of computation are provided and described as follows. First, The
'r2glmm' package is limited to the LMM. Second, The method introduced

Description: This package provide facilities to export some R analysis in a LaTeX format. Working either on a single variable or on a data.frame, it compute several statistics (frequency, centrality, dispersion, graph) for each variable, according to their nature.
Packaged: Fri Jan 30 10:07:45 2009; Christophe

Packaged: Thu Feb 19 09:57:38 2009; Christophe

Description: This package provide facilities to export some R analysis
in a LaTeX format. Working either on a single variable or on a
data.frame, it compute several statistics (frequency,
centrality, dispersion, graph) for each variable, according to
Packaged: 2009-10-11 18:16:20 UTC; Christophe

Packaged: 2009-12-06 21:54:32 UTC; Christophe

Description: r2lUniv perform some basic analysis and generate the corresponding LaTeX code.
The basic analysis depends of the variable type.
- Ordinal: modaliy, size, quartile, barplot
- Discrete: modaliy, size, mean, var, quartile, boxplot, barplot
- Continuous: mean, var, quartile, boxplot, barplot
Packaged: Mon Jan 21 08:58:00 2008; Christophe

Author: Christophe Genolini, Bernard Desgraupes, Lionel Riou Franca
Description: generate univariate and bivariate analyses in LaTeX or
Collate: functions.R logical.R factor.R ordered.R discrete.R
Packaged: 2010-06-30 17:13:15 UTC; Christophe

Packaged: 2011-07-28 09:57:12 UTC; Christophe

Title: R-Squared Measures for Multilevel Models
person("Mairead", "Shaw", email = "mairead.shaw@mail.mcgill.ca", role = c("aut", "cre")),
person("Jason", "Rights", email = "jrights@psych.ubc.ca", role = "aut"),
person("Sonya", "Sterba", email = "sonya.sterba@vanderbilt.edu", role = "aut"),
person("Jessica", "Flake", email = "jessica.flake@mcgill.ca", role = "aut"))
Description: Generates both total- and level-specific R-squared measures from
Rights and Sterba’s (2019) <doi:10.1037/met0000184> framework of R-squared measures for multilevel
models with random intercepts and/or slopes, which is based on a completely
full decomposition of variance. Additionally generates graphical
representations of these R-squared measures to allow visualizing and
interpreting all measures in the framework together as an integrated set.
This framework subsumes 10 previously-developed R-squared measures for
multilevel models as special cases of 5 measures from the framework, and it
also includes several newly-developed measures. Measures in the framework
can be used to compute R-squared differences when comparing multilevel
models (following procedures in Rights & Sterba (2020) <doi:10.1080/00273171.2019.1660605>).
Imports: dplyr (>= 0.8.5), magrittr (>= 1.5), rlang (>= 0.4.6), stringr
Packaged: 2020-09-23 05:22:34 UTC; maireadshaw