This package provides tools to compares k samples using the Anderson-Darling test, Kruskal-Wallis type tests with different rank score criteria, Steel's multiple comparison test, and the Jonckheere-Terpstra (JT) test. It computes asymptotic, simulated or (limited) exact P-values, all valid under randomization, with or without ties, or conditionally under random sampling from populations, given the observed tie pattern. Except for Steel's test and the JT test it also combines these tests across several blocks of samples.

Functions implemented in this package allow coercing (i.e. convert) network data between classes provided by other R packages. Currently supported classes are those defined in packages network and igraph.

This package provides classes and methods for spatial data; the classes document where the spatial location information resides, for 2D or 3D data. Utility functions are provided, e.g. for plotting data as maps, spatial selection, as well as methods for retrieving coordinates, for subsetting, print, summary, etc.

This package provides tools to estimate tail area-based false discovery rates as well as local false discovery rates for a variety of null models (p-values, z-scores, correlation coefficients, t-scores). The proportion of null values and the parameters of the null distribution are adaptively estimated from the data. In addition, the package contains functions for non-parametric density estimation (Grenander estimator), for monotone regression (isotonic regression and antitonic regression with weights), for computing the greatest convex minorant (GCM) and the least concave majorant (LCM), for the half-normal and correlation distributions, and for computing empirical higher criticism (HC) scores and the corresponding decision threshold.

This package provides a consistent, flexible and easy to use tool to parse and convert strings into cases like snake or camel among others.

This package provides a collection of functions to visualize spatial data and models on top of static maps from various online sources (e.g Google Maps and Stamen Maps). It includes tools common to those tasks, including functions for geolocation and routing.

This package provides JSON parsing capability through the Rapidjson library.

Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via the Template Model Builder. Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation.

This package contains linear and nonlinear regression methods based on partial least squares and penalization techniques. Model parameters are selected via cross-validation, and confidence intervals ans tests for the regression coefficients can be conducted via jackknifing.

Cyclomatic complexity is a software metric, used to indicate the complexity of a program. It is a quantitative measure of the number of linearly independent paths through a program's source code. This package provides tools to compute this metric.

This package provides functions for creating plots and image files in a unified way regardless of output format (EPS, PDF, PNG, SVG, TIFF, WMF, etc.). Default device options as well as scales and aspect ratios are controlled in a uniform way across all device types. Switching output format requires minimal changes in code. This package is ideal for large-scale batch processing, because it will never leave open graphics devices or incomplete image files behind, even on errors or user interrupts.

This package provides functions to deal with binary classification problems in the presence of imbalanced classes. Synthetic balanced samples are generated according to ROSE (Menardi and Torelli, 2013). Functions that implement more traditional remedies to the class imbalance are also provided, as well as different metrics to evaluate a learner accuracy. These are estimated by holdout, bootstrap or cross-validation methods.

This package provides several cluster-robust variance estimators (i.e., sandwich estimators) for ordinary and weighted least squares linear regression models, including the bias-reduced linearization estimator introduced by Bell and McCaffrey (2002) http://www.statcan.gc.ca/pub/12-001-x/2002002/article/9058-eng.pdf and developed further by Pustejovsky and Tipton (2017) doi:10.1080/07350015.2016.1247004. The package includes functions for estimating the variance- covariance matrix and for testing single- and multiple-contrast hypotheses based on Wald test statistics. Tests of single regression coefficients use Satterthwaite or saddle-point corrections. Tests of multiple-contrast hypotheses use an approximation to Hotelling's T-squared distribution. Methods are provided for a variety of fitted models, including lm() and mlm objects, glm(), ivreg (from package AER), plm() (from package plm), gls() and lme() (from nlme), robu() (from robumeta), and rma.uni() and rma.mv() (from metafor).

Similarly to Schafer's package pan, jomo is a package for multilevel joint modelling multiple imputation http://doi.org/10.1002/9781119942283. Novel aspects of jomo are the possibility of handling binary and categorical data through latent normal variables, the option to use cluster-specific covariance matrices and to impute compatibly with the substantive model.

The httpuv package provides low-level socket and protocol support for handling HTTP and WebSocket requests directly from within R. It is primarily intended as a building block for other packages, rather than making it particularly easy to create complete web applications using httpuv alone.

This package provides Cramer-Von Mises and Anderson-Darling tests of goodness-of-fit for continuous univariate distributions, using efficient algorithms.

This package provides other packages with access to the internal R serialization code. Access to this code is provided at the C function level by using the registration of native function mechanism. Client packages simply include a single header file RApiSerializeAPI.h provided by this package.

This package provides a complete environment for Bayesian inference using a variety of different samplers.

Run R CMD check from R programmatically, and capture the results of the individual checks.

Partition a data frame across multiple worker processes to provide simple multicore parallelism.

This package provides a graphics device for R that is accessible via network protocols. This package was created to make it easier to embed live R graphics in integrated development environments and other applications. The included HTML/JavaScript client (plot viewer) aims to provide a better overall user experience when dealing with R graphics. The device asynchronously serves graphics via HTTP and WebSockets'.

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

This package implements general purpose tools, such as functions for sampling and basic manipulation of Brazilian lawsuits identification number. It also implements functions for text cleaning, such as accentuation removal.

This package provides distance-based parametric bootstrap tests for clustering with spatial neighborhood information. It implements some distance measures, clustering of presence-absence, abundance and multilocus genetical data for species delimitation, nearest neighbor based noise detection.