rcompanion v 2.4.34 (2023-09-15)

rcompanion v 2.4.30 (2023-05-03)

rcompanion v 2.4.26 (2023-04-03)

rcompanion v 2.4.21 (2023-01-16)

rcompanion v 2.4.18 (2022-08-06)

rcompanion v 2.4.16 (2022-07-04)

rcompanion v 2.4.15 (2022-03-14)

rcompanion v 2.4.13 (2022-01-03)

rcompanion v 2.4.6 (2021-11-01)

rcompanion v 2.4.1 (2021-05-01)

rcompanion v 2.4.0 (2021-03-23)

rcompanion v 2.3.27 (2021-01-31)

rcompanion v 2.3.25 (2020-02-10)

rcompanion v 2.3.21 (2020-01-09)

rcompanion v 2.3.0 (2019-08-27)

rcompanion v 2.2.2 (2019-07-27)

rcompanion v 2.2.1 (2019-05-26)

rcompanion v 2.1.7 (2019-04-09)

rcompanion v 2.1.7 (2019-03-02)

rcompanion v 2.0.10 (2019-01-02)

rcompanion v 2.0.3 (2018-11-01)

rcompanion v 2.0.0 (2018-08-13)

rcompanion v 1.14.0 (2018-05-29)

rcompanion v 1.13.0 (2018-03-25)

Functions have been amended or added:

rcompanion v 1.12.3 (2018-02-25)

Functions added:

Minor improvements to: freemanTheta(), epsilsonSquared()

rcompanion v 1.11.3 (2018-02-19)

Improved cateNelson() and cramerV() functions.

rcompanion v 1.11.0 (2017-11-12)

rcompanion v 1.10.0 (2017-08-23)

rcompanion v 1.9.1 (2017-07-24)

rcompanion v 1.8.0 (2017)

rcompanion v 1.7.0 (2017)

rcompanion v 1.5.1 (2017-04-01)

rcompanion v 1.5.0 (2017-01-31)

rcompanion v 1.0.0 (2016-09-01)

This package provides custom functions for working through examples and analyses from “Summary and Analysis of Extension Education Program Evaluation in R” and “An R Companion for the Handbook of Biological Statistics”.

There are several functions which provide summary statistics for grouped data. These function titles tend to start with groupwise“. They provide means, medians, geometric means, and Huber M-estimators for groups, along with confidence intervals by traditional methods and bootstrap.

Function titles starting with pairwise” conduct pairwise tests among groups as a post-hoc analysis for omnibus tests. At the time of writing, these tests are Mood’s median test, sign test (for Friedman test), permutation test, robust anova, and ordinal regression. The output is a table of comparisons and p-values, or a matrix of p-values that can be parsed into a compact letter display.

There are also functions that are useful for comparing models. accuracy(), compareLM(), compareGLM(), and pairwiseModelAnova(). These use goodness-of-fit measures like AIC, BIC, and BICc, or likelihood ratio tests, or accuracy measures like RMSE and Efron’s pseudo r-square.

Functions for nominal data include post-hoc tests for Cochran-Mantel-Haenszel test (groupwiseCMH()), for McNemar–Bowker (pairwiseMcnemR()), and for tests of association like Chi-square, Fisher exact, and G-test (pairwiseNominalIndependence()).

A function close to my heart is cateNelson(), which performs Cate–Nelson analysis for bivariate data.

Examples and vignettes can be found at https://rcompanion.org/handbook/ and https://rcompanion.org/rcompanion/.