httpcode provides functionality for finding and explaining the meaning of HTTP status codes. Functions are included for searching for codes by full or partial number, by message, and to get appropriate dog and cat images for many status codes.

This package provides an R interface to the Lawson-Hanson implementation of an algorithm for non-negative least squares (NNLS). It also allows the combination of non-negative and non-positive constraints.

This package provides MathJax and macros to enable its use within Rd files for rendering equations in the HTML help files.

This package provides functionality for creating Quantile-Quantile (QQ) and Probability-Probability (PP) plots with simultaneous testing bands to asses significance of sample deviation from a reference distribution.

This package provides tools for the calibration of penalized criteria for model selection. The calibration methods available are based on the slope heuristics.

This package provides a set of simple functions that transforms longitudinal data to estimate the cosinor linear model as described in Tong (1976). Methods are given to summarize the mean, amplitude and acrophase, to predict the mean annual outcome value, and to test the coefficients.

This package provides functions for feed-forward neural networks with a single hidden layer, and for multinomial log-linear models.

This package provides unit root and cointegration tests encountered in applied econometric analysis.

This package provides functions for kernel-regression-based association tests including Burden test, SKAT and SKAT-O. These methods aggregate individual SNP score statistics in a SNP set and efficiently compute SNP-set level p-values.

This package provides a minimal R and C++ API for parsing well-known binary and well-known text representation of geometries to and from R-native formats. Well-known binary is compact and fast to parse; well-known text is human-readable and is useful for writing tests. These formats are only useful in R if the information they contain can be accessed in R, for which high-performance functions are provided here.

This package provides a collection of R functions to perform nonparametric analysis of covariance for regression curves or surfaces. Testing the equality or parallelism of nonparametric curves or surfaces is equivalent to analysis of variance (ANOVA) or analysis of covariance (ANCOVA) for one-sample functional data. Three different testing methods are available in the package, including one based on L-2 distance, one based on an ANOVA statistic, and one based on variance estimators.

This is a port of the type guesser from the readr package, the so-called readr first edition parsing engine, now superseded by vroom.

This package provides a set of predicates and assertions for checking the properties of UK-specific complex data types. This is mainly for use by other package developers who want to include run-time testing features in their own packages.

This package serves two purposes:

1. Provide a comfortable R interface to query the Google server for static maps, and

2. Use the map as a background image to overlay plots within R. This requires proper coordinate scaling.

This package provides the random ferns classifier by Ozuysal, Calonder, Lepetit and Fua (2009) <doi:10.1109/TPAMI.2009.23>, modified for generic and multi-label classification and featuring OOB error approximation and importance measure as introduced in Kursa (2014) <doi:10.18637/jss.v061.i10>.

This package provides tools to compute Gower's distance (or similarity) coefficient between records, and to compute the top-n matches between records. Core algorithms are executed in parallel on systems supporting OpenMP.

This is a collection of tools for assessment of feature importance and feature effects. Key functions are:

• feature_importance() for assessment of global level feature importance,

• ceteris_paribus() for calculation of the what-if plots,

• partial_dependence() for partial dependence plots,

• conditional_dependence() for conditional dependence plots,

• accumulated_dependence() for accumulated local effects plots,

• aggregate_profiles() and cluster_profiles() for aggregation of ceteris paribus profiles,

• generic print() and plot() for better usability of selected explainers,

• generic plotD3() for interactive, D3 based explanations, and

• generic describe() for explanations in natural language.

This package contains a collection of functions to deal with nonparametric measurement error problems using deconvolution kernel methods. We focus two measurement error models in the package: (1) an additive measurement error model, where the goal is to estimate the density or distribution function from contaminated data; (2) nonparametric regression model with errors-in-variables. The R functions allow the measurement errors to be either homoscedastic or heteroscedastic. To make the deconvolution estimators computationally more efficient in R, we adapt the "Fast Fourier Transform" (FFT) algorithm for density estimation with error-free data to the deconvolution kernel estimation. Several methods for the selection of the data-driven smoothing parameter are also provided in the package. See details in: Wang, X.F. and Wang, B. (2011). Deconvolution estimation in measurement error models: The R package decon. Journal of Statistical Software, 39(10), 1-24.

Computes local polynomial estimators for the regression and also density. It comprises several different utilities to handle kernel estimators.

This package offers an easy to use way to draw a Venn diagram with ggplot2.

This package helps you create simple maps; add sub-plots like pie plots to a map or any other plot; format, plot and export gridded data. The package was developed for displaying fisheries data but most functions can be used for more generic data visualisation.

This package provides interpretability methods to analyze the behavior and predictions of any machine learning model. Implemented methods are:

• Feature importance described by Fisher et al. (2018),

• accumulated local effects plots described by Apley (2018),

• partial dependence plots described by Friedman (2001),

• individual conditional expectation ('ice') plots described by Goldstein et al. (2013) https://doi.org/10.1080/10618600.2014.907095,

• local models (variant of 'lime') described by Ribeiro et. al (2016),

• the Shapley Value described by Strumbelj et. al (2014) https://doi.org/10.1007/s10115-013-0679-x,

• feature interactions described by Friedman et. al https://doi.org/10.1214/07-AOAS148 and tree surrogate models.

Tools for working with and comparing sets of points and intervals.

This package provides implementations of apply(), eapply(), lapply(), Map(), mapply(), replicate(), sapply(), tapply(), and vapply() that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.