Many models contain tuning parameters (i.e. parameters that cannot be directly estimated from the data). These tools can be used to define objects for creating, simulating, or validating values for such parameters.

This package lets you use syntax inspired by the package glue to extract matched substrings in a more intuitive and compact way than by using standard regular expressions.

How fast can you type R functions on your keyboard? Find out by running a zty.pe game: export R functions as instructions to type to destroy opponents' vessels.

This package checks adherence to a given style, syntax errors and possible semantic issues. It supports on the fly checking of R code edited with RStudio IDE, Emacs and Vim.

This package is an implementation of extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. It includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart).

This package provides model-robust standard error estimators for cross-sectional, time series, clustered, panel, and longitudinal data.

This is a pure R implementation of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) with optional restarts (IPOP-CMA-ES).

Uniform manifold approximation and projection is a technique for dimension reduction. This package provides an interface to the UMAP algorithm in R, including a translation of the original algorithm into R.

Full 64-bit resolution date and time functionality with nanosecond granularity is provided, with easy transition to and from the standard POSIXct type. Three additional classes offer interval, period and duration functionality for nanosecond-resolution timestamps.

R's default conflict management system gives the most recently loaded package precedence. This can make it hard to detect conflicts, particularly when they arise because a package update creates ambiguity that did not previously exist. The conflicted package takes a different approach, making every conflict an error and forcing you to choose which function to use.

This package provides a collection of methods for smoothing numerical data, commencing with a port of the Matlab gaussian window smoothing function. In addition, several functions typically used in smoothing of financial data are included.

This package provides a fast dimensionality reduction method scalable to large numbers of samples. Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS, but rather than calculating a complete distance matrix between all pairs of samples, only the distances between a set of landmarks and the samples are calculated.

This package provides a dplyr back end for databases that allows you to work with remote database tables as if they are in-memory data frames. Basic features works with any database that has a DBI back end; more advanced features require SQL translation to be provided by the package author.

The extrafont package makes it easier to use fonts other than the basic PostScript fonts that R uses. Fonts that are imported into extrafont can be used with PDF or PostScript output files. There are two hurdles for using fonts in PDF (or Postscript) output files:

1. Making R aware of the font and the dimensions of the characters.

2. Embedding the fonts in the PDF file so that the PDF can be displayed properly on a device that doesn't have the font. This is usually needed if you want to print the PDF file or share it with others.

The extrafont package makes both of these things easier.

This package provides functions for estimating tolerance limits (intervals) for various univariate distributions (binomial, Cauchy, discrete Pareto, exponential, two-parameter exponential, extreme value, hypergeometric, Laplace, logistic, negative binomial, negative hypergeometric, normal, Pareto, Poisson-Lindley, Poisson, uniform, and Zipf-Mandelbrot), Bayesian normal tolerance limits, multivariate normal tolerance regions, nonparametric tolerance intervals, tolerance bands for regression settings (linear regression, nonlinear regression, nonparametric regression, and multivariate regression), and analysis of variance tolerance intervals. Visualizations are also available for most of these settings.

Deciding what resolution to use can be a difficult question when approaching a clustering analysis. One way to approach this problem is to look at how samples move as the number of clusters increases. This package allows you to produce clustering trees, a visualization for interrogating clusterings as resolution increases.

This package provides a collection of helper functions designed to help you to better understand object oriented programming in R, particularly using S3.

This package provides a programmatic deployment interface for RPubs, shinyapps.io, and RStudio Connect. Supported content types include R Markdown documents, Shiny applications, Plumber APIs, plots, and static web content.

Hapassoc performs likelihood inference of trait associations with haplotypes and other covariates in generalized linear models (GLMs). The functions are developed primarily for data collected in cohort or cross-sectional studies. They can accommodate uncertain haplotype phase and handle missing genotypes at some SNPs.

This package supports the analysis of count data exhibiting autoregressive properties, using the Autoregressive Conditional Poisson model (ACP(p,q)) proposed by Heinen (2003).

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 converts between GeoJSON and Simple Feature objects.

The spdlog library is a widely-used and very capable header-only C++ library for logging. This package includes its headers as an R package to permit other R packages to deploy it via a simple LinkingTo: RcppSpdlog. As of version 0.0.9, it also provides both simple R logging functions and compiled functions callable by other packages.

Rasterize only specific layers of a ggplot2 plot while simultaneously keeping all labels and text in vector format. This allows users to keep plots within the reasonable size limit without losing vector properties of the scale-sensitive information.