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 unified R graphics backend. Render R graphics fast and easy to many common file formats. It provides a thread safe C interface for asynchronous rendering of R graphics.

This package lets you interface to Nocedal et al. L-BFGS-B.3.0 limited memory BFGS minimizer with bounds on parameters. This registers a R compatible C interface to L-BFGS-B.3.0 that uses the same function types and optimization as the optim() function. This package also adds more stopping criteria as well as allowing the adjustment of more tolerances.

This package provides tools for the analysis of complex survey samples. The provided features include: summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples; variances by Taylor series linearisation or replicate weights; post-stratification, calibration, and raking; two-phase subsampling designs; graphics; PPS sampling without replacement; principal components, and factor analysis.

This package implements functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the spatstat family of packages. Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.

This package provides tests and assertions to perform frequent argument checks. A substantial part of the package was written in C to minimize any worries about execution time overhead.

This package provides cross-platform utilities for prompting the user for credentials or a passphrase, for example to authenticate with a server or read a protected key.

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 tools to compute polychoric and polyserial correlations by quick "two-step" methods or ML, optionally with standard errors; tetrachoric and biserial correlations are special cases.

The CommonMark specification defines a rationalized version of markdown syntax. This package uses the cmark reference implementation for converting markdown text into various formats including HTML, LaTeX and groff man. In addition, it exposes the markdown parse tree in XML format. The latest version of this package also adds support for Github extensions including tables, autolinks and strikethrough text.

This lightweight package that adds progress bar to vectorized R functions apply. The implementation can easily be added to functions where showing the progress is useful e.g. bootstrap.

This is a package for exploratory graphical analysis of multivariate data, specifically gene expression data with different projection methods: principal component analysis, correspondence analysis, spectral map analysis.

Multivariate data analysis is the simultaneous observation of more than one characteristic. In contrast to the analysis of univariate data, in this approach not only a single variable or the relation between two variables can be investigated, but the relations between many attributes can be considered. For the statistical analysis of chemical data one has to take into account the special structure of this type of data. This package contains about 30 functions, mostly for regression, classification and model evaluation and includes some data sets used in the R help examples. It was designed as a R companion to the book "Introduction to Multivariate Statistical Analysis in Chemometrics" written by K. Varmuza and P. Filzmoser (2009).

This is a collection of econometric functions for performance and risk analysis. This package aims to aid practitioners and researchers in utilizing the latest research in analysis of non-normal return streams. In general, it is most tested on return (rather than price) data on a regular scale, but most functions will work with irregular return data as well, and increasing numbers of functions will work with P&L or price data where possible.

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

This package provides tools to convert plot function calls (using expression or formula) to grob or ggplot objects that are compatible with the grid and ggplot2 environment. With this package, we are able to e.g. use cowplot to align plots produced by base graphics, grid, lattice, vcd etc. by converting them to ggplot objects.

This package provides a collection of functions helpful in learning the basic tenets of Bayesian statistical inference. It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions. It contains MCMC algorithms for summarizing posterior distributions defined by the user. It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.

This package provides UI widget and layout functions for writing Shiny apps that work well on small screens.

The smurf package contains the implementation of the Sparse Multi-type Regularized Feature (SMuRF) modeling algorithm to fit generalized linear models (GLMs) with multiple types of predictors via regularized maximum likelihood. Next to the fitting procedure, following functionality is available:

• Selection of the regularization tuning parameter lambda using three different approaches: in-sample, out-of-sample or using cross-validation.

• S3 methods to handle the fitted object including visualization of the coefficients and a model summary.

This package provides a number of user-level functions to work with grid graphics, notably to arrange multiple grid-based plots on a page, and draw tables.

This package simplifies custom CSS styling of both shiny and rmarkdown via Bootstrap Sass. It supports both Bootstrap 3 and 4 as well as their various Bootswatch themes. An interactive widget is also provided for previewing themes in real time.

This package provides an R interface to the JAGS MCMC library. JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation.

Missing values are ubiquitous in data and need to be explored and handled in the initial stages of analysis. The package provides data structures and functions that facilitate the plotting of missing values and examination of imputations. This allows missing data dependencies to be explored with minimal deviation from the common work patterns of ggplot2 and tidy data.

This package provides RStudio addins and R functions that make copy-pasting vectors and tables to text painless.