This package provides tools to compares k samples using the Anderson-Darling test, Kruskal-Wallis type tests with different rank score criteria, Steel's multiple comparison test, and the Jonckheere-Terpstra (JT) test. It computes asymptotic, simulated or (limited) exact P-values, all valid under randomization, with or without ties, or conditionally under random sampling from populations, given the observed tie pattern. Except for Steel's test and the JT test it also combines these tests across several blocks of samples.

This package contains general data structures and functions for longitudinal data with multiple variables, repeated measurements, and irregularly spaced time points. It also implements a shrinkage estimator of dynamical correlation and dynamical covariance.

There are three main goals to the vctrs package:

1. To propose vec_size() and vec_type() as alternatives to length() and class(). These definitions are paired with a framework for type-coercion and size-recycling.

2. To define type- and size-stability as desirable function properties, use them to analyse existing base function, and to propose better alternatives. This work has been particularly motivated by thinking about the ideal properties of c(), ifelse(), and rbind().

3. To provide a new vctr base class that makes it easy to create new S3 vectors. vctrs provides methods for many base generics in terms of a few new vctrs generics, making implementation considerably simpler and more robust.

This package provides a set of tools to help explain which variables are most important in a random forests. Various variable importance measures are calculated and visualized in different settings in order to get an idea on how their importance changes depending on our criteria (Hemant Ishwaran and Udaya B. Kogalur and Eiran Z. Gorodeski and Andy J. Minn and Michael S. Lauer (2010) <doi:10.1198/jasa.2009.tm08622>, Leo Breiman (2001) <doi:10.1023/A:1010933404324>).

spacetime provides classes and methods for spatio-temporal data, including space-time regular lattices, sparse lattices, irregular data, and trajectories; utility functions for plotting data as map sequences (lattice or animation) or multiple time series; methods for spatial and temporal matching or aggregation, retrieving coordinates, print, summary, etc.

This package proposes a new file format named gson for storing gene set and related information, and provides read, write and other utilities to process this file format.

This package is a compatibility wrapper to replace the orphaned package by Romain Francois. New applications should use the openssl or base64enc package instead.

Dimensionality reduction (DR) is widely used in many domains for analyzing and visualizing high-dimensional data. The tidydr package provides uniform output and is compatible with multiple methods, including prcomp, mds, Rtsne, etc.

This package gives you the ability to automatically generate and serve an HTTP API from R functions using the annotations in the R documentation around your functions.

This package provides a comprehensive collection for structural multivariate function estimation using smoothing splines.

This package provides template functions to assist in building friendly R packages that praise their users.

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

This package provides regression models for grouped and coarse data, under the coarsened at random assumption.

This package integrates sophisticated mixed modelling methods with a whole genome approach to detecting significant QTL in linkage maps.

This package implements affinity propagation clustering introduced by Frey and Dueck (2007). The package further provides leveraged affinity propagation and an algorithm for exemplar-based agglomerative clustering that can also be used to join clusters obtained from affinity propagation. Various plotting functions are available for analyzing clustering results.

This package implements methods to perform fast approximate K-nearest neighbor search on the input matrix. The algorithm is based on the N2 implementation of an approximate nearest neighbor search using hierarchical NSW graphs.

This is a package supporting cluster analysis for cognitive diagnosis based on the Asymptotic Classification Theory (Chiu, Douglas & Li, 2009; doi:10.1007/s11336-009-9125-0). Given the sample statistic of sum-scores, cluster analysis techniques can be used to classify examinees into latent classes based on their attribute patterns. In addition to the algorithms used to classify data, three labeling approaches are proposed to label clusters so that examinees' attribute profiles can be obtained.

In S3 generics, it's useful to take ... so that methods can have additional arguments. But this flexibility comes at a cost: misspelled arguments will be silently ignored. The ellipsis package is an experiment that allows a generic to warn if any arguments passed in ... are not used.

This package provides methods for cluster analysis. It is a much extended version of the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".

This package provides a system for organizing column names in data. It is aimed at supporting a prefix-based and suffix-based column naming scheme. It extends dplyr functionality to add ordering by function and more explicit renaming.

This package analyzes data with robust methods such as regression methodology including model selections and multivariate statistics.

This package provides an implementation of the Tukey, Mandel, Johnson-Graybill, LBI, Tusell and modified Tukey non-additivity tests.

In order to create smooth animation between states of data, tweening is necessary. This package provides a range of functions for creating tweened data that can be used as basis for animation. Furthermore it adds a number of vectorized interpolaters for common R data types such as numeric, date and color.

This package lets you compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and machine learning models in R. Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference. Details can be found in Arel-Bundock, Greifer, and Heiss (2024) <doi:10.18637/jss.v111.i09>.