..bfc0e95 100644
--- a/docs/reference/compare_covariates.html
+++ b/docs/reference/compare_covariates.html
@@ -72,7 +72,7 @@
       
       
         dsmextra
-        1.1.2
+        1.1.4
       
     
    
 
diff --git a/docs/reference/compute_extrapolation.html b/docs/reference/compute_extrapolation.html
index 57e850f..9eb9b76 100644
--- a/docs/reference/compute_extrapolation.html
+++ b/docs/reference/compute_extrapolation.html
@@ -72,7 +72,7 @@
       
       
         dsmextra
-        1.1.2
+        1.1.4
       
     
    
 
@@ -260,7 +260,7 @@ 
    Examp
                                     prediction.grid = target,
                                     coordinate.system = sp::proj4string(r))
    #> Computing ...
    #> Done!
    
 # Make a map
-map_extrapolation(map.type = "extrapolation", extrapolation.object = bioclim.ex)
    #> Warning: map_extrapolation relies on the leaflet package, which is built around a Web Mercator projection (EPSG:3857), and therefore requires rasters to be reprojected for plotting. As a result, minor discrepancies may  occur between the interactive maps shown in the viewer, and the underlying raw data. The latter can be accessed directly from the extrapolation.values or gower.values objects and visualised using alternative packages such as ggplot2.
    
+map_extrapolation(map.type = "extrapolation", extrapolation.object = bioclim.ex)
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: input and ouput crs are the same
    #> Warning: input and ouput crs are the same
    #> Warning: input and ouput crs are the same
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: map_extrapolation relies on the leaflet package, which is built around a Web Mercator projection (EPSG:3857), and therefore requires rasters to be reprojected for plotting. As a result, minor discrepancies may  occur between the interactive maps shown in the viewer, and the underlying raw data. The latter can be accessed directly from the extrapolation.values or gower.values objects and visualised using alternative packages such as ggplot2.
    
   
    
   
 
diff --git a/docs/reference/dsmextra.html b/docs/reference/dsmextra.html
index fc5a780..b1e69a6 100644
--- a/docs/reference/dsmextra.html
+++ b/docs/reference/dsmextra.html
@@ -72,7 +72,7 @@
       
       
         dsmextra
-        1.1.2
+        1.1.4
       
     
    
 
diff --git a/docs/reference/extrapolation_analysis.html b/docs/reference/extrapolation_analysis.html
index 1317f08..4640106 100644
--- a/docs/reference/extrapolation_analysis.html
+++ b/docs/reference/extrapolation_analysis.html
@@ -78,7 +78,7 @@
       
       
         dsmextra
-        1.1.2
+        1.1.4
       
     
    
 
@@ -290,7 +290,7 @@ 
    Examp
 #>                 Depth, EKE, NPP         0  -                                 -     
 #> Both            DistToCAS               2  Depth, SST, NPP                   252   
    #> 
 #> === Calculating neighbourhood metrics ===
    #> Preprocessing data ...
    #> Calculating distances ....
    #> Calculating the geometric variance...
    #> Calculating cumulative frequencies ...
    #> Finishing up ...
    #> Done!
    #> 
-#> === Generating maps ===
    #> Warning: map_extrapolation relies on the leaflet package, which is built around a Web Mercator projection (EPSG:3857), and therefore requires rasters to be reprojected for plotting. As a result, minor discrepancies may  occur between the interactive maps shown in the viewer, and the underlying raw data. The latter can be accessed directly from the extrapolation.values or gower.values objects and visualised using alternative packages such as ggplot2.
    #> Warning: map_extrapolation relies on the leaflet package, which is built around a Web Mercator projection (EPSG:3857), and therefore requires rasters to be reprojected for plotting. As a result, minor discrepancies may  occur between the interactive maps shown in the viewer, and the underlying raw data. The latter can be accessed directly from the extrapolation.values or gower.values objects and visualised using alternative packages such as ggplot2.
    #> Warning: map_extrapolation relies on the leaflet package, which is built around a Web Mercator projection (EPSG:3857), and therefore requires rasters to be reprojected for plotting. As a result, minor discrepancies may  occur between the interactive maps shown in the viewer, and the underlying raw data. The latter can be accessed directly from the extrapolation.values or gower.values objects and visualised using alternative packages such as ggplot2.
    #> 
+#> === Generating maps ===
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: input and ouput crs are the same
    #> Warning: input and ouput crs are the same
    #> Warning: input and ouput crs are the same
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: map_extrapolation relies on the leaflet package, which is built around a Web Mercator projection (EPSG:3857), and therefore requires rasters to be reprojected for plotting. As a result, minor discrepancies may  occur between the interactive maps shown in the viewer, and the underlying raw data. The latter can be accessed directly from the extrapolation.values or gower.values objects and visualised using alternative packages such as ggplot2.
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: map_extrapolation relies on the leaflet package, which is built around a Web Mercator projection (EPSG:3857), and therefore requires rasters to be reprojected for plotting. As a result, minor discrepancies may  occur between the interactive maps shown in the viewer, and the underlying raw data. The latter can be accessed directly from the extrapolation.values or gower.values objects and visualised using alternative packages such as ggplot2.
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: map_extrapolation relies on the leaflet package, which is built around a Web Mercator projection (EPSG:3857), and therefore requires rasters to be reprojected for plotting. As a result, minor discrepancies may  occur between the interactive maps shown in the viewer, and the underlying raw data. The latter can be accessed directly from the extrapolation.values or gower.values objects and visualised using alternative packages such as ggplot2.
    #> 
 #> === Extrapolation assessment complete! ===
    
   
    
   
 
diff --git a/docs/reference/map_extrapolation.html b/docs/reference/map_extrapolation.html
index 8e097f4..04d9d59 100644
--- a/docs/reference/map_extrapolation.html
+++ b/docs/reference/map_extrapolation.html
@@ -72,7 +72,7 @@
       
       
         dsmextra
-        1.1.2
+        1.1.4
       
     
    
 
@@ -205,7 +205,7 @@ 
    Examp
                                covariate.names = my_cov,
                                nearby = 1)
    #> Preprocessing data ...
    #> Calculating distances ....
    #> Calculating the geometric variance...
    #> Calculating cumulative frequencies ...
    #> Finishing up ...
    #> Done!
    
