New capabilities

  • Added run_mediation() to estimate and plot_mediation() to visualize parallel mediation models.
  • Added na_when() to set values to NA based on logical conditions, and na_ifs() to replace multiple values with NA (naming choice guided by existing dplyr::na_if function)
  • Added make_scale_mi() to estimate scale scores and Cronbach’s alpha after multiple-imputation at the item level. According to Gottschall, West & Enders (2012), this is one of the best ways to deal with item-level missing data. If requested, the function can bootstrap confidence intervals, using the future-package for parallel computing.
  • Added pcor_matrix() to calculate partial correlation matrices after parceling out one or several variable.
  • Added run_and_format() to run any code and return formatted code and output for sharing - in the style of reprex::reprex() but without creating a new session (intended for teaching or sharing code examples, rather than bug reporting).
  • Added paste_() that mimics paste() but removes NA-values
  • Added dupl_items() that returns (unique) duplicated items from vector, removing need for subsetting with duplicated()

Enhancements

  • make_scale() now has a proration_cutoff argument to specify the maximum share of missing data ignored in each case. This offers a simple way to improve on casewise deletion for handling missing data, without getting scale scores based on an insufficient subset of items. [NB: This is a breaking change, earlier versions implicitly had a proration_cutoff of 1, returning scale scores if at least one item was present - which is indefensible when there is a lot of missing data.]
  • Added summary text to descriptives returned by make_scale()
  • Enabled report_lm_with_std() to show R2 change for more than one pair of models
  • Added option to use t.test()-style formula notion in pairwise_t_tests()
  • Allow automatic reverse-coding in make_scales()
  • report_cor_table() now ensures correct ordering of extra columns if row_names column is included
  • Added tidy.svy_cor_matrix() to tidy survey-weighted correlation matrices
  • Renamed wtd_cor_matrix_mi() to cor_matrix_mi() to reflect that weights are optional
  • Added output to README (by using README.Rmd) to make it more informative
  • fmt_p() gained a equal_sign argument that determines whether p-values that are reported precisely are prefixed with “=”
  • dump_to_clip() now accepts objects passed directly, or through the pipe (#5)
  • line_to_vector() now automatically returns numeric vectors when only numbers are passed, and has gained an option to retain NA values for blank entries. It also allows users to pass one or multiple separators to split at explicitly. l2v() is now an alias for line_to_vector().

Bug fixes

New capabilities

  • Added svy_miss_var_summary() to quickly check for missing data in survey objects
  • Added polr_std() function to run proportional-odds model with continuous predictors standardised (analogous to lm_std())
  • Added dummy_code() for creating k-1 dummies with tidy names
  • Added report_anova() to report F-tests for model comparisons (limited functionalities at present)

Enhancements

  • Added a broom-like tidier for correlation matrices returned by cor_matrix()
  • Added time to temporary filenames used by ggplot_save()

Bug fixes