This package provides efficient tools to compute the proximity between rows or columns of large matrices. Functions are optimised for large sparse matrices using the Armadillo and Intel TBB libraries. Among several built-in similarity/distance measures, computation of correlation, cosine similarity and Euclidean distance is particularly fast.

This package provides functions for the consistent analysis of compositional data (e.g. portions of substances) and positive numbers (e.g. concentrations).

This is a package for visualizing data quality of partially accruing data.

This package lets you build complex plots, heatmaps in particular, using natural semantics. Bigger plots can be assembled using directives such as LeftOf, RightOf, TopOf, and Beneath and more. Other features include clustering, dendrograms and integration with ggplot2 generated grid objects. This package is particularly designed for bioinformaticians to assemble complex plots for publication.

Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as glm. More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates.

This package provides color palettes that have been generated mostly from Wes Anderson movies.

This package can be used to solve Linear Programming / Linear Optimization problems by using the simplex algorithm.

This is an R package for the imputation of left-censored data under a compositional approach. The implemented methods consider aspects of relevance for a compositional approach such as scale invariance, subcompositional coherence or preserving the multivariate relative structure of the data. Based on solid statistical frameworks, it comprises the ability to deal with single and varying censoring thresholds, consistent treatment of closed and non-closed data, exploratory tools, multiple imputation, Markov Chain Monte Carlo (MCMC), robust and non-parametric alternatives, and recent proposals for count data.

This package provides an interface to the NetCDF file formats designed by Unidata for efficient storage of array-oriented scientific data and descriptions. Most capabilities of NetCDF version 4 are supported. Optional conversions of time units are enabled by UDUNITS version 2, also from Unidata.

Tools to clean and process text. Tools are geared at checking for substrings that are not optimal for analysis and replacing or removing them (normalizing) with more analysis friendly substrings (see Sproat, Black, Chen, Kumar, Ostendorf, & Richards (2001) doi:10.1006/csla.2001.0169) or extracting them into new variables. For example, emoticons are often used in text but not always easily handled by analysis algorithms. The replace_emoticon() function replaces emoticons with word equivalents.

Contains functions for data preparation, descriptives, hazard estimation and prediction with Aalen-Johansen or simulation in competing risks and multi-state models.

The analysis and inference of faunal remains recovered from archaeological sites concerns the field of zooarchaeology. The zooaRch package provides analytical tools to make inferences on zooarchaeological data. Functions in this package allow users to read, manipulate, visualize, and analyze zooarchaeological data.

This package provides functions for feed-forward neural networks with a single hidden layer, and for multinomial log-linear models.

This package provides a menu-driven program and library of functions for carrying out convergence diagnostics and statistical and graphical analysis of Markov chain Monte Carlo (MCMC) sampling output.

This package provides tool for estimation, testing and regression modeling of subdistribution functions in competing risks, as described in Gray (1988), A class of K-sample tests for comparing the cumulative incidence of a competing risk, Ann. Stat. 16:1141-1154, and Fine JP and Gray RJ (1999), A proportional hazards model for the subdistribution of a competing risk, JASA, 94:496-509.

This package provides the header files of mio, a cross-platform C++11 header-only library for memory mapped file IO.

This package provides a vectorized R function for calculating probabilities from a standard bivariate normal CDF.

This package allows for fast, correct, consistent, portable, as well as convenient character string/text processing in every locale and any native encoding. Owing to the use of the ICU library, the package provides R users with platform-independent functions known to Java, Perl, Python, PHP, and Ruby programmers. Among available features there are: pattern searching (e.g. via regular expressions), random string generation, string collation, transliteration, concatenation, date-time formatting and parsing, etc.

This package provides functions for Constraint Based Simulation using Flux Balance Analysis and informative analysis of the data generated during simulation.

This package generates version 2 and 4 request signatures for Amazon Web Services (AWS) and provides a mechanism for retrieving credentials from environment variables, AWS credentials files, and EC2 instance metadata. For use on EC2 instances, the package 'aws.ec2metadata' is suggested.

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 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 implements heuristics for the quadratic assignment problem (QAP). Currently only a simulated annealing heuristic is available.

This package provides data sets and scripts to accompany Time Series Analysis and Its Applications: With R Examples (4th ed), by R.H. Shumway and D.S. Stoffer. Springer Texts in Statistics, 2017, https://doi.org/10.1007/978-3-319-52452-8, and Time Series: A Data Analysis Approach Using R. Chapman-Hall, 2019, https://doi.org/10.1201/9780429273285.