This a package containing diverse spatial datasets for demonstrating, benchmarking and teaching spatial data analysis. It includes R data of class sf, Spatial, and nb. It also contains data stored in a range of file formats including GeoJSON, ESRI Shapefile and GeoPackage. Some of the datasets are designed to illustrate specific analysis techniques. cycle_hire() and cycle_hire_osm(), for example, are designed to illustrate point pattern analysis techniques.

This package extends the functionality of ggplot2, providing the capability to plot ternary diagrams for (a subset of) the ggplot2 geometries. Additionally, ggtern has implemented several new geometries which are unavailable to the standard ggplot2 release.

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 lets you build complex Structured Query Language (SQL) queries dynamically. Classes and/or factory functions are used to produce a syntax tree from which the final character string is generated. Strings and identifiers are automatically quoted using the right quotes, using either American National Standards Institute (ANSI) quoting or the quoting style of an existing database connector. Style can be configured to set uppercase/lowercase for keywords, remove unnecessary spaces, or omit optional keywords.

The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at http://dmg.org/. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products.

This package provides a model agnostic tool for decomposition of predictions from black boxes. It supports additive attributions and attributions with interactions. The Break Down Table shows contributions of every variable to a final prediction. The Break Down Plot presents variable contributions in a concise graphical way. This package works for classification and regression models.

This package provides a set of R bindings for the Selenium 2.0 WebDriver (see https://selenium.dev/documentation/en/ for more information) using the JsonWireProtocol (see https://github.com/SeleniumHQ/selenium/wiki/JsonWireProtocol for more information). Selenium 2.0 WebDriver allows driving a web browser natively as a user would either locally or on a remote machine using the Selenium server it marks a leap forward in terms of web browser automation. Selenium automates web browsers (commonly referred to as browsers). Using RSelenium you can automate browsers locally or remotely.

The Ziggurat pseudo-random number generator (or PRNG) offers a lightweight and very fast PRNG for the normal, exponential, and uniform distributions. It is provided here in a small zero-dependency package. It can be used from R as well as from C/C++ code in other packages as is demonstrated by four included sample packages using four distinct methods to use the PRNG presented here in client package.

This package offers methods to perform asymptotically bias-corrected regularized linear discriminant analysis (ABC_RLDA) for cost-sensitive binary classification. The bias-correction is an estimate of the bias term added to regularized discriminant analysis that minimizes the overall risk.

This package implements reinforcement learning environments and algorithms as described in Sutton & Barto (1998). The Q-Learning algorithm can be used with function approximation, eligibility traces (Singh & Sutton, 1996) and experience replay (Mnih et al., 2013).

This package provides tools for creating detailed dataframes for common statistical approaches and tests. These include parametric, nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses. The functions are pipe-friendly and provide a consistent syntax to work with tidy data. These dataframes additionally contain expressions with statistical details, and can be used in graphing packages. This package also forms the statistical processing backend for ggstatsplot.

This package provides an easy way to determine which directories on the user's computer should be used to save data, caches and logs. It is a port of Python's Appdirs to R.

Strex is a collection of string manipulation functions not provided by the stringi or stringr packages. The foremost of these is the extraction of numbers from strings. There are many other handy functionalities in strex.

This package provides tools to infer the code style (which style rules are followed and which ones are not) from one package and use it to check another. This makes it easier to find and correct the most important problems first.

This package provides useful tools to pry back the covers of R and understand the language at a deeper level.

This package generates graphics with embedded details from statistical tests. Statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous or categorical data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses.

This package provides the cumulative distribution function (CDF), quantile, and statistical power calculator for a collection of thresholding Fisher's p-value combination methods, including Fisher's p-value combination method, truncated product method and, in particular, soft-thresholding Fisher's p-value combination method which is proven to be optimal in some context of signal detection. The p-value calculator for the omnibus version of these tests are also included.

This package provides datasets to accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage.

This package lets you construct Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Results Data objects. These objects are used and re-used to construct summary tables, visualizations, and written reports. The package also exports utilities for working with these objects and creating new Analysis Results Data objects.

This package provides a general framework for high-dimensional undirected graph estimation. It integrates data preprocessing, neighborhood screening, graph estimation, and model selection techniques into a pipeline.

The Rcpp package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about Rcpp is provided by several vignettes included in this package, via the Rcpp Gallery site at <http://gallery.rcpp.org>, the paper by Eddelbuettel and Francois (2011, JSS), and the book by Eddelbuettel (2013, Springer); see citation("Rcpp") for details on these last two.

Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 <doi:10.1111/ecog.00566>), and Markov random field models on irregular grids (as considered in the INLA package, <https://www.r-inla.org>), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) are also implemented.

This package provides utilities for processing and analyzing the files that are exported from a recorded Zoom meeting. This includes analyzing data captured through video cameras and microphones, the text-based chat, and meta-data. You can analyze aspects of the conversation among meeting participants and their emotional expressions throughout the meeting.

This package provides vector map data from https://www.naturalearthdata.com/. Access functions are provided in the accompanying package rnaturalearth.