This package provides tools for exploratory data analysis and data visualization of biological sequence (DNA and protein) data. It also includes utilities for sequence data management under the ACNUC system.

This package provides a helper that tests DBI back ends for conformity to the interface.

This package allows you to render vector-based SVG images into high-quality custom-size bitmap arrays using the librsvg2 library. The resulting bitmap can be written to e.g. PNG, JPEG or WEBP format. In addition, the package can convert images directly to various formats such as PDF or PostScript.

This package provides procedures to work with block diagonal symmetric matrices, a special case of sparse matrices.

This package provides an interface to Amazon Web Services analytics services, including Elastic MapReduce Hadoop and Spark big data service, Elasticsearch search engine, and more.

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 provides fast machine learning algorithms including matrix factorization and divisive clustering for large sparse and dense matrices.

The pls package implements multivariate regression methods: Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and Canonical Powered Partial Least Squares (CPPLS). It supports:

• several algorithms: the traditional orthogonal scores (NIPALS) PLS algorithm, kernel PLS, wide kernel PLS, Simpls, and PCR through svd

• multi-response models (or PLS2)

• flexible cross-validation

• Jackknife variance estimates of regression coefficients

• extensive and flexible plots: scores, loadings, predictions, coefficients, (R)MSEP, R², and correlation loadings

• formula interface, modelled after lm(), with methods for predict, print, summary, plot, update, etc.

• extraction functions for coefficients, scores, and loadings

• MSEP, RMSEP, and R² estimates

• multiplicative scatter correction (MSC)

This package lets you read and write JSON Web Keys (JWK, rfc7517), generate and verify JSON Web Signatures (JWS, rfc7515) and encode/decode JSON Web Tokens (JWT, rfc7519). These standards provide modern signing and encryption formats that are natively supported by browsers via the JavaScript WebCryptoAPI, and used by services like OAuth 2.0, LetsEncrypt, and Github Apps.

This package provides a unit testing system for R designed to be fun, flexible and easy to set up.

This package provides an implementation of both the exact and approximation methods for computing the cumulative distribution function (CDF) of the Poisson binomial distribution. It also provides the probability mass function (PMF), quantile function, and random number generation for the Poisson binomial distribution.

This package provides functions and datasets for bootstrapping from the book "Bootstrap Methods and Their Application" by A.C. Davison and D.V. Hinkley (1997, CUP), originally written by Angelo Canty for S.

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

This package provides miscellaneous helper functions for the development of R packages.

This is a package for the estimation, validation and prediction of kriging models.

This package provides an API for https://orcid.org. Functions include searching for people, searching by DOI, or searching by Orcid ID.

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

The clusterGeneration package provides functions for generating random clusters, generating random covariance/correlation matrices, calculating a separation index (data and population version) for pairs of clusters or cluster distributions, and 1-D and 2-D projection plots to visualize clusters. The package also contains a function to generate random clusters based on factorial designs with factors such as degree of separation, number of clusters, number of variables, number of noisy variables.

This package provides functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, and more.

Some basic features of MUMPS are wrapped in a class whose methods can be used for sequentially solving a sparse linear system (symmetric or not) with one or many right hand sides (dense or sparse). There is a possibility to do separately symbolic analysis, LU (or LDL^t) factorization and system solving. Third part ordering libraries are included and can be used: PORD, METIS, SCOTCH.

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 tool takes longitudinal dataset as input and analyzes if there is significant change of the features over time (a proxy for treatments), while detects and controls for covariates simultaneously. LongDat is able to take in several data types as input, including count, proportion, binary, ordinal and continuous data. The output table contains p values, effect sizes and covariates of each feature, making the downstream analysis easy.

This package creates "Table 1", i.e., description of baseline patient characteristics, which is essential in every medical research. It supports both continuous and categorical variables, as well as p-values and standardized mean differences. Weighted data are supported via the survey package.

This package provides a vectorized R function for calculating probabilities from a standard bivariate normal CDF.