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galamm 0.4.1

galamm has been peer reviewed and accepted by rOpenSci, and the repository has moved to https://github.com/ropensci/galamm. This release contains the changes made during and after that review. There are no changes to the user-facing model fitting functions.

• All links to the source repository, including the continuous integration and test coverage badges, now point to the repository under the rOpenSci organization.
• The package website has moved to https://docs.ropensci.org/galamm/, and is now built centrally by rOpenSci. All documentation links have been updated accordingly, and the package no longer deploys a website of its own.
• The pkgdown configuration now requests MathJax explicitly, so that the mathematical notation in the vignettes renders reliably on the new website.
• The bundled code of conduct has been removed, since rOpenSci’s code of conduct at https://ropensci.org/code-of-conduct/ now applies. The contributing guidelines link to it instead.
• The README describes how to install development versions from the rOpenSci R-universe.

galamm 0.4.0

• Vignettes are updated to better demonstrate model checking possibilities.
• A qqmath() function has now been added, which makes quantile-quantile plots.
• residuals.galamm() now has a “scaled = TRUE/FALSE” argument.
• plot.galamm() has been considerably extended.

galamm 0.3.0

• The expected output of a unit test has been changed.
• A bug in the computation of deviance residuals has been corrected.
• A bug in the computation of Pearson residuals for non-Gaussian models has been corrected.
• The plot() function now takes an optional argument for the type of residuals to plot.
• The family_mapping argument for mixed response types has been deprecated. Use the gfam() function instead.
• Argument weights to galamm has been renamed to dispformula. The old name weights will work for a while, but a deprecation warning will be issued.
• Improved tests.
• Improved error messages.
• Argument load.var to galamm has been renamed to load_var. The old name load_var will work for a while, but a deprecation warning will be issued.

galamm 0.2.3

• A bug causing heteroscedastic models with more than two groups to fail has been solved. Thanks to Espen Moen Eilertsen for reporting the issue.
• Have rerun all vignettes. There are some minor changes in the formatting of summary(model), due to changes in lme4.

galamm 0.2.2

• Internal fix to avoid warning about uninitialized variables.
• New function response() returns the response used for fitting the model.
• New print.galamm() function which is an alias for print(summary(galamm_obj)).
• Rank deficient fixed effect matrices now leads to an error. Previously only a message was written that the redundant columns were dropped.

galamm version 0.2.1

• Software paper in Multivariate Behavioral Research has been added to the recommended citation.

galamm version 0.2.0

• The print.summary.galamm() method does no longer print residual percentiles with mixed response models. The version implemented until now had a bug, and it is not clear how to present this information in a useful way.
• Fixed bug causing galamm() to fail with mixed response types, when the first argument to “family” was not “gaussian”.
• BREAKING CHANGE: argument “factor”, “factor_interaction” and “lambda” to galamm should no longer be enclosed in a list.
• A vignette investigating computational scalability has been added.
• formula.galamm() method has been added, inheriting from stats::formula().
• nobs.galamm() function is now exported.
• na.action argument has been added to galamm().
• input validation has been extended.
• all internal functions have been documented, using the @noRd tag to suppress generation of markdown.

galamm version 0.1.1

• Fixed bug causing galamm to fail on R4.2.3.
• Fixed memory issues in C++ code.
• In the smooth terms sl() and t2(), the argument ‘load_var’ has been renamed to ‘factor’, to correspond with the remaining factor arguments.

galamm version 0.1.0

Initial version.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.