This package aims to streamline and accelerate the process of saving and loading R objects, improving speed and compression compared to other methods. The package provides two compression formats: the qs2 format, which uses R serialization via the C API while optimizing compression and disk I/O, and the qdata format, featuring custom serialization for slightly faster performance and better compression. Additionally, the qs2 format can be directly converted to the standard RDS format, ensuring long-term compatibility with future versions of R.

Visualise complex relations in texts. This is done by providing functionalities for displaying text co-occurrence networks, text correlation networks, dependency relationships as well as text clustering. Feel free to join the effort of providing interesting text visualisations.

This package enables you to estimate the p-values for predictors x against target variable y in Lasso regression, using the regularization strength when each predictor enters the active set of regularization path for the first time as the statistic.

This package provides the datasets to support the Fish Stock Assessment (FSA) package.

This package contains routines for logspline density estimation. The function oldlogspline() uses the same algorithm as the logspline package version 1.0.x; i.e., the Kooperberg and Stone (1992) algorithm (with an improved interface). The recommended routine logspline() uses an algorithm from Stone et al (1997).

This package provides tools for the comparison of distributions. This includes nonparametric estimation of the relative distribution PDF and CDF and numerical summaries as described in "Relative Distribution Methods in the Social Sciences" by Mark S. Handcock and Martina Morris, Springer-Verlag, 1999, Springer-Verlag, ISBN 0387987789.

This package provides qualitative methods for the validation of dynamic models. It contains

1. an orthogonal set of deviance measures for absolute, relative and ordinal scale and

2. approaches accounting for time shifts.

The first approach transforms time to take time delays and speed differences into account. The second divides the time series into interval units according to their main features and finds the longest common subsequence (LCS) using a dynamic programming algorithm.

This package provides an implementation of maximum likelihood estimators for a variety of heavy tailed distributions, including both the discrete and continuous power law distributions. Additionally, a goodness-of-fit based approach is used to estimate the lower cut-off for the scaling region.

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

httpcode provides functionality for finding and explaining the meaning of HTTP status codes. Functions are included for searching for codes by full or partial number, by message, and to get appropriate dog and cat images for many status codes.

dplyr is the next iteration of plyr. It is focused on tools for working with data frames. It has three main goals: 1) identify the most important data manipulation tools needed for data analysis and make them easy to use in R; 2) provide fast performance for in-memory data by writing key pieces of code in C++; 3) use the same code interface to work with data no matter where it is stored, whether in a data frame, a data table or database.

This package provides an R wrapper for the special functions and quasi random number generators of the GNU Scientific Library.

This package provides a range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, network regression, random graph generation, and 2D/3D network visualization.

This package provides tools to interact with Google Sheets from within R.

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.

This package provides chronological R objects which can handle dates and times.

This package contains functions to estimate L-moments and trimmed L-moments from the data. It also contains functions to estimate the parameters of the normal polynomial quantile mixture and the Cauchy polynomial quantile mixture from L-moments and trimmed L-moments.

This package provides functions for fitting the Autoregressive and Moving Average Symmetric Model for univariate time series introduced by Maior and Cysneiros (2018), <doi:10.1007/s00362-016-0753-z>. Fitting method: conditional maximum likelihood estimation. For details see: Wei (2006), Time Series Analysis: Univariate and Multivariate Methods, Section 7.2.

This package provides a collection of functions to create spatial weights matrix objects from polygon contiguities, from point patterns by distance and tessellations, for summarizing these objects, and for permitting their use in spatial data analysis, including regional aggregation by minimum spanning tree.

This package provides common functionality for the dynverse packages. dynverse is created to support the development, execution, and benchmarking of trajectory inference methods.

This package provides tools for depth functions methodology applied to multivariate analysis. Besides allowing calculation of depth values and depth-based location estimators, the package includes functions or drawing contour plots and perspective plots of depth functions. Euclidean and spherical depths are supported.

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 an implementation of about 6 major classes of statistical regression models. Currently only fixed-effects models are implemented, i.e., no random-effects models. Many (150+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE, using Fisher scoring. VGLMs can be loosely thought of as multivariate generalised linear models.

This package provides tools to compute and represent gene set enrichment or depletion from your data based on pre-saved maps from the Atlas of Cancer Signalling Networks (ACSN) or user imported maps. The gene set enrichment can be run with hypergeometric test or Fisher exact test, and can use multiple corrections. Visualization of data can be done either by barplots or heatmaps.