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.

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.

r2lUniv perform some basic analysis and generate the corresponding LaTeX code.
The basic analysis depends of the variable type.
Four types are considered:
- Nominal: modaliy, size, barplot
- Ordinal: modaliy, size, quartile, barplot
- Discrete: modaliy, size, mean, var, quartile, boxplot, barplot
- Continuous: mean, var, quartile, boxplot, barplot

Christophe Genolini, Bernard Desgraupes, Lionel Riou Franca
generate univariate and bivariate analyses in LaTeX or
HTML formats
functions.R logical.R factor.R ordered.R discrete.R
continuous.R rtlu.R rtlbDisplay.R rtlb.R

R-Squared Measures for Multilevel Models
c(
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"))
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>).
Mairead Shaw [aut, cre],
Jason Rights [aut],
Sonya Sterba [aut],
Jessica Flake [aut]