Calculates Critical Test Statistics to Control False Discovery
and Familywise Error Rates in Marginal Effects Plots
Implements the procedures suggested in Esarey and Sumner (2017) <URL:https://goo.gl/riqX7Q> for controlling the false discovery rate or familywise error rate when constructing marginal effects plots for models with interaction terms.

Implements the procedures suggested in Esarey and Sumner (2015) for controlling the false discovery rate or familywise error rate when constructing marginal effects plots for models with interaction terms.

Calculates Critical Test Statistics to Control False Discovery
Rates in Marginal Effects Plots
Implements the procedures suggested in Esarey and Sumner (2017) <http://justinesarey.com/interaction-overconfidence.pdf> for controlling the false discovery rate when constructing marginal effects plots for models with interaction terms.

Comprehensive, User-Friendly Toolkit for Probing Interactions
A suite of functions for conducting and interpreting analysis
of statistical interaction in regression models that was formerly part of the
'jtools' package. Functionality includes visualization of two- and three-way
interactions among continuous and/or categorical variables as well as
calculation of "simple slopes" and Johnson-Neyman intervals. These
capabilities are implemented for generalized linear models in addition to the
standard linear regression context.
ggplot2, crayon, cli, jtools (>= 2.0.0), rlang
brms, broom, cowplot, ggstance, glue, huxtable (>= 3.0.0),
lme4, rstanarm, sandwich, survey, knitr, rmarkdown, rmdformats,
testthat

ggplot2, crayon, cli, jtools (>= 2.0.0), rlang (>= 0.3.0),
tibble
broom, cowplot, ggstance, glue, huxtable (>= 3.0.0), lme4,
margins, sandwich, survey, knitr, rmarkdown, testthat

A suite of functions for conducting and interpreting analysis
of statistical interaction in regression models that was formerly part of the
'jtools' package. Functionality includes visualization of two- and three-way
interactions among continuous and/or categorical variables as well as
calculation of "simple slopes" and Johnson-Neyman intervals (see e.g.,
Bauer & Curran, 2005 <doi:10.1207/s15327906mbr4003_5>). These
capabilities are implemented for generalized linear models in addition to the
standard linear regression context.
ggplot2, crayon, cli, generics, jtools (>= 2.0.0), rlang (>=
0.3.0), tibble