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.

'Drat' R Archive Template

Creation and use of R Repositories via helper functions to insert packages into a repository, and to add repository information to the current R session. Two primary types of repositories are support: gh-pages at GitHub, as well as local repositories on either the same machine or a local network. Drat is a recursive acronym: Drat R Archive Template.

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'.

Trends and Indices for Monitoring Data

The TRIM model is widely used for estimating growth and decline of animal populations based on (possibly sparsely available) count data. The current package is a reimplementation of the original TRIM software developed at Statistics Netherlands by Jeroen Pannekoek. See < https://www.cbs.nl/en-gb/society/nature-and-environment/indices-and-trends%2d%2dtrim%2d%2d> for more information about TRIM.

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.

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.

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).

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.

Super Learner Prediction

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

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.