This package provides functions for creating plots and image files in a unified way regardless of output format (EPS, PDF, PNG, SVG, TIFF, WMF, etc.). Default device options as well as scales and aspect ratios are controlled in a uniform way across all device types. Switching output format requires minimal changes in code. This package is ideal for large-scale batch processing, because it will never leave open graphics devices or incomplete image files behind, even on errors or user interrupts.

This package provides routines for the statistical analysis of landmark shapes, including Procrustes analysis, graphical displays, principal components analysis, permutation and bootstrap tests, thin-plate spline transformation grids and comparing covariance matrices. See Dryden, I.L. and Mardia, K.V. (2016). Statistical shape analysis, with Applications in R (2nd Edition), John Wiley and Sons.

This package provides a C++ header library for using the libsoda-cxx library with R. The C++ header reimplements the lsoda function from the ODEPACK library for solving initial value problems for first order ordinary differential equations. The C++ header can be used by other R packages by linking against this package. The C++ functions can be called inline using Rcpp. Finally, the package provides an ode function to call from R.

This package provides selected commonly used methods for choosing univariate class intervals for mapping or other graphics purposes.

This package implements the Figueiredo machine learning algorithm for adaptive sparsity and the Wong algorithm for adaptively sparse Gaussian geometric models.

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 extends the mlr3 package with cluster analysis.

This package provides miscellaneous functions to help customize ggplot2 objects. High-level functions are provided to post-process ggplot2 layouts and allow alignment between plot panels, as well as setting panel sizes to fixed values. Other functions include a custom geom, and helper functions to enforce symmetric scales or add tags to facetted plots.

This package provides a minimal set of predicates and assertions used by the assertive package. This is mainly for use by other package developers who want to include run-time testing features in their own packages.

This package supports multiple precision arithmetic (big integers and rationals, prime number tests, matrix computation), "arithmetic without limitations" using the GNU Multiple Precision library.

This package provides basic I/O tools for streaming and data parsing.

This package provides functions for feed-forward neural networks with a single hidden layer, and for multinomial log-linear models.

This package implements beta regression for modeling beta-distributed dependent variables on the open unit interval (0, 1), e.g., rates and proportions, see Cribari-Neto and Zeileis (2010) <doi:10.18637/jss.v034.i02>. Moreover, extended-support beta regression models can accommodate dependent variables with boundary observations at 0 and/or 1. For the classical beta regression model, alternative specifications are provided: Bias-corrected and bias-reduced estimation, finite mixture models, and recursive partitioning for beta regression, see <doi:10.18637/jss.v048.i11>.

ICGE is a package that helps to estimate the number of real clusters in data as well as to identify atypical units. The underlying methods are based on distances rather than on unit x variables.

This package provides tools to check the latest release version of R and R packages (on CRAN, Bioconductor or Github).

When analyzing data, plots are a helpful tool for visualizing data and interpreting statistical models. This package provides a set of simple tools for building plots incrementally, starting with an empty plot region, and adding bars, data points, regression lines, error bars, gradient legends, density distributions in the margins, and even pictures. The package builds further on R graphics by simply combining functions and settings in order to reduce the amount of code to produce for the user. As a result, the package does not use formula input or special syntax, but can be used in combination with default R plot functions.

Functions implemented in this package allow coercing (i.e. convert) network data between classes provided by other R packages. Currently supported classes are those defined in packages network and igraph.

This package computes standardized mean differences and confidence intervals for multiple data types based on Yang, D., & Dalton, J. E. (2012) <https://support.sas.com/resources/papers/proceedings12/335-2012.pdf>.

This package provides a method to test genetic linkage with covariates by regression methods with response IBD sharing for relative pairs. Account for correlations of IBD statistics and covariates for relative pairs within the same pedigree.

This package provides functions and scripts used in the book "Chemometrics with R - Multivariate Data Analysis in the Natural Sciences and Life Sciences" by Ron Wehrens, Springer (2011).

This package lets you estimate fixed effects binary choice models (logit and probit) with potentially many individual fixed effects and compute average partial effects. Incidental parameter bias can be reduced with an asymptotic bias correction proposed by Fernandez-Val (2009) <doi:10.1016/j.jeconom.2009.02.007>.

This package provides tools for the estimation and simulation of latent variable models.

This package provides RStudio addins and R functions that make copy-pasting vectors and tables to text painless.

This package provides different approaches for selecting the threshold in generalized Pareto distributions. Most of them are based on minimizing the AMSE-criterion or at least by reducing the bias of the assumed GPD-model. Others are heuristically motivated by searching for stable sample paths, i.e. a nearly constant region of the tail index estimator with respect to k, which is the number of data in the tail. The third class is motivated by graphical inspection. In addition, a sequential testing procedure for GPD-GoF-tests is also implemented here.