# ==============================================================================
# PsyStatsPracticals environment setup
# Install and load all packages used at
# https://kosugitti.github.io/PsyStatsPracticals/
# ==============================================================================
# ------------------------------------------------------------------------------
# Package manager
# ------------------------------------------------------------------------------
if (!requireNamespace("pacman", quietly = TRUE)) {
install.packages("pacman")
}
# ------------------------------------------------------------------------------
# Core and data manipulation
# ------------------------------------------------------------------------------
pacman::p_load(
tidyverse, # meta-package for dplyr, ggplot2, tidyr, forcats, ...
broom # tidy model output
)
# ------------------------------------------------------------------------------
# Visualisation
# ------------------------------------------------------------------------------
pacman::p_load(
ggplot2, # bundled in tidyverse but loaded standalone in some chapters
patchwork, # combining plots
gridExtra, # arranging plots
RColorBrewer, # colour palettes
ggrepel, # avoid label overlap
corrplot, # correlation-matrix visualisation
GGally, # pair plots
bayesplot, # MCMC visualisation
qgraph, # network diagrams
semPlot, # SEM path diagrams
lavaanPlot # lavaan result visualisation
)
# ------------------------------------------------------------------------------
# General statistics and multivariate analysis
# ------------------------------------------------------------------------------
pacman::p_load(
psych, # general psychometric utilities
car, # regression diagnostics (Anova, etc.)
MASS, # multivariate normal RNG and others
effsize, # effect sizes
pwr, # power analysis
e1071, # skewness, kurtosis, fuzzy c-means, ...
mclust # Gaussian mixture clustering
)
# ------------------------------------------------------------------------------
# Regression and mixed models
# ------------------------------------------------------------------------------
pacman::p_load(
lmerTest, # linear mixed-effects models
multilevel # ICC and related tools
)
# ------------------------------------------------------------------------------
# Bayesian
# ------------------------------------------------------------------------------
pacman::p_load(
brms, # Stan front-end
cmdstanr, # CmdStan interface
bayestestR # posterior summaries
)
# Note: cmdstanr is distributed by the Stan-dev repository, not by CRAN.
# If not yet installed:
# install.packages("cmdstanr",
# repos = c("https://stan-dev.r-universe.dev", getOption("repos")))
# After installation, install CmdStan itself with cmdstanr::install_cmdstan().
# ------------------------------------------------------------------------------
# Structural equation modelling
# ------------------------------------------------------------------------------
pacman::p_load(
lavaan # SEM
)
# ------------------------------------------------------------------------------
# Item response theory and test theory
# ------------------------------------------------------------------------------
pacman::p_load(
ltm, # unidimensional IRT
mirt, # multidimensional IRT
exametrika # integrated test theory
)
# ------------------------------------------------------------------------------
# Multidimensional scaling
# ------------------------------------------------------------------------------
pacman::p_load(
smacof # MDS
)
# ------------------------------------------------------------------------------
# Text mining
# ------------------------------------------------------------------------------
pacman::p_load(
gibasa # Japanese morphological analysis
)
# Note: gibasa relies on a MeCab (or mecab-ipadic-neologd) installation. On
# macOS, `brew install mecab mecab-ipadic` is the usual route. The text-mining
# example in Chapter 16 is Japanese-language; for other languages, substitute
# an appropriate tokeniser.20 Installation Guide
To install in one go all the packages used in this textbook, run the following code.