This package implements various procedures for finding multiple change-points. Two methods make use of dynamic programming and pruning, with no distributional assumptions other than the existence of certain absolute moments in one method. Hierarchical and exact search methods are included. All methods return the set of estimated change-points as well as other summary information.

Format dates and times flexibly and to whichever locales make sense. This package parses dates, times, and date-times in various formats (including string-based ISO 8601 constructions). The formatting syntax gives the user many options for formatting the date and time output in a precise manner. Time zones in the input can be expressed in multiple ways and there are many options for formatting time zones in the output as well. Several of the provided helper functions allow for automatic generation of locale-aware formatting patterns based on date/time skeleton formats and standardized date/time formats with varying specificity.

This package provides a set of tools for displaying, modeling and analysing multivariate abundance data in community ecology.

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 edit and simplify geojson, Spatial, and sf objects. This is a wrapper around the mapshaper JavaScript library to perform topologically-aware polygon simplification, as well as other operations such as clipping, erasing, dissolving, and converting multi-part to single-part geometries.

This package provides various methods for clustering and cluster validation. For example, it provides fixed point clustering, linear regression clustering, clustering by merging Gaussian mixture components, as well as symmetric and asymmetric discriminant projections for visualisation of the separation of groupings.

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.

This package provides gradient projection algorithms for factor rotation. For details see ?GPArotation.

This package implements a DBI compliant interface to Presto, a distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes.

This package offers an easy to use way to draw a Venn diagram with ggplot2.

This is a package for constructing minimum-cost regular spanning subgraph as part of a non-parametric two-sample test for equality of distribution.

This package provides a compilation of extra ggplot2 themes, scales and utilities, including a spell check function for plot label fields and an overall emphasis on typography.

This package provides fast and accurate convolution-type smoothed quantile regression, implemented using Barzilai-Borwein gradient descent with a Huber regression warm start. Confidence intervals for regression coefficients are constructed using multiplier bootstrap.

This package provides tools for handling Base64 encoding. It is more flexible than the orphaned "base64" package.

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.

This package provides tools to estimate parameters of accumulated damage (load duration) models based on failure time data under a Bayesian framework, using Approximate Bayesian Computation (ABC), and to assess long-term reliability under stochastic load profiles.

Building on the infrastructure provided by the lattice package, this package provides several new high-level graphics functions and methods, as well as additional utilities such as panel and axis annotation functions.

This is a package for drawing calibrated scales with tick marks on (non-orthogonal) variable vectors in scatterplots and biplots.

This package provides simple and crisp publication-quality graphics for the ExPosition family of packages. See An ExPosition of the Singular Value Decomposition in R (Beaton et al 2014) <doi:10.1016/j.csda.2013.11.006>.

This package provides a collection of functions for interpretation and presentation of regression analysis. These functions are used to produce the statistics lectures in http://pj.freefaculty.org/guides. The package includes regression diagnostics, regression tables, and plots of interactions and "moderator" variables. The emphasis is on "mean-centered" and "residual-centered" predictors. The vignette rockchalk offers a fairly comprehensive overview.

This package provides a re-implementation of the gWidgets API. The API is defined in this package. A second, toolkit-specific package is required to use it.

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

This package supplies tools for tabulating and analyzing the results of predictive models. The methods employed are applicable to virtually any predictive model and make comparisons between different methodologies straightforward.

This package provides an R implementation of an extension of the BayeScan software for codominant markers, adding the option to group individual SNPs into pre-defined blocks. A typical application of this new approach is the identification of genomic regions, genes, or gene sets containing one or more SNPs that evolved under directional selection.