The Splancs package was written as an enhancement to S-Plus for display and analysis of spatial point pattern data; it has been ported to R and is in "maintenance mode".

This package provides routines for Maximum likelihood, Kalman filtering and smoothing, and Bayesian analysis of Normal linear State Space models, also known as Dynamic Linear Models.

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

This package provides a developer-facing interface to Arrow Database Connectivity (ADBC) for the purposes of driver development, driver testing, and building high-level database interfaces for users. ADBC is an API standard for database access libraries that uses Arrow for result sets and query parameters.

This package performs several conventional cross-validation statistical methods for climate-growth model in the climate reconstruction from tree rings, including Sign Test statistic, Reduction of Error statistic, Product Mean Test, Durbin-Watson statistic etc.

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

This package makes the qhull library available in R, in a similar manner as in Octave. Qhull computes convex hulls, Delaunay triangulations, halfspace intersections about a point, Voronoi diagrams, furthest-site Delaunay triangulations, and furthest-site Voronoi diagrams. It runs in 2-d, 3-d, 4-d, and higher dimensions. It implements the Quickhull algorithm for computing the convex hull. Qhull does not support constrained Delaunay triangulations, or mesh generation of non-convex objects, but the package does include some R functions that allow for this. Currently the package only gives access to Delaunay triangulation and convex hull computation.

This package provides an easy and simple way to read, write and display bitmap images stored in the PNG format. It can read and write both files and in-memory raw vectors.

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>.

This package provides tools and functions for managing the download of binary files. Binary repositories are defined in the YAML format. Defining new pre-download, download and post-download templates allow additional repositories to be added.

This package provides a collection of R functions to perform nonparametric analysis of covariance for regression curves or surfaces. Testing the equality or parallelism of nonparametric curves or surfaces is equivalent to analysis of variance (ANOVA) or analysis of covariance (ANCOVA) for one-sample functional data. Three different testing methods are available in the package, including one based on L-2 distance, one based on an ANOVA statistic, and one based on variance estimators.

This package contains supporting data sets that are used in other packages maintained by Torsten Hothorn.

This package provides functions for analyzing multivariate data. Dependencies of the distribution of the specified variable (response variable) to other variables (explanatory variables) are derived and evaluated by the Akaike Information Criterion (AIC).

Convert a logical vector or a vector of p-values or a correlation, difference, or distance matrix into a display identifying the pairs for which the differences were not significantly different.

This package adds distinctive yet unobtrusive geometric patterns where solid color fills are normally used. Patterned figures look just as professional when viewed by colorblind readers or when printed in black and white. The dozen included patterns can be customized in terms of scale, rotation, color, fill, line type, and line width. It is compatible with the ggplot2 package as well as grid graphics.

This package provides a suite of methods for powerful and robust microbiome data analysis, including data normalization, data simulation, community-level association testing and differential abundance analysis. It implements generalized UniFrac distances, Geometric Mean of Pairwise Ratios (GMPR) normalization, semiparametric data simulator, distance-based statistical methods, and feature- based statistical methods. The distance-based statistical methods include three extensions of PERMANOVA:

• PERMANOVA using the Freedman-Lane permutation scheme,

• PERMANOVA omnibus test using multiple matrices, and

• analytical approach to approximating PERMANOVA p-value.

Feature-based statistical methods include linear model-based methods for differential abundance analysis of zero-inflated high-dimensional compositional data.

This package contains functionality for importing and managing of downloaded genome annotation data from the Ensembl genome browser (European Bioinformatics Institute) and from the UCSC genome browser (University of California, Santa Cruz) and annotation routines for genomic positions and splice site positions.

Sending functions to remote processes can be wasteful of resources because they carry their environments with them. With this package, it is easy to create functions that are isolated from their environment. These isolated functions, also called crates, print to the console with their total size and can be easily tested locally before being sent to a remote.

This package provides tools to convert the output of utils::getParseData() to an XML tree, that one can search via XPath, and is easier to manipulate in general.

This package performs Bayesian calibration of computer models as per Kennedy and O'Hagan 2001. The package includes routines to find the hyperparameters and parameters; see the help page for stage1() for a worked example using the toy dataset. A tutorial is provided in the calex.Rnw vignette; and a suite of especially simple one dimensional examples appears in inst/doc/one.dim/.

dplyr is the next iteration of plyr. It is focused on tools for working with data frames. It has three main goals: 1) identify the most important data manipulation tools needed for data analysis and make them easy to use in R; 2) provide fast performance for in-memory data by writing key pieces of code in C++; 3) use the same code interface to work with data no matter where it is stored, whether in a data frame, a data table or database.

This package provides a complete GCC cross toolchain for C/C++ development to be installed in user profiles. This includes GCC, as well as libc (headers and binariesl), and Binutils. GCC is the GNU Compiler Collection.

This package provides a complete GCC cross toolchain for C/C++ development to be installed in user profiles. This includes GCC, as well as libc (headers and binariesl), and Binutils. GCC is the GNU Compiler Collection.

This package provides a complete GCC cross toolchain for C/C++ development to be installed in user profiles. This includes GCC, as well as libc (headers and binariesl), and Binutils. GCC is the GNU Compiler Collection.