This package provides tools to create a measure of inter-point dissimilarity useful for clustering mixed data, and, optionally, perform the clustering.

This package provides an R port of the library Clipper. It performs polygon clipping operations (intersection, union, set minus, set difference) for polygonal regions of arbitrary complexity, including holes. It computes offset polygons (spatial buffer zones, morphological dilations, Minkowski dilations) for polygonal regions and polygonal lines. It computes the Minkowski Sum of general polygons. There is a function for removing self-intersections from polygon data.

This package provides an implementation of multiscale bootstrap resampling for assessing the uncertainty in hierarchical cluster analysis. It provides an AU (approximately unbiased) P-value as well as a BP (bootstrap probability) value for each cluster in a dendrogram.

This package provides a versatile interior point solver that solves linear programs (LPs), quadratic programs (QPs), second-order cone programs (SOCPs), semidefinite programs (SDPs), and problems with exponential and power cone constraints (https://clarabel.org/stable/). For quadratic objectives, unlike interior point solvers based on the standard homogeneous self-dual embedding (HSDE) model, Clarabel handles quadratic objective without requiring any epigraphical reformulation of its objective function. It can therefore be significantly faster than other HSDE-based solvers for problems with quadratic objective functions. Infeasible problems are detected using using a homogeneous embedding technique.

This package provides common base and stats methods for rle objects, aiming to make it possible to treat them transparently as vectors.

The package provides estimators of the mode of univariate unimodal (and sometimes multimodal) data and values of the modes of usual probability distributions.

This package performs estimation of physical activity and sedentary behavior variables from activPAL events files.

This package performs angle-based outlier detection on a given data frame. It offers three methods to process data:

1. full but slow implementation using all the data that has cubic complexity;

2. a fully randomized method;

3. a method using k-nearest neighbours.

These algorithms are well suited for high dimensional data outlier detection.

This package is a feature selection package of the mlr3 ecosystem. It selects the optimal feature set for any mlr3 learner. The package works with several optimization algorithms e.g. random search, Recursive feature elimination, and genetic search. Moreover, it can automatically optimize learners and estimate the performance of optimized feature sets with nested resampling.

This package provides two high quality and fast PPRNGs that may be used in an OpenMP parallel environment. In addition, there is a generator for one dimensional low-discrepancy sequence.

This is a package for developers to check user-supplied function arguments. It is designed to be simple, fast and customizable. Error messages follow the tidyverse style guide.

This package provides a collection of R functions for analyzing finite mixture models.

This package contain data sets and utilities from Project MOSAIC used to teach mathematics, statistics, computation and modeling. Project MOSAIC is a community of educators working to tie together aspects of quantitative work that students in science, technology, engineering and mathematics will need in their professional lives, but which are usually taught in isolation, if at all.

This package provides various tools for developers of R packages interfacing with Stan, including functions to set up the required package structure, S3 generics and default methods to unify function naming across Stan-based R packages, and vignettes with recommendations for developers.

These functions were developed to support functional data analysis as described in Ramsay, J. O. and Silverman, B. W. (2005) Functional Data Analysis. The package includes data sets and script files working many examples.

This package provides an optimization method based on sequential quadratic programming for maximum likelihood estimation of the mixture proportions in a finite mixture model where the component densities are known. The algorithm is expected to obtain solutions that are at least as accurate as the state-of-the-art MOSEK interior-point solver, and they are expected to arrive at solutions more quickly when the number of samples is large and the number of mixture components is not too large.

This package provides functions for extracting feature contributions from a random forest model from package randomForest. Feature contributions provide detailed information about the relationship between data variables and the predicted value returned by random forest model.

This is a package to simplify loading of system fonts and Google Fonts into R, in order to support other packages.

This package lets you construct Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Results Data objects. These objects are used and re-used to construct summary tables, visualizations, and written reports. The package also exports utilities for working with these objects and creating new Analysis Results Data objects.

This package provides a simple and intuitive pipe-friendly framework, coherent with the tidyverse design philosophy, for performing basic statistical tests, including t-test, Wilcoxon test, ANOVA, Kruskal-Wallis and correlation analyses. The output of each test is automatically transformed into a tidy data frame to facilitate visualization. Additional functions are available for reshaping, reordering, manipulating and visualizing correlation matrix.

This package provides a general framework for high-dimensional undirected graph estimation. It integrates data preprocessing, neighborhood screening, graph estimation, and model selection techniques into a pipeline.

This package provides a low-level interface to the Java VM very much like .C/.Call and friends. It allows the creation of objects, calling methods and accessing fields.

The C++ header files of the Stan project are provided by this package. There is a shared object containing part of the CVODES library, but it is not accessible from R. r-stanheaders is only useful for developers who want to utilize the LinkingTo directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies.

The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or variational methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, templated statistical and linear algebra functions that can handle the automatically differentiable scalar types (and doubles, ints, etc.), and a parser for the Stan language. The r-rstan package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models.

Meta-analysis is widely used to summarize estimated effects sizes across multiple statistical tests. Standard fixed and random effect meta-analysis methods assume that the estimated of the effect sizes are statistically independent. Here we relax this assumption and enable meta-analysis when the correlation matrix between effect size estimates is known.