This package provides a computationally stable approach of fitting a Gaussian Process (GP) model to a deterministic simulator.

int64 values can be created and accessed via the bit64 package and its integer64 class which package the int64 representation cleverly into a double. The nanotime package builds on this to support nanosecond-resolution timestamps. This package helps conversions between R and C++ via several helper functions provided via a single header file. A complete example client package is included as an illustration.

This is a package for pretty-printing R code without changing the user's formatting intent.

Access to selected functions found in liblwgeom, the light-weight geometry library used by PostGIS.

The objective of this package is to perform inference using an expressive statistical grammar that coheres with the Tidy design framework.

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 package provides sparse vectors powered by ALTREP (Alternative Representations for R Objects) that behave like regular vectors, and can thus be used in data frames. It also provides tools to convert between sparse matrices and data frames with sparse columns and functions to interact with sparse vectors.

This is a package providing tools for weighted k-Nearest neighbors for classification, regression and clustering.

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

This package implements Dirichlet regression models.

This package provides numerical simulations, and visualizations, of Hubbell's Unified Neutral Theory of Biodiversity (UNTB).

This is a package for converting natural language text into tokens. It includes tokenizers for shingled n-grams, skip n-grams, words, word stems, sentences, paragraphs, characters, shingled characters, lines, tweets, Penn Treebank, regular expressions, as well as functions for counting characters, words, and sentences, and a function for splitting longer texts into separate documents, each with the same number of words. The tokenizers have a consistent interface, and the package is built on the stringi and Rcpp packages for fast yet correct tokenization in UTF-8 encoding.

This package provides wrappers on regexpr and gregexpr to return the match results in tidy data frames.

This package provides functions for fitting and working with generalized additive models, as described in chapter 7 of "Statistical Models in S" (Chambers and Hastie (eds), 1991), and "Generalized Additive Models" (Hastie and Tibshirani, 1990).

The CommonMark specification defines a rationalized version of markdown syntax. This package uses the cmark reference implementation for converting markdown text into various formats including HTML, LaTeX and groff man. In addition, it exposes the markdown parse tree in XML format. The latest version of this package also adds support for Github extensions including tables, autolinks and strikethrough text.

This package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.

This package provides a fast implementation of a key-value store. Environments are commonly used as key-value stores, but every time a new key is used, it is added to R's global symbol table, causing a small amount of memory leakage. This can be problematic in cases where many different keys are used. Fastmap avoids this memory leak issue by implementing the map using data structures in C++.

This package performs penalized multivariate analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis.

In order to reduce potential package dependencies and conflicts, generics provides a number of commonly used S3 generics that are not provided by base R methods related to model fitting.

This package provides vector map data from https://www.naturalearthdata.com/. Access functions are provided in the accompanying package rnaturalearth.

This package provides fast and memory-friendly tools for text vectorization, topic modeling (LDA, LSA), word embeddings (GloVe), similarities. It provides a source-agnostic streaming API, which allows researchers to perform analysis of collections of documents which are larger than available RAM. All core functions are parallelized to benefit from multicore machines.

The package offers functions for analyzing and interactively exploring large-scale single-cell RNA-seq datasets. Pagoda2 primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. pagoda2 was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos.

This package provides an R to C/C++ interface that runs the Leiden community detection algorithm to find a basic partition. It runs the equivalent of the leidenalg find_partition() function. This package includes the required source code files from the official leidenalg distribution and functions from the R igraph package.

This package implements fast hierarchical, agglomerative clustering routines. Part of the functionality is designed as drop-in replacement for existing routines: linkage() in the SciPy package scipy.cluster.hierarchy, hclust() in R's stats package, and the flashClust package. It provides the same functionality with the benefit of a much faster implementation. Moreover, there are memory-saving routines for clustering of vector data, which go beyond what the existing packages provide.