This package provides a file format for storing tensors that is secure (doesn't allow for code execution), fast and simple to implement. safetensors also enables cross language and cross frameworks compatibility making it an ideal format for storing machine learning model weights.

This package provides tools for data importation, recoding, and inspection. There are functions to create new project folders, R code templates, create uniquely named output directories, and to quickly obtain a visual summary for each variable in a data frame. The main feature here is the systematic implementation of the "variable key" framework for data importation and recoding.

This package provides tools to create and modify network objects. The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes.

The ability to tune models is important. tune contains functions and classes to be used in conjunction with other tidymodels packages for finding reasonable values of hyper-parameters in models, pre-processing methods, and post-processing steps.

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 is an extension to Shiny that brings interactions and animation effects from the jQuery UI library.

Partial application is the process of reducing the arity of a function by fixing one or more arguments, thus creating a new function lacking the fixed arguments. The curry package provides three different ways of performing partial function application by fixing arguments from either end of the argument list (currying and tail currying) or by fixing multiple named arguments (partial application). This package provides this functionality through the %<%, %-<%, and %><% operators which allows for a programming style comparable to modern functional languages. Compared to other implementations such a purrr::partial() the operators in curry composes functions with named arguments, aiding in autocomplete etc.

The ade4 package contains data analysis functions to analyze ecological and environmental data in the framework of Euclidean exploratory methods.

This package is an R package designed for QC, analysis, and exploration of single cell RNA-seq data. It easily enables widely-used analytical techniques, including the identification of highly variable genes, dimensionality reduction; PCA, ICA, t-SNE, standard unsupervised clustering algorithms; density clustering, hierarchical clustering, k-means, and the discovery of differentially expressed genes and markers.

This package provides a wrapper for the homologene database by the National Center for Biotechnology Information (NCBI). It allows searching for gene homologs across species. The package also includes an updated version of the homologene database where gene identifiers and symbols are replaced with their latest (at the time of submission) version and functions to fetch latest annotation data to keep updated.

This package provides a simple set of wrapper functions for data.table::fread() that allows subsetting or filtering rows and selecting columns of table-formatted files too large for the available RAM.

This package lets you rarefy data, calculate diversity and plot the results.

This package contains functions to compute the nonparametric maximum likelihood estimator (MLE) for the bivariate distribution of (X,Y), when realizations of (X,Y) cannot be observed directly. To be more precise, we consider the situation where we observe a set of rectangles that are known to contain the unobservable realizations of (X,Y). We compute the MLE based on such a set of rectangles. The methods can also be used for univariate censored data (see data set cosmesis), and for censored data with competing risks (see data set menopause). The package also provides functions to visualize the observed data and the MLE.

This package provides functions for simple fixed and random effects meta-analysis for two-sample comparisons and cumulative meta-analyses. It draws standard summary plots, funnel plots, and computes summaries and tests for association and heterogeneity.

This package provides a derivative-free optimization by quadratic approximation based on an interface to Fortran implementations by M. J. D. Powell.

This package provides an interface to a large number of classification and regression techniques. These techniques include machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Also included:

• Generic resampling, including cross-validation, bootstrapping and subsampling;

• Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems;

• Filter and wrapper methods for feature selection;

• Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling.

Most operations can be parallelized.

This package lets you build complex Structured Query Language (SQL) queries dynamically. Classes and/or factory functions are used to produce a syntax tree from which the final character string is generated. Strings and identifiers are automatically quoted using the right quotes, using either American National Standards Institute (ANSI) quoting or the quoting style of an existing database connector. Style can be configured to set uppercase/lowercase for keywords, remove unnecessary spaces, or omit optional keywords.

This R package provides functions to create formattable vectors and data frames. Formattable vectors are printed with text formatting, and formattable data frames are printed with multiple types of formatting in HTML to improve the readability of data presented in tabular form rendered in web pages.

This package implements an R interface to the Leiden algorithm, an iterative community detection algorithm on networks. The algorithm is designed to converge to a partition in which all subsets of all communities are locally optimally assigned, yielding communities guaranteed to be connected. The implementation proves to be fast, scales well, and can be run on graphs of millions of nodes (as long as they can fit in memory).

This package provides analytic derivatives and information matrices for fitted linear mixed effects (lme) models and generalized least squares (gls) models estimated using lme() (from package nlme) and gls() (from package nlme), respectively. The package includes functions for estimating the sampling variance-covariance of variance component parameters using the inverse Fisher information. The variance components include the parameters of the random effects structure (for lme models), the variance structure, and the correlation structure. The expected and average forms of the Fisher information matrix are used in the calculations, and models estimated by full maximum likelihood or restricted maximum likelihood are supported. The package also includes a function for estimating standardized mean difference effect sizes based on fitted lme or gls models.

Format dates and times flexibly and to whichever locales make sense. This package parses dates, times, and date-times in various formats (including string-based ISO 8601 constructions). The formatting syntax gives the user many options for formatting the date and time output in a precise manner. Time zones in the input can be expressed in multiple ways and there are many options for formatting time zones in the output as well. Several of the provided helper functions allow for automatic generation of locale-aware formatting patterns based on date/time skeleton formats and standardized date/time formats with varying specificity.

This package provides a simple git client for R based on libgit2 with support for SSH and HTTPS remotes. All functions in gert use basic R data types (such as vectors and data-frames) for their arguments and return values. User credentials are shared with command line git through the git-credential store and SSH keys stored on disk or ssh-agent.

This package provides functions for bitwise operations on integer vectors.

This R package provides tools for training gapped-kmer SVM classifiers for DNA and protein sequences. This package supports several sequence kernels, including: gkmSVM, kmer-SVM, mismatch kernel and wildcard kernel.