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Changelog

supernova 3.0.0

Breaking changes

• Remove deprecated superanova()
• Remove magrittr, ggplot2, and backports dependencies

supernova 2.5.8

• Don’t trim the leading digit from p-value or PRE when they are exactly 1

supernova 2.5.7

• Check package version using string comparison to pass R_CHECK_STOP_ON_INVALID_NUMERIC_VERSION_INPUTS check on CRAN
• Match parameter names to docs for two internal functions

supernova 2.5.6

• Move from deprecated rlang::with_handlers + rlang::calling to the simpler rlang::try_fetch
• Fix generic function consistency errors (ggplot2::autoplot, pillar::tbl_sum) with current version (on devel build)

supernova 2.5.5

• Remove data (all moved to the coursekata package). This removes some of the messages that spout when the coursekata package loads.

supernova 2.5.4

• Fix print error where generate_models() would only print the comparison models for type III SS.

supernova 2.5.3

• Remove references to Fingers data in examples and tests as it may be removed in the near future
• Fix issue where using a model fit with an interactive term that has factor() in it (e.g. lm(mpg ~ factor(cyl) * hp, data = mtcars)) would result in an incorrect df (and related values) in the ANOVA table.

supernova 2.5.2

Breaking (kind of)

• Deprecate superanova() as it was just an alias to supernova() (which was confusing for some students)

supernova 2.5.1

• Fix issue where models with long calls (i.e. deparse(model$call) results in a vector of length greater than 1) would break the functionality of listwise_delete()
• Change print method for generate_models() to look clean and comprehensible in Jupyter Notebooks.

supernova 2.5.0

• Change the order of the pairs when plotting pairwise() so that the plot matches the table.
• Fix bug where pairwise() would not recognize categorical variables if they were created by using factor() in the formula, e.g. pairwise(lm(mpg ~ factor(cyl), data = mtcars)).
• Fix printing in RMarkdown documents where supernova() output was interpreted as a table.
• Move estimate-extraction functions to coursekata.
• Breaking: drop support for R 3.4

supernova 2.4.4

• Remove supernova-vctrs from exports

supernova 2.4.3

• Fix issues with lintr causing R CMD CHECK to fail
• Change maintainer to [@adamblake](https://github.com/adamblake)
• Change mislabeled factor level in Fingers$Interest to “Very Interested”

supernova 2.4.2

• Ensure appropriate version of pillar is available (thanks @cedricbatailler)

supernova 2.4.1

• Small tweaks to make the package work on R 3.4.0

supernova 2.4.0

New features

There are four new pairwise comparisons functions:

• pairwise()
• pairwise_t()
• pairwise_bonferroni()
• pairwise_tukey()

Each of these determines all the pairwise comparisons that can be made for a model (fit by lm()) and then computes the comparisons. For pairwise_t() no correction is made for multiple comparisons, but for the others, the named correction is made. These corrections can also be specified as arguments to the pairwise() wrapper function. Each function produces output that has customized printing, supports most (if not all) normal data frame actions, and a plotting function that graphs the mean differences and their confidence intervals.

supernova 2.3.0

• Dependency on lme4 is moved to Suggests. Models implementing lmerMod are handled via supernova.lmerMod and variables.lmerMod but use of the lme4 package is limited to tests.
• More robust and readable implementation of variables() using the new formula utility functions added. See ?formula_building, ?formula_expansion, and ?formula_extraction.
• Add a new function equation() to extract the fitted equation from a linear model (lm()) (thanks for the suggestion from [@ave-63](https://github.com/ave-63)!)

supernova 2.2.3

• Remove dependency on dplyr because it changes too quickly and has too many other dependencies
• Mild refactoring to improve code readability

supernova 2.2.2

• Patch to keep up with changes to lme4

supernova 2.2.1

• Add support for mixed models (as in nested and crossed). See the README for more information.

supernova 2.2.0

Extend supernova to handle within (crossed) designs

• Add lme4 and dplyr to Imports
• Update R dependency to 3.5.0 (for serialized data; Rds)
• Convert supernova to S3 class with methods for lm and lmerMod
• Add tests for supernova() for crossed (but not nested) lmer() fits
• Extend print.supernova to handle new models

Minor changes:

• Refactor utility functions into utils.R
• Add internal documentation for utility functions

supernova 2.1.1

• Added a NEWS.md file to track changes to the package.

• Created and added a logo to the package. (#21, @adamblake)

• Added the ability to change the type of sums of squares to calculate when computing the ANOVA tables. Users can choose from 1/I/sequential, 2/II/hierarchical, 3/III/orthogonal. (#22, @adamblake)

• Added pedagogical function generate_models() for showing which models are being compared when evaluating terms in a model. This function also supports specification of the type of sums of squares to use. (#22, @adamblake)

• Updated the README to be generated from an Rmd file and to include information and examples regarding how to calculate different SS types and how to use generate_models()

• Added a data frame identical to Servers named Tables. This is a more appropriate name for the dataset because each row describes what happened at a table in the restaurant.
  • Updated variable names and documentation to “table” as well.
  • Added deprecation notice to Servers documentation as the table will be removed in the future.

supernova 2.0.0

• Added support for multiple regression using Type III sums of squares

• Updated README for more information, examples, and a description of how the calculation of the ANOVA tables follows the model comparison approach used in Judd, McClelland, & Ryan (2017).

supernova 1.1.0

This version of supernova is the original distributed on CRAN. Calculation of supernova() tables with multiple predictor variables in this version will not produce output similar to the reference text, Judd, McClelland, and Ryan. However, the values for single predictor models are correct.

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.
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