Inspired by the the futile.logger R package and logging Python module, this utility provides a flexible and extensible way of formatting and delivering log messages with low overhead.

This package provides a collection of tools to streamline the process of fitting elastic net models with glmnet. In addition to providing a formula interface, it also features a function cva.glmnet to do crossvalidation for both α and λ, as well as some utility functions.

The lpSolveAPI package provides an R interface to lp_solve, a MILP, solver with support for pure linear, (mixed) integer/binary, semi-continuous and SOS models.

This package provides an interface to Amazon Web Services, including storage, database, and compute services, such as Simple Storage Service (S3), DynamoDB NoSQL database, and Lambda functions-as-a-service.

This package provides a small collection of interesting and educational machine learning data sets which are used as examples in the mlr3 book Applied machine learning using mlr3 in R https://mlr3book.mlr-org.com, the use case gallery https://mlr3gallery.mlr-org.com, or in other examples. All data sets are properly preprocessed and ready to be analyzed by most machine learning algorithms. Data sets are automatically added to the dictionary of tasks if mlr3 is loaded.

This package provides a set of Shiny apps for effective communication and understanding in statistics. The current version includes properties of normal distribution, properties of sampling distribution, one-sample z and t tests, two samples independent (unpaired) t test and analysis of variance.

This package provides an Rstudio add-in that delivers a graphical interface for editing ggplot2 theme elements.

This package provides tools to create interactive tutorials using R Markdown. Use a combination of narrative, figures, videos, exercises, and quizzes to create self-paced tutorials for learning about R and R packages.

This package contains a number of common astronomy conversion routines, particularly the HMS and degrees schemes, which can be fiddly to convert between on mass due to the textural nature of the former. It allows users to coordinate match datasets quickly. It also contains functions for various cosmological calculations.

This package extends the out of memory vectors of ff with statistical functions and other utilities to ease their usage.

This package provides more controls on the option values such as validation and filtering on the values, making options invisible or private.

This package provides statistical models of biased sampling in the form of univariate and multivariate noncentral hypergeometric distributions, including Wallenius' noncentral hypergeometric distribution and Fisher's noncentral hypergeometric distribution (also called extended hypergeometric distribution).

This package provides support for simple features, a standardized way to encode spatial vector data. It binds to GDAL for reading and writing data, to GEOS for geometrical operations, and to PROJ for projection conversions and datum transformations.

This package provides tools to create some specific Space-Filling Design (SFD) and to test their quality.

This package provides a set of functions for data manipulation with list objects, including mapping, filtering, grouping, sorting, updating, searching, and other useful functions. Most functions are designed to be pipeline friendly so that data processing with lists can be chained.

This package infers directional Conservative causal core (gene) networks (C3NET). This is a version of the algorithm C3NET with directional network.

Estimate a suite of normalizing transformations, including a new adaptation of a technique based on ranks which can guarantee normally distributed transformed data if there are no ties: ordered quantile normalization (ORQ). ORQ normalization combines a rank-mapping approach with a shifted logit approximation that allows the transformation to work on data outside the original domain. It is also able to handle new data within the original domain via linear interpolation. The package is built to estimate the best normalizing transformation for a vector consistently and accurately. It implements the Box-Cox transformation, the Yeo-Johnson transformation, three types of Lambert WxF transformations, and the ordered quantile normalization transformation. It estimates the normalization efficacy of other commonly used transformations, and it allows users to specify custom transformations or normalization statistics. Finally, functionality can be integrated into a machine learning workflow via recipes.

Hapassoc performs likelihood inference of trait associations with haplotypes and other covariates in generalized linear models (GLMs). The functions are developed primarily for data collected in cohort or cross-sectional studies. They can accommodate uncertain haplotype phase and handle missing genotypes at some SNPs.

This package provides a drop-in replacement for rasterize from the raster package that takes sf-type objects, and is much faster. There is support for the main options provided by the rasterize function, including setting the field used and background value, and options for aggregating multi-layer rasters.

This is software accompanying the book 'Applied Smoothing Techniques for Data Analysis---The Kernel Approach with S-Plus Illustrations', Oxford University Press. It provides smoothing methods for nonparametric regression and density estimation

This package provides bindings to ImageMagick, a comprehensive image processing library. It supports many common formats (PNG, JPEG, TIFF, PDF, etc.) and manipulations (rotate, scale, crop, trim, flip, blur, etc). All operations are vectorized via the Magick++ STL meaning they operate either on a single frame or a series of frames for working with layers, collages, or animation. In RStudio, images are automatically previewed when printed to the console, resulting in an interactive editing environment.

Online data collection tools like Google Forms often export multiple-response questions with data concatenated in cells. The concat.split (cSplit) family of functions provided by this package splits such data into separate cells. This package also includes functions to stack groups of columns and to reshape wide data, even when the data are "unbalanced"---something which reshape (from base R) does not handle, and which melt and dcast from reshape2 do not easily handle.

This package implements targeted minimum loss-based estimators of counterfactual means and causal effects that are doubly-robust with respect both to consistency and asymptotic normality.

This package provides a simple and flexible way to generate Circos 2D track plot images. The types of plots include: heatmap, histogram, lines, scatterplot, tiles and plot items for further decorations include connector, link (lines and ribbons), and text (gene) label. All functions require only R graphics packages that comes with the base installation.