The tictoc package provides the timing functions tic and toc that can be nested. It provides an alternative to system.time() with a different syntax similar to that in another well-known software package. tic and toc are easy to use, and are especially useful when timing several sections in more than a few lines of code.

This package provides functions for testing affine hypotheses on the regression coefficient vector in regression models with autocorrelated errors.

This package implements numerically-stable Gauss-Hermite quadrature rules and utility functions for adaptive GH quadrature.

This package provides two methods of plotting categorical scatter plots such that the arrangement of points within a category reflects the density of data at that region, and avoids over-plotting.

Alabama stands for Augmented Lagrangian Adaptive Barrier Minimization Algorithm; it is used for optimizing smooth nonlinear objective functions with constraints. Linear or nonlinear equality and inequality constraints are allowed.

This package contains the function ggsurvplot() for easily drawing beautiful and 'ready-to-publish' survival curves with the 'number at risk' table and 'censoring count plot'. Other functions are also available to plot adjusted curves for Cox model and to visually examine Cox model assumptions.

Compare complex R objects and reveal the key differences. This package was designed particularly for use in testing packages where being able to quickly isolate key differences makes understanding test failures much easier.

This package fits generalized linear models efficiently using RcppEigen'. The iteratively reweighted least squares implementation utilizes the step-halving approach of Marschner to help safeguard against convergence issues.

This package provides improved predictive models by indirect classification and bagging for classification, regression and survival problems as well as resampling based estimators of prediction error.

This package provides binning and plotting functions for hexagonal bins. It uses and relies on grid graphics and formal (S4) classes and methods.

This package lets you rarefy data, calculate diversity and plot the results.

This package provides an interface to Amazon Web Services analytics services, including Elastic MapReduce Hadoop and Spark big data service, Elasticsearch search engine, and more.

This package provides statistical methods especially developed to analyze anthropometric data. These methods are aimed at providing effective solutions to some commons problems related to Ergonomics and Anthropometry. They are based on clustering, the statistical concept of data depth, statistical shape analysis and archetypal analysis.

This package provides R bindings to the Sundown Markdown rendering library (https://github.com/vmg/sundown). Markdown is a plain-text formatting syntax that can be converted to XHTML or other formats.

This package provides R6 abstract classes for building machine learning models with a scikit-learn like API. Scikit-learn is a popular module for the Python programming language whose design became a de facto standard in industry for machine learning tasks.

This is a package to compare sequence fragment lengths or molecular weights from pairs of lanes. The number of matching bands in the Restriction Fragment Length Polymorphism (RFLP) data is calculated using the align-and-count method.

This package provides functions for the analysis of income distributions for subgroups of the population as defined by a set of variables like age, gender, region, etc. This entails a Kolmogorov-Smirnov test for a mixture distribution as well as functions for moments, inequality measures, entropy measures and polarisation measures of income distributions. This package thus aides the analysis of income inequality by offering tools for the exploratory analysis of income distributions at the disaggregated level.

r-kmer is an R package for rapidly computing distance matrices and clustering large sequence datasets using fast alignment-free k-mer counting and recursive k-means partitioning.

Constructs confidence intervals on the probability of success in a binomial experiment via several parameterizations

This package provides tests and assertions to perform frequent argument checks. A substantial part of the package was written in C to minimize any worries about execution time overhead.

This package computes model and semi partial R squared with confidence limits for the linear and generalized linear mixed model (LMM and GLMM). The R squared measure from L. J. Edwards et al. (2008) is extended to the GLMM using penalized quasi-likelihood (PQL) estimation (see Jaeger et al. (2016)).

This package provides functions for obtaining the density, random variates and maximum likelihood estimates of the Zero-truncated Poisson lognormal distribution and their mixture distribution.

This package implements general purpose tools, such as functions for sampling and basic manipulation of Brazilian lawsuits identification number. It also implements functions for text cleaning, such as accentuation removal.

This package provides implementation of methods for estimation of quantitative maps from Multi-Parameter Mapping (MPM) acquisitions including adaptive smoothing methods in the framework of the ESTATICS model. The smoothing method is described in Mohammadi et al. (2017). <doi:10.20347/WIAS.PREPRINT.2432>. Usage of the package is also described in Polzehl and Tabelow (2019), Magnetic Resonance Brain Imaging, Chapter 6, Springer, Use R! Series. <doi:10.1007/978-3-030-29184-6_6>.