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

This package provides an arsenal of R functions for large-scale statistical summaries, which are streamlined to work within the latest reporting tools in R and RStudio and which use formulas and versatile summary statistics for summary tables and models. The primary functions include

1. tableby, a Table-1-like summary of multiple variable types by the levels of one or more categorical variables;

2. paired, a Table-1-like summary of multiple variable types paired across two time points;

3. modelsum, which performs simple model fits on one or more endpoints for many variables (univariate or adjusted for covariates);

4. freqlist, a powerful frequency table across many categorical variables;

5. comparedf, a function for comparing data.frames; and

6. write2, a function to output tables to a document.

This is a package that allows conversion to and from data in JavaScript Object Notation (JSON) format. This allows R objects to be inserted into Javascript/ECMAScript/ActionScript code and allows R programmers to read and convert JSON content to R objects. This is an alternative to the rjson package.

This package provides a unified R graphics backend. Render R graphics fast and easy to many common file formats. It provides a thread safe C interface for asynchronous rendering of R graphics.

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

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.

This package provides an interface to Amazon Web Services database services, including Relational Database Service (RDS), DynamoDB NoSQL database, and more.

This package provides response time distributions (density/PDF, distribution function/CDF, quantile function, and random generation):

• Ratcliff diffusion model (Ratcliff & McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>) based on C code by Andreas and Jochen Voss and

• linear ballistic accumulator (LBA; Brown & Heathcote, 2008, <doi:10.1016/j.cogpsych.2007.12.002>) with different distributions underlying the drift rate.

The DBI package provides a database interface (DBI) definition for communication between R and relational database management systems. All classes in this package are virtual and need to be extended by the various R/DBMS implementations.

This package provides infrastructure for seriation with an implementation of several seriation/sequencing techniques to reorder matrices, dissimilarity matrices, and dendrograms. It also provides (optimally) reordered heatmaps, color images and clustering visualizations like dissimilarity plots, and visual assessment of cluster tendency plots (VAT and iVAT).

This package represents an implementation of functions to optimize ordering of nodes in a dendrogram, without affecting the meaning of the dendrogram. A dendrogram can be sorted based on the average distance of subtrees, or based on the smallest distance value. These sorting methods improve readability and interpretability of tree structure, especially for tasks such as comparison of different distance measures or linkage types and identification of tight clusters and outliers. As a result, it also introduces more meaningful reordering for a coupled heatmap visualization.

This package provides functions for fitting the entire solution path of the Elastic-Net and also provides functions for estimating sparse Principal Components. The Lasso solution paths can be computed by the same function.

This package provides functions for cognitive diagnosis modeling and multidimensional item response modeling for dichotomous and polytomous item responses. It enables the estimation of the DINA and DINO model, the multiple group (polytomous) GDINA model, the multiple choice DINA model, the general diagnostic model (GDM), the structured latent class model (SLCA), and regularized latent class analysis. See George, Robitzsch, Kiefer, Gross, and Uenlue (2017) doi:10.18637/jss.v074.i02 for further details on estimation and the package structure. For tutorials on how to use the CDM package see George and Robitzsch (2015, doi:10.20982/tqmp.11.3.p189) as well as Ravand and Robitzsch (2015).

dplyr is the next iteration of plyr. It is focused on tools for working with data frames. It has three main goals: 1) identify the most important data manipulation tools needed for data analysis and make them easy to use in R; 2) provide fast performance for in-memory data by writing key pieces of code in C++; 3) use the same code interface to work with data no matter where it is stored, whether in a data frame, a data table or database.

This package provides ACE and AVAS methods for choosing regression transformations.

This package provides an implementation of heatmaps that offers more control over dimensions and appearance.

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 is an implementation of a regularized regression prediction and empirical Bayes method to recover the true gene expression profile in noisy and sparse single-cell RNA-seq data. In single-cell RNA sequencing (scRNA-seq) studies, only a small fraction of the transcripts present in each cell are sequenced. This leads to unreliable quantification of genes with low or moderate expression, which hinders downstream analysis. This package single-cell analysis via expression recovery (SAVER) implements an expression recovery method for unique molecule index (UMI)-based scRNA-seq data that borrows information across genes and cells to provide accurate expression estimates for all genes.

This package provides functions to compare a model object to a comparison object. If the objects are not identical, the functions can be instructed to explore various modifications of the objects (e.g., sorting rows, dropping names) to see if the modified versions are identical.

This package implements the regularized Gaussian maximum likelihood estimation of the inverse of a covariance matrix. It uses Newton's method and coordinate descent to solve the regularized inverse covariance matrix estimation problem.

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

This package contains a set of functions that extend the cancor function. These functions provide new numerical and graphical outputs. It also includes a regularized extension of the canonical correlation analysis to deal with datasets with more variables than observations.

These functions were developed to support functional data analysis as described in Ramsay, J. O. and Silverman, B. W. (2005) Functional Data Analysis. The package includes data sets and script files working many examples.

This package provides a basic set of R functions for querying the Cancer Genomics Data Server (CGDS), hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC).