awesome-r

An online version of the Advanced R book.

Task Views for CRAN packages.

An online home of the O’Reilly book: Efficient R Programming.

Regularization for semiparametric additive hazards regression.

Tidy Anomaly Detection using Twitter's AnomalyDetection method.

AnomalyDetection R package from Twitter.

Mining Association Rules and Frequent Itemsets

Big Random Forests: Classification and Regression Forests for Large Data Sets

Generalized Ridge Regression (with special advantage for p >> n cases)

A simple way to produce animated graphics in R, using

R graphics device using cairo graphics library for creating high-quality display output.

A graphical display of a correlation matrix or general matrix. It also contains some algorithms to do matrix reordering.

visualizing, adjusting and comparing trees of hierarchical clustering.

Tools for using fonts in R graphics.

Extra Coordinate Systems, Geoms and Statistical Transformations for ggplot2.

A very good introductory text on R, also covers some advanced topics.

Basic analytical skills for all sorts of data in R.

CRAN Contributed Documentation in many languages.

A simplified and "operational" version of

Learning R as a programming language from basics to advanced topics.

Analyses of Phylogenetics and Evolution.

Tools for the analysis and comprehension of high-throughput genomic data.

An integrated package for genetic data analysis of both population and family data.

Classes and methods for handling genetic data.

Generalized mixed-effects models, handling user-specified matrix of residual covariance, relevant for the anaysis of repeated observations in longitudinal trials.

Generalized mixed-effects models.

High performance computing with LSF, TORQUE, Slurm, OpenLava, SGE and Docker Swarm.

Provides distributed data structures and simplifies distributed computing in R.

A scalable high-performance platform from HP Vertica Analytics Team.

Executing the loop in parallel.

A minimal, efficient, cross-platform unified Future API for parallel and distributed processing in R; designed for beginners as well as advanced developers.

Shared memory and memory-mapped matrices. The big* packages provide additional tools including linear models (

Convert statistical analysis objects into tidy data frames.

Fast exploratory data analysis with minimum code.

Fast data manipulation in a short and flexible syntax.

Fast data frames manipulation and database query.

Data structures designed to store large datasets.