This package implements multiple imputation for multivariate panel or clustered data.

The range of functions provided by this package makes it possible to draw highly versatile genomic sequence logos. Features include, but are not limited to, modifying colour schemes and fonts used to draw the logo, generating multiple logo plots, and aiding the visualisation with annotations. Sequence logos can easily be combined with other ggplot2 plots.

This package provides an R interface to the NCBI's EUtils API, allowing users to search databases like GenBank PubMed, process the results of those searches and pull data into their R sessions.

This package provides a convenience wrapper that uses the rmarkdown package to render small snippets of code to target formats that include both code and output. The goal is to encourage the sharing of small, reproducible, and runnable examples on code-oriented websites or email. reprex also extracts clean, runnable R code from various common formats, such as copy/paste from an R session.

This package provides tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models. More precisely, this package allows (conditional) simulations from various parametric max-stable models, analysis of the extremal spatial dependence, the fitting of such processes using composite likelihoods or least square (simple max-stable processes only), model checking and selection and prediction.

This package provides a set of functions to analyze overdispersed counts or proportions. Most of the methods are already available elsewhere but are scattered in different packages. The proposed functions should be considered as complements to more sophisticated methods such as generalized estimating equations (GEE) or generalized linear mixed effect models (GLMM).

This package provides bindings to GnuPG for working with OpenGPG (RFC4880) cryptographic methods. It includes utilities for public key encryption, creating and verifying digital signatures, and managing your local keyring. Some functionality depends on the version of GnuPG that is installed on the system.

This package provides a %<-% operator to perform multiple, unpacking, and destructuring assignment in R. The operator unpacks the right-hand side of an assignment into multiple values and assigns these values to variables on the left-hand side of the assignment.

This package provides routines for the analysis of indirectly measured haplotypes. The statistical methods assume that all subjects are unrelated and that haplotypes are ambiguous (due to unknown linkage phase of the genetic markers). The main functions are: haplo.em(), haplo.glm(), haplo.score(), and haplo.power(); all of which have detailed examples in the vignette.

This package provides code analysis tools for R to check R code for possible problems.

This package provides convenience functions for data preparation and modeling often used in analytical customer relationship management (aCRM).

This package provides a set of predicates and assertions for checking the properties of models. This is mainly for use by other package developers who want to include run-time testing features in their own packages.

This package offers extensive tools for phylogenetic analysis. It focuses on phylogenetic comparative biology but also includes methods for visualizing, analyzing, manipulating, reading, writing, and inferring phylogenetic trees. Functions for comparative biology include ancestral state reconstruction, model fitting, and phylogeny and trait data simulation. A broad range of plotting methods includes mapping trait evolution on trees, projecting trees into phenotype space or geographic maps, and visualizing correlated speciation between trees. Additional functions allow for reading, writing, analyzing, inferring, simulating, and manipulating phylogenetic trees and comparative data. Examples include computing consensus trees, simulating trees and data under various models, and attaching species or clades to a tree either randomly or non-randomly. This package provides numerous tools for tree manipulations and analyses that are valuable for phylogenetic research.

This package provides an extension to the Shiny web application framework for R, making it easy to create attractive dashboards.

The package provides estimators of the mode of univariate unimodal (and sometimes multimodal) data and values of the modes of usual probability distributions.

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

This package provides an interface to Amazon Web Services cost management services, including cost and usage reports, budgets, pricing, and more.

This package implements a successive halving and hyperband optimization algorithm for the mlr3 ecosystem. The implementation in mlr3hyperband features improved scheduling and parallelizes the evaluation of configurations. The package includes tuners for hyperparameter optimization in mlr3tuning and optimizers for black-box optimization in bbotk.

This package provides an R interface to the dygraphs JavaScript charting library (a copy of which is included in the package). It provides rich facilities for charting time-series data in R, including highly configurable series- and axis-display and interactive features like zoom/pan and series/point highlighting.

This package is an implementation of extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. It includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart).

This package contains functions to compute the nonparametric maximum likelihood estimator (MLE) for the bivariate distribution of (X,Y), when realizations of (X,Y) cannot be observed directly. To be more precise, we consider the situation where we observe a set of rectangles that are known to contain the unobservable realizations of (X,Y). We compute the MLE based on such a set of rectangles. The methods can also be used for univariate censored data (see data set cosmesis), and for censored data with competing risks (see data set menopause). The package also provides functions to visualize the observed data and the MLE.

This package contains the function ggsurvplot() for easily drawing beautiful and 'ready-to-publish' survival curves with the 'number at risk' table and 'censoring count plot'. Other functions are also available to plot adjusted curves for Cox model and to visually examine Cox model assumptions.

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 package provides a graph implementation that can be thought of as two tidy data frames describing node and edge data respectively. It provides an approach to manipulate these two virtual data frames using the API defined in the dplyr package, and it also provides tidy interfaces to a lot of common graph algorithms.