Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.
API method:
GET /api/packages?search=hello&page=1&limit=20
where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned
in response headers.
If you'd like to join our channel webring send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.
This package provides a collection of miscellaneous statistical functions for:
probability distributions,
probability density estimation,
most frequent value estimation,
other statistical measures of location,
construction of histograms,
calculation of the Hellinger distance,
use of classical kernels, and
univariate piecewise-constant regression.
Various utilities for evaluating continued fractions.
Convert a logical vector or a vector of p-values or a correlation, difference, or distance matrix into a display identifying the pairs for which the differences were not significantly different.
Recipes is an extensible framework to create and preprocess design matrices. Recipes consist of one or more data manipulation and analysis "steps". Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting design matrices can then be used as inputs into statistical or machine learning models.
This package provides an interface to Amazon Web Services storage services, including Simple Storage Service (S3).
Written in C++ using Rcpp, this package provides a collection of metrics for evaluating models.
This package provides a set of functions for sparse matrix algebra. Differences with other sparse matrix packages are:
it only supports (essentially) one sparse matrix format;
it is based on transparent and simple structure(s);
it is tailored for MCMC calculations within G(M)RF;
and it is fast and scalable (with the extension package
spam64).
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.
This package provides colour choice in information visualisation. It important in order to avoid being mislead by inherent bias in the used colour palette. This package provides access to the perceptually uniform and colour-blindness friendly palettes developed by Fabio Crameri and released under the "Scientific Colour-Maps" moniker. The package contains 24 different palettes and includes both diverging and sequential types.
This package provides functions and datasets for bootstrapping from the book "Bootstrap Methods and Their Application" by A.C. Davison and D.V. Hinkley (1997, CUP), originally written by Angelo Canty for S.
This package provides a graph implementation that can be thought of as two tidy data frames describing node and edge data respectively. It provides an approach to manipulate these two virtual data frames using the API defined in the dplyr package, and it also provides tidy interfaces to a lot of common graph algorithms.
This package provides functions and data to construct technical trading rules with R.
This package provides an SCSS compiler, powered by the libsass library. With this, R developers can use variables, inheritance, and functions to generate dynamic style sheets. The package uses the Sass CSS extension language, which is stable, powerful, and CSS compatible.
This package exposes R bindings to jsTree, a JavaScript library that supports interactive trees, to enable rich, editable trees in Shiny.
This package lets you expand factors, characters and other eligible classes into dummy/indicator variables.
User-facing R functions are provided to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the StanHeaders package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough Bayesian inference via 'variational' approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives.
This package provides high performance container data types such as queues, stacks, deques, dicts and ordered dicts.
This package provides vector map data from https://www.naturalearthdata.com/. Access functions are provided in the accompanying package rnaturalearth.
This package contains genomic data for the plant pathogen Phytophthora infestans. It includes a variant file, a sequence file and an annotation file. This package is intended to be used as example data for packages that work with genomic data.
This package provides tools to integrate nucleotide sequencing data (variant call format, e.g. VCF or BCF) or meta-analysis results in R.
This package provides a collection of templates to author preregistration documents for scientific studies in PDF format.
This package provides functions for working with the scrypt key derivation functions. Scrypt is a password-based key derivation function created by Colin Percival. The algorithm was specifically designed to make it costly to perform large-scale custom hardware attacks by requiring large amounts of memory.
This package provides the dyn class interfaces ts, irts, zoo and zooreg time series classes to lm, glm, loess, quantreg::rq, MASS::rlm, MCMCpack::MCMCregress(), quantreg::rq(), randomForest::randomForest() and other regression functions, allowing those functions to be used with time series including specifications that may contain lags, diffs and missing values.
This package implements affinity propagation clustering introduced by Frey and Dueck (2007). The package further provides leveraged affinity propagation and an algorithm for exemplar-based agglomerative clustering that can also be used to join clusters obtained from affinity propagation. Various plotting functions are available for analyzing clustering results.