This package provides basic infrastructure for linear test statistics and permutation inference in the framework of Strasser and Weber (1999).

This package provides the Open Source Geometry Engine (GEOS) as a C API that can be used to write high-performance C and C++ geometry operations using R as an interface. Headers are provided to make linking to and using these functions from C++ code as easy and as safe as possible. This package contains an internal copy of the GEOS library to guarantee the best possible consistency on multiple platforms.

This package is intended to make it easy to create D3 JavaScript network, tree, dendrogram, and Sankey graphs from R using data frames.

R comes with a suite of utilities for linear algebra with "numeric" (double precision) vectors/matrices. However, sometimes single precision (or less!) is more than enough for a particular task. This package extends R's linear algebra facilities to include 32-bit float (single precision) data. Float vectors/matrices have half the precision of their "numeric"-type counterparts but are generally faster to numerically operate on, for a performance vs accuracy trade-off.

This package provides methods for species distribution modeling, i.e., predicting the environmental similarity of any site to that of the locations of known occurrences of a species.

This package provides flexible Bayesian estimation of IMIFA and related models, for nonparametrically clustering high-dimensional data. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, posterior predictive checking, and quantifying uncertainty.

As a successor of the packages BatchJobs and BatchExperiments, this package provides a parallel implementation of the Map function for high performance computing systems managed by various schedulers. A multicore and socket mode allow the parallelization on a local machines, and multiple machines can be hooked up via SSH to create a makeshift cluster. Moreover, the package provides an abstraction mechanism to define large-scale computer experiments in a well-organized and reproducible way.

This package provides tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). The area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.

This package provides a simple client package for the Amazon Web Services (AWS) Simple Storage Service (S3) REST API.

The DBI package provides a database interface (DBI) definition for communication between R and relational database management systems. All classes in this package are virtual and need to be extended by the various R/DBMS implementations.

This package provides gsubfn which is like gsub but can take a replacement function or certain other objects instead of the replacement string. Matches and back references are input to the replacement function and replaced by the function output. gsubfn can be used to split strings based on content rather than delimiters and for quasi-perl-style string interpolation. The package also has facilities for translating formulas to functions and allowing such formulas in function calls instead of functions.

Dunn's test computes stochastic dominance & reports pairwise comparisons. This is done following a Kruskal-Wallis test (Kruskal and Wallis, 1952). It employs Dunn's z-test-statistic approximations for rank statistics, conducting k(k-1)/2 comparisons. The null hypothesis assumes that the probability of a randomly selected value from the first group being larger than one from the second group is one half, similar to the Wilcoxon-Mann-Whitney test. Dunn's test serves as a test for median difference and takes into account tied ranks.

This package lets you standardize country names, convert them into one of 40 different coding schemes, convert between coding schemes, and assign region descriptors.

This package provides tools to parse simple .ini configuration files to an structured list. Users can manipulate this resulting list with lapply() functions. This same structured list can be used to write back to file after modifications.

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.

Download and install R packages stored in GitHub, BitBucket, or plain subversion or git repositories. This package is a lightweight replacement of the install_* functions in the devtools package. Indeed most of the code was copied over from devtools.

This is a package for slanted matrices and ordered clustering for better visualization of similarity data.

This package creates alluvial diagrams (also known as parallel sets plots) for multivariate and time series-like data.

This package lets you generate planar and spherical triangle meshes, compute finite element calculations for 1- and 2-dimensional flat and curved manifolds with associated basis function spaces, methods for lines and polygons, and transparent handling of coordinate reference systems and coordinate transformation, including sf and sp geometries. The core fmesher library code was originally part of the INLA package, and implements parts of "Triangulations and Applications" by Hjelle and Daehlen (2006) <doi:10.1007/3-540-33261-8>.

This package provides a suite of methods for powerful and robust microbiome data analysis, including data normalization, data simulation, community-level association testing and differential abundance analysis. It implements generalized UniFrac distances, Geometric Mean of Pairwise Ratios (GMPR) normalization, semiparametric data simulator, distance-based statistical methods, and feature- based statistical methods. The distance-based statistical methods include three extensions of PERMANOVA:

• PERMANOVA using the Freedman-Lane permutation scheme,

• PERMANOVA omnibus test using multiple matrices, and

• analytical approach to approximating PERMANOVA p-value.

Feature-based statistical methods include linear model-based methods for differential abundance analysis of zero-inflated high-dimensional compositional data.

This package provides functions to compare a model object to a comparison object. If the objects are not identical, the functions can be instructed to explore various modifications of the objects (e.g., sorting rows, dropping names) to see if the modified versions are identical.

This package provides an efficient algorithm for solving ultra-sparse regularized regression models using a variational Bayes algorithm with a spike prior. The algorithm is solved on a path, with coordinate updates, and is capable of generating very sparse models. Very general model diagnostics for controlling type-1 errors are also provided.

This package performs projection predictive feature selection for generalized linear models and generalized linear and additive multilevel models. The package is compatible with the rstanarm and brms packages, but other reference models can also be used. See the package vignette for more information and examples.

This package provides fast machine learning algorithms including matrix factorization and divisive clustering for large sparse and dense matrices.