1.0.1

This new development version includes new functions to conduct inferences on the specification curve analysis (the third step outlined Simohnsohn et al., 2020). It thereby facilitates resampling under-the-null to obtain test statistics to evaluate the extremity of the specification curve analysis results. See this vignette for a comprehensive tutorial.

1.0.0

This is version currently published on CRAN: https://​cloud.r-project.org/​package=specr.
With this version, we introduce a completely new analytic framework which breaks with earlier versions of specr. See this vignette for a comprehensive tutorial on how to use this new framework.

• A new function called setup() is introduced and replaces the original setup_specs() to make the specification of analytical choices more intuitive and comprehensive. Most importantly, it allows to set up all specifications (now also including subset analyses, additions to the formula, etc.) a priori, i.e., before the estimation of all models.

  • Data, analytic choices, and even parameter extraction functions can be specified before the fitting process (solving github issue #23 and #26).
  • Excludes some non-meaningful specifications automatically (e.g., when control and independent or dependent variable are the same).
  • Allows to specify all combinations of control variables or to "simplify" and only include none, each individually and all together (github issue #11).
  • Resulting specification can be filtered using standard tidyverse functions such as e.g., filter(). This way, one can make sure a priori that only reasonable specifications are actually included.
  • Produces an object of class specr.setup, which can be investigated using e.g., summary() or plot().
  • The function run_specs() is replaced by specr(), which now only wraps around setup() to estimate all models. Most changes are related to increasing speed of the computations.

• Most importantly, the estimation process can now be parallelized (based on furrr) to reduce fitting time (finally solving github issue #1).

  • Deals better and faster with subset analyses.
  • Produces an object of class specr.object, which can be investigated using generic function such as summary() and plot().
  • Plotting function have been updated and e.g. also allow to group specification according to specific choices (github issue #19)
  • For more information about these major changes and how to use the new version of specr, see this vignette.

Please note that the functions from earlier versions are still available, but deprecated. It is hence still possible to use the older framework as implemented in version 0.2.1, but we suggest to move to the new framework of version 0.3.0 due to its increased speed and flexibility.

0.2.2

Some minor updates and bug fixes:

• all plotting functions allow to choose which parameter to plot
• more complete results based on broom::tidy() and broom::glance()

The package now allows to integrate:

• random effects (see this vignette: https://masurp.github.io/specr/articles/random_effects.html)
• latent measurement models (see this vignette: https://masurp.github.io/specr/articles/measurement_models.html

0.2.1

Fixed typos. This is version currently published on CRAN: https://​cloud.r-project.org/​package=specr

0.2.0

Stable version. Tested in several environments.

Pre-release version

This version is still in development but main functions and features are established. Some further performance enhancements, customizations, debugging, and cosmetic changes will take place before any official release.

First release

First working version. Will get several updates in the near future.