Exact and approximation algorithms for variable-subset
selection in ordinary linear regression models.  Either compute all
submodels with the lowest residual sum of squares, or determine the
single-best submodel according to a pre-determined statistical
criterion.  Hofmann et al. (2020) <10.18637/jss.v093.i03>.

Exact and approximation algorithms for variable-subset
selection in ordinary linear regression models.  Either compute all
submodels with the lowest residual sum of squares, or determine the
single-best submodel according to a pre-determined statistical
criterion.  Hofmann et al. (2020) <doi:10.18637/jss.v093.i03>.

This package accompanies John Curtin's graduate course in
the General Linear Model (PSY710).

This package accompanies Markus Brauer and John Curtin's
two course graduate series in the General Linear Model (PSY
610/710).

Accompanies Markus Brauer, Bas Rokers, and John Curtin's two-course graduate series in
the General Linear Model (PSY 610/710).
psych, gvlma, AICcmodavg, lme4, pbkrtest

Accompanies Markus Brauer, Bas Rokers, and John Curtin's two-course graduate series in
General, Generalized, and Multi-level Linear Models (PSY 610/710).

Provides tools and a consistent interface to support analyses using General, Generalized, and Multi-level Linear Models.
AICcmodavg, car, gplots, graphics, grDevices, gvlma, lme4,
pbkrtest, psych, pwr, stats, utils

Accompanies Markus Brauer, Bas Rokers, and John Curtin's two-course
graduate series in General, Generalized, and Multi-level Linear Models (PSY
610/710).
psych,gvlma, AICcmodavg, lme4, pbkrtest, grDevices, graphics,
stats, utils

psych,gvlma, AICcmodavg, lme4, pbkrtest, grDevices, graphics,
stats, utils, gplots, pwr

c(person(given = "Mark", family = "de Rooij", role = c("aut", "cre","cph"), email
= "rooijm@fsw.leidenuniv.nl"), person(given = "Frank", family = "Busing", role = c("aut"),
email = "busing@fsw.leidenuniv.nl"), person(given = "Juan", family = "Claramunt Gonzalez",
role = c("ctb"), email = "j.claramunt.gonzalez@fsw.leidenuniv.nl"))
Set of tools for mapping of categorical response variables based on principal component analysis (pca) and multidimensional unfolding (mdu).
R (>= 3.5.0), ggplot2, ggrepel, ggforce, tidyverse, dplyr
Mark de Rooij [aut, cre, cph],
Frank Busing [aut],
Juan Claramunt Gonzalez [ctb]