This package contains the data set for the crowd-sourced benchmarks from running the benchmarkme package.

This package provides functions for regulation, decomposition and analysis of space-time series. The pastecs library is a PNEC-Art4 and IFREMER initiative to bring PASSTEC 2000 functionalities to R.

This package implements the diffusion map method of data parametrization, including creation and visualization of diffusion maps, clustering with diffusion K-means and regression using the adaptive regression model.

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

This package provides a computational toolbox for recursive partitioning. The core of the package is ctree(), an implementation of conditional inference trees which embed tree-structured regression models into a well defined theory of conditional inference procedures. This non-parametric class of regression trees is applicable to all kinds of regression problems, including nominal, ordinal, numeric, censored as well as multivariate response variables and arbitrary measurement scales of the covariates. Based on conditional inference trees, cforest() provides an implementation of Breiman's random forests. The function mob() implements an algorithm for recursive partitioning based on parametric models (e.g. linear models, GLMs or survival regression) employing parameter instability tests for split selection. Extensible functionality for visualizing tree-structured regression models is available.

Graphical and tabular effect displays, e.g., of interactions, for various statistical models with linear predictors.

This is a QTL mapping toolkit for inbred crosses and recombinant inbred lines. It includes maximum likelihood and Bayesian tools.

This package provides power analysis functions along the lines of Cohen (1988).

Access to selected functions found in liblwgeom, the light-weight geometry library used by PostGIS.

This package provides interactive, configurable and graphics visualization of the chromosome regions of any living organism allowing users to map chromosome elements (like genes, SNPs etc.) on the chromosome plot. It introduces a special plot viz. the "chromosome heatmap" that, in addition to mapping elements, can visualize the data associated with chromosome elements (like gene expression) in the form of heat colors. Users can investigate the detailed information about the mappings (like gene names or total genes mapped on a location) or can view the magnified single or double stranded view of the chromosome at a location showing each mapped element in sequential order. The package provide multiple features like visualizing multiple sets, chromosome heat-maps, group annotations, adding hyperlinks, and labelling. The plots can be saved as HTML documents that can be customized and shared easily. In addition, you can include them in R Markdown or in R Shiny applications.

This is an alternative mechanism for importing objects from packages. The syntax allows for importing multiple objects from a package with a single command in an expressive way. The import package bridges some of the gap between using library (or require) and direct (single-object) imports. Furthermore the imported objects are not placed in the current environment. It is also possible to import objects from stand-alone .R files.

This package provides an R interface to the dygraphs JavaScript charting library (a copy of which is included in the package). It provides rich facilities for charting time-series data in R, including highly configurable series- and axis-display and interactive features like zoom/pan and series/point highlighting.

This package provides a collection of tools for building RAxML supermatrix using PHYLIP or aligned FASTA files. These functions will be useful for building large phylogenies using multiple markers.

This package implements multiple performance measures for supervised learning. It includes over 40 measures for regression and classification. Additionally, meta information about the performance measures can be queried, e.g. what the best and worst possible performances scores are.

This package provides functions to convert R objects into JSON objects and vice-versa.

The ggcorrplot package can be used to visualize easily a correlation matrix using ggplot2. It provides a solution for reordering the correlation matrix and displays the significance level on the plot. It also includes a function for computing a matrix of correlation p-values.

This package performs sparse linear discriminant analysis for Gaussians and mixture of Gaussian models.

This package provides an R wrapper to the Python natural language processing (NLP) library spaCy, from http://spacy.io.

This package provides functions for plotting graphical shapes such as ellipses, circles, cylinders, arrows, ...

This package provides some low-level utilities to use for R package development. It currently provides managers for multiple package specific options and registries, vignette, unit test and bibtex related utilities.

This package extends several functions to the complex domain, including the matrix exponential and logarithm, and the determinant.

This package provides kernel-based machine learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods kernlab includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver.

This package implements the libyaml YAML 1.1 parser and emitter (http://pyyaml.org/wiki/LibYAML) for R.

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