Examples: visualization, C++, networks, data cleaning, html widgets, ropensci.

Found 1169 packages in 0.03 seconds

sfnetworks — by Lucas van der Meer, 4 months ago

Tidy Geospatial Networks

Provides a tidy approach to spatial network analysis, in the form of classes and functions that enable a seamless interaction between the network analysis package 'tidygraph' and the spatial analysis package 'sf'.

geodist — by Mark Padgham, a year ago

Fast, Dependency-Free Geodesic Distance Calculations

Dependency-free, ultra fast calculation of geodesic distances. Includes the reference nanometre-accuracy geodesic distances of Karney (2013) , as used by the 'sf' package, as well as Haversine and Vincenty distances. Default distance measure is the "Mapbox cheap ruler" which is generally more accurate than Haversine or Vincenty for distances out to a few hundred kilometres, and is considerably faster. The main function accepts one or two inputs in almost any generic rectangular form, and returns either matrices of pairwise distances, or vectors of sequential distances.

mvnfast — by Matteo Fasiolo, 4 years ago

Fast Multivariate Normal and Student's t Methods

Provides computationally efficient tools related to the multivariate normal and Student's t distributions. The main functionalities are: simulating multivariate random vectors, evaluating multivariate normal or Student's t densities and Mahalanobis distances. These tools are very efficient thanks to the use of C++ code and of the OpenMP API.

shiny — by Carson Sievert, 3 months ago

Web Application Framework for R

Makes it incredibly easy to build interactive web applications with R. Automatic "reactive" binding between inputs and outputs and extensive prebuilt widgets make it possible to build beautiful, responsive, and powerful applications with minimal effort.

nnls — by Katharine Mullen, 2 years ago

The Lawson-Hanson Algorithm for Non-Negative Least Squares (NNLS)

An R interface to the Lawson-Hanson implementation of an algorithm for non-negative least squares (NNLS). Also allows the combination of non-negative and non-positive constraints.

phylolm — by Lam Si Tung Ho, 2 years ago

Phylogenetic Linear Regression

Provides functions for fitting phylogenetic linear models and phylogenetic generalized linear models. The computation uses an algorithm that is linear in the number of tips in the tree. The package also provides functions for simulating continuous or binary traits along the tree. Other tools include functions to test the adequacy of a population tree.

reclin2 — by Jan van der Laan, a year ago

Record Linkage Toolkit

Functions to assist in performing probabilistic record linkage and deduplication: generating pairs, comparing records, em-algorithm for estimating m- and u-probabilities (I. Fellegi & A. Sunter (1969) , T.N. Herzog, F.J. Scheuren, & W.E. Winkler (2007), "Data Quality and Record Linkage Techniques", ISBN:978-0-387-69502-0), forcing one-to-one matching. Can also be used for pre- and post-processing for machine learning methods for record linkage. Focus is on memory, CPU performance and flexibility.

mark — by Jordan Mark Barbone, a year ago

Miscellaneous, Analytic R Kernels

Miscellaneous functions and wrappers for development in other packages created, maintained by Jordan Mark Barbone.

compositions — by K. Gerald van den Boogaart, a year ago

Compositional Data Analysis

Provides functions for the consistent analysis of compositional data (e.g. portions of substances) and positive numbers (e.g. concentrations) in the way proposed by J. Aitchison and V. Pawlowsky-Glahn.

rstan — by Ben Goodrich, a year ago

R Interface to Stan

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.