This package provides a multi-modal simulation engine for studying dynamic cellular processes at single-cell resolution.

This package provides an exact Goodness-of-Fit test for multinomial data with fixed probabilities. It can be used to determine whether a set of counts fits a given expected ratio. To see whether a set of observed counts fits an expectation, one can examine all possible outcomes with xmulti() or a random sample of them with xmonte() and find the probability of an observation deviating from the expectation by at least as much as the observed. As a measure of deviation from the expected, one can use the log-likelihood ratio, the multinomial probability, or the classic chi-square statistic. A histogram of the test statistic can also be plotted and compared with the asymptotic curve.

This package provides a port of the web-based software DAGitty for analyzing structural causal models (also known as directed acyclic graphs or DAGs). This package computes covariate adjustment sets for estimating causal effects, enumerates instrumental variables, derives testable implications (d-separation and vanishing tetrads), generates equivalent models, and includes a simple facility for data simulation.

This package contains R functions and datasets detailed in the book "Time Series Analysis with Applications in R (second edition)" by Jonathan Cryer and Kung-Sik Chan.

This package provides tools to query and print information about the current R session. It is similar to utils::sessionInfo(), but includes more information about packages, and where they were installed from.

This package implements two methods for performing a constrained principal component analysis (PCA), where non-negativity and/or sparsity constraints are enforced on the principal axes (PAs). The function nsprcomp computes one principal component (PC) after the other. Each PA is optimized such that the corresponding PC has maximum additional variance not explained by the previous components. In contrast, the function nscumcomp jointly computes all PCs such that the cumulative variance is maximal. Both functions have the same interface as the prcomp function from the stats package (plus some extra parameters).

This package was previously an R wrapper of the ARPACK library, and now a shell of the R package RSpectra, an R interface to the Spectra library for solving large scale eigenvalue/vector problems. The current version of rARPACK simply imports and exports the functions provided by RSpectra. New users of rARPACK are advised to switch to the RSpectra package.

This package provides an extensible framework for automatically placing direct labels onto multicolor plots. Label positions are described using positioning methods that can be re-used across several different plots. There are heuristics for examining trellis and ggplot objects and inferring an appropriate positioning method.

This package provides a convenient tool to install and update Bioconductor packages.

This package provides tools for shrunken centroids regularized discriminant analysis for the purpose of classifying high dimensional data.

This is a package for parallel computing with a network of local and remote workers. It enables fast exchange of results between the workers through a Redis database. Key features include task queues, local caching, and sophisticated error handling.

This package provides a collection of R functions for analyzing finite mixture models.

For tree ensembles such as random forests, regularized random forests and gradient boosted trees, this package provides functions for: extracting, measuring and pruning rules; selecting a compact rule set; summarizing rules into a learner; calculating frequent variable interactions; formatting rules in latex code. Reference: Interpreting tree ensembles with inTrees (Houtao Deng, 2019, <doi:10.1007/s41060-018-0144-8>).

In putative Transcription Factor Binding Sites (TFBSs) identification from sequence/alignments, we are interested in the significance of certain match scores. TFMPvalue provides the accurate calculation of a p-value with a score threshold for position weight matrices, or the score with a given p-value. It is an interface to code originally made available by Helene Touzet and Jean-Stephane Varre, 2007, Algorithms Mol Biol:2, 15. Touzet and Varre (2007).

This package provides tool for estimation, testing and regression modeling of subdistribution functions in competing risks, as described in Gray (1988), A class of K-sample tests for comparing the cumulative incidence of a competing risk, Ann. Stat. 16:1141-1154, and Fine JP and Gray RJ (1999), A proportional hazards model for the subdistribution of a competing risk, JASA, 94:496-509.

The wordspace package turns R into an interactive laboratory for empirical research on distributional semantic models (DSM). It consists of a small set of carefully designed functions, most of which

• encapsulate non-trivial R operations in a user-friendly manner or

• provide efficient and memory-lean C implementations of key operations.

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.

Sending functions to remote processes can be wasteful of resources because they carry their environments with them. With this package, it is easy to create functions that are isolated from their environment. These isolated functions, also called crates, print to the console with their total size and can be easily tested locally before being sent to a remote.

This package provides R bindings to OpenSSL libssl and libcrypto, plus custom SSH pubkey parsers. It supports RSA, DSA and NIST curves P-256, P-384 and P-521. Cryptographic signatures can either be created and verified manually or via x509 certificates. AES block cipher is used in CBC mode for symmetric encryption; RSA for asymmetric (public key) encryption. High-level envelope functions combine RSA and AES for encrypting arbitrary sized data. Other utilities include key generators, hash functions (md5, sha1, sha256, etc), base64 encoder, a secure random number generator, and bignum math methods for manually performing crypto calculations on large multibyte integers.

This package contains an S3 class with methods for totally ordered indexed observations. It is particularly aimed at irregular time series of numeric vectors/matrices and factors.

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

This package provides infrastructure for psychometric modeling such as data classes (for item response data and paired comparisons), basic model fitting functions (for Bradley-Terry, Rasch, parametric logistic IRT, generalized partial credit, rating scale, multinomial processing tree models), extractor functions for different types of parameters (item, person, threshold, discrimination, guessing, upper asymptotes), unified inference and visualizations, and various datasets for illustration. It is intended as a common lightweight and efficient toolbox for psychometric modeling and a common building block for fitting psychometric mixture models in package psychomix and trees based on psychometric models in package psychotree.

This package provides zm, a utility that allows you to zoom/navigate any plot when called with any active plot.

The basic idea of latent semantic analysis (LSA) is, that text do have a higher order (=latent semantic) structure which, however, is obscured by word usage (e.g. through the use of synonyms or polysemy). By using conceptual indices that are derived statistically via a truncated singular value decomposition (a two-mode factor analysis) over a given document-term matrix, this variability problem can be overcome.