As a successor of the packages BatchJobs and BatchExperiments, this package provides a parallel implementation of the Map function for high performance computing systems managed by various schedulers. A multicore and socket mode allow the parallelization on a local machines, and multiple machines can be hooked up via SSH to create a makeshift cluster. Moreover, the package provides an abstraction mechanism to define large-scale computer experiments in a well-organized and reproducible way.

The DBI package provides a database interface (DBI) definition for communication between R and relational database management systems. All classes in this package are virtual and need to be extended by the various R/DBMS implementations.

This package provides a derivative-free optimization by quadratic approximation based on an interface to Fortran implementations by M. J. D. Powell.

This package provides an R interface to the GNU Linear Programming Kit, software for solving large-scale linear programming (LP), mixed integer linear programming (MILP) and other related problems.

This package provides functions for cognitive diagnosis modeling and multidimensional item response modeling for dichotomous and polytomous item responses. It enables the estimation of the DINA and DINO model, the multiple group (polytomous) GDINA model, the multiple choice DINA model, the general diagnostic model (GDM), the structured latent class model (SLCA), and regularized latent class analysis. See George, Robitzsch, Kiefer, Gross, and Uenlue (2017) doi:10.18637/jss.v074.i02 for further details on estimation and the package structure. For tutorials on how to use the CDM package see George and Robitzsch (2015, doi:10.20982/tqmp.11.3.p189) as well as Ravand and Robitzsch (2015).

This package provides functions to convert R objects into JSON objects and vice-versa.

This package provides advanced tryCatch and try functions for better error handling (logging, stack trace with source code references and support for post-mortem analysis via dump files).

This package provides an implementation of many measures for the assessment of the stability of feature selection. Both simple measures and measures which take into account the similarities between features are available.

This package provides classes and functions to create and summarize different types of resampling objects (e.g. bootstrap, cross-validation).

Extract metadata from NetCDF data sources; these can be files, file handles or servers. This package leverages and extends the lower level functions of the RNetCDF package providing a consistent set of functions that all return data frames.

This package contains the program ttf2pt1, for use with the extrafont package.

This package provides maximally selected rank statistics with several p-value approximations.

This package allows the user to create new Github gists, update gists with new files, rename files, delete files, get and delete gists, star and un-star them, fork them, open a gist in your default browser, get an embed code for a gist, list gist commits, and get rate limit information when authenticated.

This package provides a set of tools for the statistical analysis of data using:

1. normal linear models;

2. generalized linear models;

3. negative binomial regression models as alternative to the Poisson regression models under the presence of overdispersion;

4. beta-binomial and random-clumped binomial regression models as alternative to the binomial regression models under the presence of overdispersion;

5. zero-inflated and zero-altered regression models to deal with zero-excess in count data;

6. generalized nonlinear models;

7. generalized estimating equations for cluster correlated data.

This package provides a tool to provide an easy, intuitive and consistent access to information contained in various R models, like model formulas, model terms, information about random effects, data that was used to fit the model or data from response variables. The package mainly revolves around two types of functions: Functions that find (the names of) information, starting with find_, and functions that get the underlying data, starting with get_. The package has a consistent syntax and works with many different model objects, where otherwise functions to access these information are missing.

Provides implementations of functions which have been introduced in R since version 3.0.0. The backports are conditionally exported which results in R resolving the function names to the version shipped with R (if available) and uses the implemented backports as fallback. This way package developers can make use of the new functions without worrying about the minimum required R version.

Generalized Additive Mixed Modeling (GAMM; Lin & Zhang, 1999) as implemented in the R package mgcv is a nonlinear regression analysis which is particularly useful for time course data such as EEG, pupil dilation, gaze data (eye tracking), and articulography recordings, but also for behavioral data such as reaction times and response data. As time course measures are sensitive to autocorrelation problems, GAMMs implements methods to reduce the autocorrelation problems. This package includes functions for the evaluation of GAMM models (e.g., model comparisons, determining regions of significance, inspection of autocorrelational structure in residuals) and interpreting of GAMMs (e.g., visualization of complex interactions, and contrasts).

This package provides efficient tools to compute the proximity between rows or columns of large matrices. Functions are optimised for large sparse matrices using the Armadillo and Intel TBB libraries. Among several built-in similarity/distance measures, computation of correlation, cosine similarity and Euclidean distance is particularly fast.

This package provides a collection of evaluation metrics, including loss, score and utility functions, that measure regression, classification and ranking performance.

This package contains a set of functions that extend the cancor function. These functions provide new numerical and graphical outputs. It also includes a regularized extension of the canonical correlation analysis to deal with datasets with more variables than observations.

This package offers an easy to use way to draw a Venn diagram with ggplot2.

This package represents a collection of plotting and table output functions for data visualization. Results of various statistical analyses (that are commonly used in social sciences) can be visualized using this package, including simple and cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, principal component analysis and correlation matrices, cluster analyses, scatter plots, stacked scales, effects plots of regression models (including interaction terms) and much more. This package supports labelled data.

This package provides a set of estimators for models and (robust) covariance matrices, and tests for panel data econometrics, including within/fixed effects, random effects, between, first-difference, nested random effects as well as instrumental-variable (IV) and Hausman-Taylor-style models, panel generalized method of moments (GMM) and general FGLS models, mean groups (MG), demeaned MG, and common correlated effects (CCEMG) and pooled (CCEP) estimators with common factors, variable coefficients and limited dependent variables models. Test functions include model specification, serial correlation, cross-sectional dependence, panel unit root and panel Granger (non-)causality. Typical references are general econometrics text books such as Baltagi (2021), Econometric Analysis of Panel Data (<doi:10.1007/978-3-030-53953-5>), Hsiao (2014), Analysis of Panel Data (<doi:10.1017/CBO9781139839327>), and Croissant and Millo (2018), Panel Data Econometrics with R (<doi:10.1002/9781119504641>).

This package provides a simple interface to lat/long projection and datum transformation of the PROJ.4 cartographic projections library. It allows transformation of geographic coordinates from one projection and/or datum to another.