 # Generate maps
-map_extrapolation(map.type = "extrapolation", extrapolation.object = spermw.extrapolation)
    #> Warning: map_extrapolation relies on the leaflet package, which is built around a Web Mercator projection (EPSG:3857), and therefore requires rasters to be reprojected for plotting. As a result, minor discrepancies may  occur between the interactive maps shown in the viewer, and the underlying raw data. The latter can be accessed directly from the extrapolation.values or gower.values objects and visualised using alternative packages such as ggplot2.
    map_extrapolation(map.type = "mic", extrapolation.object = spermw.extrapolation)
    #> Warning: map_extrapolation relies on the leaflet package, which is built around a Web Mercator projection (EPSG:3857), and therefore requires rasters to be reprojected for plotting. As a result, minor discrepancies may  occur between the interactive maps shown in the viewer, and the underlying raw data. The latter can be accessed directly from the extrapolation.values or gower.values objects and visualised using alternative packages such as ggplot2.
    map_extrapolation(map.type = "nearby", extrapolation.object = spermw.nearby)
    #> Warning: map_extrapolation relies on the leaflet package, which is built around a Web Mercator projection (EPSG:3857), and therefore requires rasters to be reprojected for plotting. As a result, minor discrepancies may  occur between the interactive maps shown in the viewer, and the underlying raw data. The latter can be accessed directly from the extrapolation.values or gower.values objects and visualised using alternative packages such as ggplot2.
    
+map_extrapolation(map.type = "extrapolation", extrapolation.object = spermw.extrapolation)
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: input and ouput crs are the same
    #> Warning: input and ouput crs are the same
    #> Warning: input and ouput crs are the same
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: map_extrapolation relies on the leaflet package, which is built around a Web Mercator projection (EPSG:3857), and therefore requires rasters to be reprojected for plotting. As a result, minor discrepancies may  occur between the interactive maps shown in the viewer, and the underlying raw data. The latter can be accessed directly from the extrapolation.values or gower.values objects and visualised using alternative packages such as ggplot2.
    map_extrapolation(map.type = "mic", extrapolation.object = spermw.extrapolation)
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: map_extrapolation relies on the leaflet package, which is built around a Web Mercator projection (EPSG:3857), and therefore requires rasters to be reprojected for plotting. As a result, minor discrepancies may  occur between the interactive maps shown in the viewer, and the underlying raw data. The latter can be accessed directly from the extrapolation.values or gower.values objects and visualised using alternative packages such as ggplot2.
    map_extrapolation(map.type = "nearby", extrapolation.object = spermw.nearby)
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: Discarded ellps WGS 84 in CRS definition: +proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null +wktext +no_defs +type=crs
    #> Warning: Discarded datum World Geodetic System 1984 in CRS definition
    #> Warning: map_extrapolation relies on the leaflet package, which is built around a Web Mercator projection (EPSG:3857), and therefore requires rasters to be reprojected for plotting. As a result, minor discrepancies may  occur between the interactive maps shown in the viewer, and the underlying raw data. The latter can be accessed directly from the extrapolation.values or gower.values objects and visualised using alternative packages such as ggplot2.
    diff --git a/docs/reference/spermwhales.html b/docs/reference/spermwhales.html index c842240..153ad84 100644 --- a/docs/reference/spermwhales.html +++ b/docs/reference/spermwhales.html @@ -72,7 +72,7 @@ dsmextra - 1.1.2 + 1.1.4

    diff --git a/docs/reference/summarise_extrapolation.html b/docs/reference/summarise_extrapolation.html index 2f4080f..6cb5675 100644 --- a/docs/reference/summarise_extrapolation.html +++ b/docs/reference/summarise_extrapolation.html @@ -72,7 +72,7 @@ dsmextra - 1.1.2 + 1.1.4 diff --git a/docs/reference/summary.extrapolation_results.html b/docs/reference/summary.extrapolation_results.html index 0d79397..7d67c07 100644 --- a/docs/reference/summary.extrapolation_results.html +++ b/docs/reference/summary.extrapolation_results.html @@ -72,7 +72,7 @@ dsmextra - 1.1.2 + 1.1.4 diff --git a/docs/reference/whatif.html b/docs/reference/whatif.html index 520d591..c235ecb 100644 --- a/docs/reference/whatif.html +++ b/docs/reference/whatif.html @@ -72,7 +72,7 @@ dsmextra - 1.1.2 + 1.1.4 diff --git a/docs/reference/whatif.opt.html b/docs/reference/whatif.opt.html index 467ee69..026e8c8 100644 --- a/docs/reference/whatif.opt.html +++ b/docs/reference/whatif.opt.html @@ -72,7 +72,7 @@ dsmextra - 1.1.2 + 1.1.4 diff --git a/vignettes/dsmextra.css b/vignettes/dsmextra.css index b4f85dd..6e326a3 100644 --- a/vignettes/dsmextra.css +++ b/vignettes/dsmextra.css @@ -96,3 +96,6 @@ ul{padding-left: 1.2em;} .figure{ display: block; margin: 0 auto;} + +.csl-entry{ + margin-bottom: 10px !important;}