This package is designed as an integrated package for genetic data analysis of both population and family data. Currently, it contains functions for sample size calculations of both population-based and family-based designs, probability of familial disease aggregation, kinship calculation, statistics in linkage analysis, and association analysis involving genetic markers including haplotype analysis with or without environmental covariates. Over years, the package has been developed in-between many projects hence also in line with the name (gap).

This package provides a collection of functions to create spatial weights matrix objects from polygon contiguities, from point patterns by distance and tessellations, for summarizing these objects, and for permitting their use in spatial data analysis, including regional aggregation by minimum spanning tree.

This package contains the datasets and a few functions for use with the practicals outlined in Appendix A of the book Statistical Models (Davison, 2003, Cambridge University Press). The practicals themselves can be found at http://statwww.epfl.ch/davison/SM/.

Proto is an object oriented system using object-based, also called prototype-based, rather than class-based object oriented ideas.

Data exploration and modelling is a process in which a lot of data artifacts are produced. Artifacts like: subsets, data aggregates, plots, statistical models, different versions of data sets and different versions of results. Archivist helps to store and manage artifacts created in R. It allows you to store selected artifacts as binary files together with their metadata and relations. Archivist allows sharing artifacts with others. It can look for already created artifacts by using its class, name, date of the creation or other properties. It also makes it easy to restore such artifacts.

This package provides tools for performing the leaf reordering for the dendrogram that preserves the hierarchical clustering result and at the same time tries to group instances from the same class together.

This package is a collection of baseline correction algorithms. Beside those it provides a framework and a Tcl/Tk enabled GUI for optimizing baseline algorithm parameters. Typical use is the removal of the background effects from spectra, which are originating from various types of spectroscopy and spectrometry. Also, there is a possibility of optimizing this with regard to regression or classification results. Correction methods include polynomial fitting, weighted local smoothers and many more.

This package provides access to phyloinformatic data in NeXML format. The package should add new functionality to R such as the possibility to manipulate NeXML objects in more various and refined way and compatibility with ape objects.

This package computes exact conditional p-values and quantiles using an implementation of the Shift-Algorithm by Streitberg & Roehmel.

Tree based algorithms can be improved by introducing boosting frameworks. LightGBM is one such framework, based on Ke, Guolin et al. (2017). This package offers an R interface to work with it. It is designed to be distributed and efficient with the following goals:

1. Faster training speed and higher efficiency;

2. lower memory usage;

3. better accuracy;

4. parallel learning supported; and

5. capable of handling large-scale data.

The range of functions provided by this package makes it possible to draw highly versatile genomic sequence logos. Features include, but are not limited to, modifying colour schemes and fonts used to draw the logo, generating multiple logo plots, and aiding the visualisation with annotations. Sequence logos can easily be combined with other ggplot2 plots.

This package provides functions, data sets, analyses and examples from the third edition of the book A Handbook of Statistical Analyses Using R (Torsten Hothorn and Brian S. Everitt, Chapman & Hall/CRC, 2014). The first chapter of the book, which is entitled An Introduction to R, is completely included in this package, for all other chapters, a vignette containing all data analyses is available. In addition, Sweave source code for slides of selected chapters is included in this package.

This package can be used to solve Linear Programming / Linear Optimization problems by using the simplex algorithm.

This package provides helper functions to work with spreadsheets and the A1:D10 style of cell range specification.

This is a package supporting the analysis of multivariate dichotomous and polytomous data using latent trait models under the Item Response Theory approach. It includes the Rasch, the Two-Parameter Logistic, the Birnbaum's Three-Parameter, the Graded Response, and the Generalized Partial Credit Models.

This package provides methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

This package provides functions to perform k-prototypes partitioning clustering for mixed variable-type data according to Z.Huang (1998): Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Variables, Data Mining and Knowledge Discovery 2, 283-304.

This package provides implementations of the family of map() functions from the purrr package that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.

This package preloads class unions for defining/loading core OOMPA tools. It also includes vectorized operations for row-by-row means, variances, and t-tests. Finally, it provides new colorschemes.

NbClust provides 30 indexes for determining the optimal number of clusters in a data set and offers the best clustering scheme from different results to the user.

This package provides a Shiny app that can disconnect for a variety of reasons: an unrecoverable error occurred in the app, the server went down, the user lost internet connection, or any other reason that might cause the Shiny app to lose connection to its server. With shinydisconnect, you can call disonnectMessage anywhere in a Shiny app's UI to add a nice message when this happens. It works locally (running Shiny apps within RStudio) and on Shiny servers.

This package provides distance-based parametric bootstrap tests for clustering with spatial neighborhood information. It implements some distance measures, clustering of presence-absence, abundance and multilocus genetical data for species delimitation, nearest neighbor based noise detection.

This package provides a %dopar% adapter such that any type of futures can be used as backends for the foreach framework.

This is a package for random number generation for the truncated multivariate normal and Student t distribution. It computes probabilities, quantiles and densities, including one-dimensional and bivariate marginal densities. It computes first and second moments (i.e. mean and covariance matrix) for the double-truncated multinormal case.