Typically, models in R exist in memory and can be saved via regular R serialization. However, some models store information in locations that cannot be saved using R serialization alone. The goal of bundle is to provide a common interface to capture this information, situate it within a portable object, and restore it for use in new settings.

This package supports arithmetic (via S4 classes and methods) for arbitrary precision floating point numbers, including transcendental functions. To this end, the package interfaces with the Multiple Precision Floating-Point Reliable (MPFR) library.

This package provides methods for variable selection for AFT models.

This package assists you in setting up and retrieving of HTTPS and SSH credentials for use with git and other services. For HTTPS remotes the package interfaces the git-credential utility which git uses to store HTTP usernames and passwords. For SSH remotes this package provides convenient functions to find or generate appropriate SSH keys. The package both helps the user to setup a local git installation, and also provides a back-end for git/ssh client libraries to authenticate with existing user credentials.

This package provides geometries to plot network objects with the ggplot2 package.

Sensitivity (or recall or true positive rate), false positive rate, specificity, precision (or positive predictive value), negative predictive value, misclassification rate, accuracy, F-score---these are popular metrics for assessing performance of binary classifiers for certain thresholds. These metrics are calculated at certain threshold values. Receiver operating characteristic (ROC) curve is a common tool for assessing overall diagnostic ability of the binary classifier. Unlike depending on a certain threshold, area under ROC curve (also known as AUC), is a summary statistic about how well a binary classifier performs overall for the classification task. The ROCit package provides flexibility to easily evaluate threshold-bound metrics.

This is a port of the type guesser from the readr package, the so-called readr first edition parsing engine, now superseded by vroom.

This package lets you analyze response times and accuracies from psychological experiments with the linear ballistic accumulator (LBA) model from Brown and Heathcote (2008). The LBA model is optionally fitted with explanatory variables on the parameters such as the drift rate, the boundary and the starting point parameters. A log-link function on the linear predictors can be used to ensure that parameters remain positive when needed.

This package contains a list of functional time series, sliced functional time series, and functional data sets. Functional time series is a special type of functional data observed over time. Sliced functional time series is a special type of functional time series with a time variable observed over time.

This package performs prediction of a response function from simulated response values, allowing black-box optimization of functions estimated with some error. It includes a simple user interface for such applications, as well as more specialized functions designed to be called by the Migraine software (Rousset and Leblois, 2012 <doi:10.1093/molbev/MSR262>; Leblois et al., 2014 <doi:10.1093/molbev/msu212>; and see URL). The latter functions are used for prediction of likelihood surfaces and implied likelihood ratio confidence intervals, and for exploration of predictor space of the surface. Prediction of the response is based on ordinary Kriging (with residual error) of the input. Estimation of smoothing parameters is performed by generalized cross-validation.

This package provides several utility functions for the book entitled "Practices of Medical and Health Data Analysis using R" (Pearson Education Japan, 2007) with Japanese demographic data and some demographic analysis related functions.

r-kmer is an R package for rapidly computing distance matrices and clustering large sequence datasets using fast alignment-free k-mer counting and recursive k-means partitioning.

The R package ggplot2 is a plotting system based on the grammar of graphics. GGally extends ggplot2 by adding several functions to reduce the complexity of combining geometric objects with transformed data. Some of these functions include a pairwise plot matrix, a two group pairwise plot matrix, a parallel coordinates plot, a survival plot, and several functions to plot networks.

Suppose we have data that has so many series that it is hard to identify them by their colors as the differences are so subtle. With gghighlight we can highlight those lines that match certain criteria. The result is a usual ggplot object, so it is fully customizable and can be used with custom themes and facets.

This package provides flexible parametric models for time-to-event data, including the Royston-Parmar spline model, generalized gamma and generalized F distributions. Any user-defined parametric distribution can be fitted, given at least an R function defining the probability density or hazard. There are also tools for fitting and predicting from fully parametric multi-state models.

This package runs a minimum-hypergeometric (mHG) test as described in "Discovering Motifs in Ranked Lists of DNA Sequences" by Eran Eden.

This package provides tools and functions for managing the download of binary files. Binary repositories are defined in the YAML format. Defining new pre-download, download and post-download templates allow additional repositories to be added.

This package contains a function that imports data from a CSV file, or uses manually entered data from the format (x, y, weight) and plots the appropriate ACC vs LOI graph and LMA graph. The main function is plotLMA (source file, header) that takes a data set and plots the appropriate LMA and ACC graphs. If no source file (a string) was passed, a manual data entry window is opened. The header parameter indicates by TRUE/FALSE (false by default) if the source CSV file has a header row or not. The dataset should contain only one independent variable (x) and one dependent variable (y) and can contain a weight for each observation.

The r-mhsmm package implements estimation and prediction methods for hidden Markov and semi-Markov models for multiple observation sequences. Such techniques are of interest when observed data is thought to be dependent on some unobserved (or hidden) state. Also, this package is suitable for equidistant time series data, with multivariate and/or missing data. Allows user defined emission distributions.

This package solves convex cone programs via operator splitting. It can solve: linear programs, second-order cone programs, semidefinite programs, exponential cone programs, and power cone programs, or problems with any combination of those cones. SCS uses AMD (a set of routines for permuting sparse matrices prior to factorization) and LDL (a sparse LDL factorization and solve package) from SuiteSparse.

This package provides bindings to tesseract: an optical character recognition (OCR) engine that supports over 100 languages. The engine is highly configurable in order to tune the detection algorithms and obtain the best possible results.

This package provides easy-to-use and versatile functions to output R objects in HTML format.

This package implements a parametric bootstrap test and a Kenward Roger modification of F-tests for linear mixed effects models and a parametric bootstrap test for generalized linear mixed models.

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.