This package implements many algorithms for statistical learning on sparse matrices: matrix factorizations, matrix completion, elastic net regressions, factorization machines. The rsparse package also enhances the Matrix package by providing methods for multithreaded <sparse, dense> matrix products and native slicing of the sparse matrices in Compressed Sparse Row (CSR) format.

This is a package that allows conversion to and from data in JavaScript Object Notation (JSON) format. This allows R objects to be inserted into Javascript/ECMAScript/ActionScript code and allows R programmers to read and convert JSON content to R objects. This is an alternative to the rjson package.

Functions, workflow, and a Shiny application for visualizing sequence conservation and designing degenerate primers, probes, and (RT)-(q/d)PCR assays from a multiple DNA sequence alignment. The results can be presented in data frame format and visualized as dashboard-like plots. For more information, please see the package vignette.

R infrastructure for optimally robust estimation in general smoothly parameterized models using S4 classes and methods as described Kohl, M., Ruckdeschel, P., and Rieder, H. (2010), <doi:10.1007/s10260-010-0133-0>, and in Rieder, H., Kohl, M., and Ruckdeschel, P. (2008), <doi:10.1007/s10260-007-0047-7>.

This package provides a collection of functions to estimate Rogers-Castro migration age schedules using Stan'. This model which describes the fundamental relationship between migration and age in the form of a flexible multi-exponential migration model was most notably proposed in Rogers and Castro (1978) <doi:10.1068/a100475>.

Summarise results from simulation studies and compute Monte Carlo standard errors of commonly used summary statistics. This package is modelled on the simsum user-written command in Stata (White I.R., 2010 <https://www.stata-journal.com/article.html?article=st0200>), further extending it with additional performance measures and functionality.

This package provides tools to (i) check consistency of a finite set of consumer demand observations with a number of revealed preference axioms at a given efficiency level, (ii) compute goodness-of-fit indices when the data do not obey the axioms, and (iii) compute power against uniformly random behavior.

This package provides memory efficient S4 classes for storing sequences "externally" (behind an R external pointer, or on disk).

This package is used for cell type identification in spatial transcriptomics. It also handles cell type-specific differential expression.

The tkrplot package lets you place R graphics in a Tk, cross-platform graphical user interface toolkit widget.

This is a package for maximum likelihood estimation of censored regression (Tobit) models with cross-sectional and panel data.

The gdtools package provides functionalities to get font metrics and to generate base64 encoded string from raster matrix.

This package provides a violin plot, which is a combination of a box plot and a kernel density plot.

This package provides an implementation of the FastICA algorithm to perform independent component analysis (ICA) and projection pursuit.

Rove is a unit testing framework for Common Lisp applications. This is intended to be a successor of Prove.

Slim is a template language for Ruby that aims to reduce the syntax to the minimum while remaining clear.

This package implements the board game CamelUp for use in introductory statistics classes using a Shiny app.

Amends errors, augments data and aids analysis of John Snow's map of the 1854 London cholera outbreak.

Use the US Census API to collect summary data tables for SF1 and ACS datasets at arbitrary geographies.

Implement some deep learning architectures and neural network algorithms, including BP,RBM,DBN,Deep autoencoder and so on.

The CRAN check results and where your package stands in the CRAN submission queue in your R terminal.

This package provides a path-following algorithm for L1 regularized generalized linear models and Cox proportional hazards model.

This package provides basic graphing functions to fully demonstrate point-to-point connections in a polar coordinate space.

Calculate Hopkins statistic to assess the clusterability of data. See Wright (2023) <doi:10.32614/RJ-2022-055>.