It is sometimes useful to perform a computation in a separate R process, without affecting the current R process at all. This package does exactly that.

This package provides methods for variable selection for AFT models.

This package reads and writes data files like CSV, TSV and FWF. When reading it uses a quick initial indexing step, then reads the values lazily, so only the data you actually use needs to be read. The writer formats the data in parallel and writes to disk asynchronously from formatting.

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 the ggplot binning layer stat_summaries_hex(), which functions similar to its singular form, but allows the use of multiple statistics per bin. Those statistics can be mapped to multiple bin aesthetics.

This package provides an integration of base and grid graphics for R.

This package lets you construct paths to your project's files. Use the here function as a drop-in replacement for file.path, it will always locate the files relative to your project root.

Ggfittext is a ggplot2 extension for fitting text into boxes.

This package provides utilities for working with Google APIs. This includes functions and classes for handling common credential types and for preparing, executing, and processing HTTP requests.

This package provides a simple and light-weight API for memory profiling of R expressions. The profiling is built on top of R's built-in memory profiler utils::Rprofmem(), which records every memory allocation done by R (also native code).

This package provides functions for kriging and point pattern analysis.

The main function biclust() provides several algorithms to find biclusters in two-dimensional data, spectral, plaid model, xmotifs, and bimax. In addition, the package provides methods for data preprocessing (normalization and discretization), visualization, and validation of bicluster solutions.

This package provides an interface to the C implementation of the random number generator with multiple independent streams developed by L'Ecuyer et al (2002). The main purpose of this package is to enable the use of this random number generator in parallel R applications.

This package provides functions, documentation and example data to help divide geographic space into discrete polygons (zones). The functions are motivated by research into the merits of different zoning systems. A flexible ClockBoard zoning system is provided, which breaks-up space by concentric rings and radial lines emanating from a central point.

Convert a logical vector or a vector of p-values or a correlation, difference, or distance matrix into a display identifying the pairs for which the differences were not significantly different.

This package provides David Scott's ASH routines ported from S-PLUS to R.

This package contains functions for creating various types of summary tables, e.g. comparing characteristics across levels of a categorical variable and summarizing fitted generalized linear models, generalized estimating equations, and Cox proportional hazards models. Functions are available to handle data from simple random samples as well as complex surveys.

This package contains methods for the detection of clusters in hierarchical clustering dendrograms.

This package provides Gaussian mixture models, k-means, mini-batch-kmeans, k-medoids and affinity propagation clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of RcppArmadillo to speed up the computationally intensive parts of the functions. For more information, see

1. "Clustering in an Object-Oriented Environment" by Anja Struyf, Mia Hubert, Peter Rousseeuw (1997), Journal of Statistical Software, https://doi.org/10.18637/jss.v001.i04;

2. "Web-scale k-means clustering" by D. Sculley (2010), ACM Digital Library, https://doi.org/10.1145/1772690.1772862;

3. "Armadillo: a template-based C++ library for linear algebra" by Sanderson et al (2016), The Journal of Open Source Software, https://doi.org/10.21105/joss.00026;

4. "Clustering by Passing Messages Between Data Points" by Brendan J. Frey and Delbert Dueck, Science 16 Feb 2007: Vol. 315, Issue 5814, pp. 972-976, https://doi.org/10.1126/science.1136800.

This package provides tools to combine multidimensional arrays into a single array. This is a generalization of cbind and rbind. It works with vectors, matrices, and higher-dimensional arrays. It also provides the functions adrop, asub, and afill for manipulating, extracting and replacing data in arrays.

A workflow is an object that can bundle together your pre-processing, modeling, and post-processing requests. For example, if you have a recipe and parsnip model, these can be combined into a workflow. The advantages are:

1. You don’t have to keep track of separate objects in your workspace.

2. The recipe prepping and model fitting can be executed using a single call to fit().

3. If you have custom tuning parameter settings, these can be defined using a simpler interface when combined with tune.

4. In the future, workflows will be able to add post-processing operations, such as modifying the probability cutoff for two-class models.

This package provides pre-fit and post-fit methods for detecting separation and infinite maximum likelihood estimates in generalized linear models with categorical responses. The pre-fit methods apply on binomial-response generalized liner models such as logit, probit and cloglog regression, and can be directly supplied as fitting methods to the glm() function. The post-fit methods apply to models with categorical responses, including binomial-response generalized linear models and multinomial-response models, such as baseline category logits and adjacent category logits models; for example, the models implemented in the brglm2 package. The post-fit methods successively refit the model with increasing number of iteratively reweighted least squares iterations, and monitor the ratio of the estimated standard error for each parameter to what it has been in the first iteration.

This package provides tools to identify global ("unknown" or "free") objects in R expressions by code inspection using various strategies, e.g. conservative or liberal. The objective of this package is to make it as simple as possible to identify global objects for the purpose of exporting them in distributed compute environments.

The brew package implements a templating framework for mixing text and R code for report generation. The template syntax is similar to PHP, Ruby's erb module, Java Server Pages, and Python's psp module.