In my work, I make extensive use of the statistical software package/environment R. One of the great strengths of R is the large number of add-on packages that extend the functionality of R (i.e., the functionality of the base/standard packages that are installed by default) in various ways. Here, I keep track and list packages that I make use of on a (semi)regular basis. My work would be many magnitudes more difficult if these packages (and R itself!) did not exist.

• nlme and lme4: These are my go-to packages for fitting (generalized) (non-)linear mixed-effects models (e.g., for analyzing multilevel and longitudinal data).
• GLMMadaptive and glmmML: Other useful packages for fitting generalized linear mixed-effects models.
• glmmTMB: For mixed-effects models with zero-inflation, a dispersion model, and/or some alternative var-cov structures for the random effects.
• MCMCglmm, brms, and rstanarm: For fitting (generalized) linear mixed-effects models in a Bayesian framework.
• mbest: For fitting (generalized) linear mixed-effects models using the method of moments.

• survival: For time-to-event data / Cox proportional hazards regression models.
• CAMAN and mclust: For fitting mixture models.
• flexclust: Useful cluster algorithms.
• aods3: For analyzing overdispersed counts and proportions.
• censReg: For censored regression (Tobit) models.
• betareg: For beta regression.
• VGAM: For vector generalized linear and additive models.
• gee, geepack, and glmtoolbox: For fitting models via generalized estimation equations (GEE).
• mgcv: For generalized additive models (GAMs).

• boot: As the name implies, for bootstrapping.
• car: Package to accompany An R Companion to Applied Regression by Fox and Weisberg (2019). I regularly make use of some functions from this package such as linear.hypothesis and Anova.
• lmtest and multcomp: Useful packages for getting post model fitting contrasts (i.e., testing general linear hypotheses).
• lmerTest: For approximate Wald-type t-tests for lmer model objects.
• emmeans: To obtain marginal means.
• glmulti and MuMIn: Information theoretic methods for model selection, model averaging, and multimodel inference.
• sandwich and clubSandwich: For (cluster) robust inferences using the Eicker-Huber-White method.
• mice and miceadds: For multiple imputation.
• MatchIt: For propensity score matching.

• The stats package already provides a useful collection of optimizers (optimize, optim, nlminb, constrOptim, nlm functions). For root finding, I use the uniroot function.
• minqa: Several derivative-free optimizers.
• nloptr: Provides an R interface to NLopt, a free/open-source library for nonlinear optimization.
• dfoptim: Includes another implementation of the Nelder-Mead algorithm (as in optim), the Hooke-Jeeves algorithm, and the Mesh Adaptive Direct Searches (MADS) algorithm.
• ucminf and lbfgsb3c: Additional derivative-free, quasi-Newton type algorithms.
• subplex: A subspace-searching simplex algorithm.
• BB: For the Barzilai-Borwein gradient decent method.
• optimParallel: Parallel version of the BFGS method.
• cubature: For multivariate numerical integration (and integrate from stats for one-dimensional integration).
• numDeriv: For numerical calculation of first and second order derivatives.
• pracma: Also for numerical derivatives, integration, and some other numerical analysis functions.

• Matrix: For working with dense and sparse matrices.
• gsl: Wrapper for some functions from the GNU Scientific Library.

• MASS: Package to support Modern Applied Statistics with S by Venables and Ripley (2002). Includes many useful functions, but mentioned here because of the mvrnorm function for generating data from a multivariate normal distribution and fitdistr for fitting univariate distributions.
• mvtnorm: For the multivariate normal and t distribution.
• BiasedUrn: For the noncentral hypergeometric distribution.
• CompQuadForm: For the distribution function of quadratic forms.
• SuppDists: For some other distributions.

• metafor: Obviously (I wrote it).
• metadat: Lots of meta-analysis datasets.
• meta: Another nice package for meta-analysis.
• metaSEM: For approaching meta-analysis from a structural equation modeling (SEM) perspective.
• Since meta-analysis is one of my primary research areas, I have played around with many packages mentioned under the Meta-Analysis Task View.

• psych and psychometric: Lots of functions for psychometric analyses (e.g., principal component analysis, factor analysis, reliability analysis).
• GPArotation: Includes various rotation methods for factor analysis.
• sem and lavaan: For confirmatory factor analysis (CFA) and structural equation modeling (SEM).
• OpenMx: Another package for structural equation modeling; have used this mostly just for twin models.
• ltm and eRm: For IRT analyses / Rasch modeling.

• rjags: For interfacing with JAGS.
• R2WinBUGS: For interfacing with OpenBUGS and WinBUGS.
• coda: In combination with the previous two packages, for summarizing and plotting chains.
• MCMCglmm, brms, and rstanarm: For fitting various types of (generalized) linear (mixed-effects) models in a Bayesian framework.

• genetics: Wide variety of functions for dealing with genetic data.
• LDheatmap: For creating heat maps of the degree of linkage disequilibrium between SNPs.
• poolr: For gene-based testing in GWAS.

• parallel: For the 'base R' way of doing parallel/multicore processing.
• pbapply: To show progress bars when using parallel processing.
• future and future.apply: For the 'future' way of doing parallel/multicore processing.

• graphics: I am an old fart when it comes to graphics, sticking mostly to 'base graphics' (I don't actually have any strong opinions on 'base graphics' versus 'ggplot2'; I got into R before ggplot2 or even ggplot was around and just learned how to get all my graphing done with the tools available at the time – and that still works for me).
• lattice and latticeExtra: But occasionally I use trellis-type graphics (especially in combination with multilevel or longitudinal data).
• plotly: And for creating interactive graphics, plotly is really nice.
• igraph: For drawing network graphs/analyses.
• viridis and viridisLite: Some very nice color maps.
• RColorBrewer: And more color schemes.
• MASS: The kde2d function for 2d kernel density estimation and plotting.
• akima and interp: For interpolating gridded data so that it can be plotted with functions that require a matrix as input.
• hexbin: Another approach for creating scatterplots that indicate the density of particular areas.
• beanplot, beeswarm, and vioplot: For bean, bee swarm, and violin plots (as alternatives to standard boxplots).
• scatterplot3d and rgl: When I want to show off with 3-dimensional plots.
• extrafont: For making use of additional fonts in graphs.
• Cairo: For the Cairo graphics device.

• devtools: Various useful functions for package development.
• remotes: For installing (usually development versions of) packages from GitHub and other repos.
• testthat: For software/unit testing.
• covr: For tracking/reporting code coverage of the tests.
• Formula: To make use of extended formulas in model fitting functions.
• mathjaxr: To include nicely rendered equations (via MathJax) in the HTML help files of an R package.

• pander: For pandoc markdown.
• knitr and rmarkdown: Tools for generating dynamic documents (besides utils::Sweave)
• table1 and tableone: for creating tables with descriptive statistics (typically for multiple groups).

• foreign, readstata13, readxl, and haven: For reading in SPSS, Stata, and Excel data.
• RStata: For interacting with Stata directly from R.
• esmpack: For preparation and management of ESM/EMA data.
• sos, packagefinder, CRANsearcher, and pkgsearch: For searching for R packages on CRAN.