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

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errorlocate — by Edwin de Jonge, 6 months ago

Locate Errors with Validation Rules

Errors in data can be located and removed using validation rules from package 'validate'. See also Van der Loo and De Jonge (2018) , chapter 7.

validatetools — by Edwin de Jonge, 6 months ago

Checking and Simplifying Validation Rule Sets

Rule sets with validation rules may contain redundancies or contradictions. Functions for finding redundancies and problematic rules are provided, given a set a rules formulated with 'validate'.

loo — by Jonah Gabry, a month ago

Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models

Efficient approximate leave-one-out cross-validation (LOO) for Bayesian models fit using Markov chain Monte Carlo, as described in Vehtari, Gelman, and Gabry (2017) . The approximation uses Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. As a byproduct of the calculations, we also obtain approximate standard errors for estimated predictive errors and for the comparison of predictive errors between models. The package also provides methods for using stacking and other model weighting techniques to average Bayesian predictive distributions.

Rtsne — by Jesse Krijthe, 4 months ago

T-Distributed Stochastic Neighbor Embedding using a Barnes-Hut Implementation

An R wrapper around the fast T-distributed Stochastic Neighbor Embedding implementation by Van der Maaten (see < https://github.com/lvdmaaten/bhtsne/> for more information on the original implementation).

ggplot2 — by Thomas Lin Pedersen, a month ago

Create Elegant Data Visualisations Using the Grammar of Graphics

A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.

SuperLearner — by Eric Polley, a month ago

Super Learner Prediction

Implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner.

mice — by Stef van Buuren, 10 months ago

Multivariate Imputation by Chained Equations

Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) . Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.

fst — by Mark Klik, 2 years ago

Lightning Fast Serialization of Data Frames

Multithreaded serialization of compressed data frames using the 'fst' format. The 'fst' format allows for full random access of stored data and a wide range of compression settings using the LZ4 and ZSTD compressors.

covr — by Jim Hester, 5 months ago

Test Coverage for Packages

Track and report code coverage for your package and (optionally) upload the results to a coverage service like 'Codecov' < https://about.codecov.io> or 'Coveralls' < https://coveralls.io>. Code coverage is a measure of the amount of code being exercised by a set of tests. It is an indirect measure of test quality and completeness. This package is compatible with any testing methodology or framework and tracks coverage of both R code and compiled C/C++/FORTRAN code.

rmarkdown — by Yihui Xie, 24 days ago

Dynamic Documents for R

Convert R Markdown documents into a variety of formats.