This package provides a tbl_df class that offers better checking and printing capabilities than traditional data frames.

This package provides functions to access the RStudio API and provide informative error messages when it's not available.

This package is a port of the S+ "Robust Library". It provides methods for robust statistics, notably for robust regression and robust multivariate analysis.

XLISP-STAT is a statistical environment based on a Lisp dialect called XLISP. To facilitate statistical computations, standard functions for addition, logarithms, etc., have been modified to operate on lists and arrays of numbers, and a number of basic statistical functions have been added. Many of these functions have been written in Lisp, and additional functions can be added easily by a user. Several basic forms of plots, including histograms, scatterplots, rotatable plots and scatterplot matrices are provided. These plots support various forms of interactive highlighting operations and can be linked so points highlighted in one plot will be highlighted in all linked plots. Interactions with the plots are controlled by the mouse, menus and dialog boxes. An object-oriented programming system is used to allow menus, dialogs, and the response to mouse actions to be customized.

This package provides a library for Probabilistic Graphical Models. It can be used for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

This package implements a Dynamic Nested Sampling for computing Bayesian posteriors and evidences.

GNU PSPP is a statistical analysis program. It can perform descriptive statistics, T-tests, linear regression and non-parametric tests. It features both a graphical interface as well as command-line input. PSPP is designed to interoperate with Gnumeric, LibreOffice and OpenOffice. Data can be imported from spreadsheets, text files and database sources and it can be output in text, PostScript, PDF or HTML.

ArviZ is a Python package for exploratory analysis of Bayesian models. It includes functions for posterior analysis, data storage, model checking, comparison and diagnostics.

This package provides an implementation of the Ensemble Slice Sampling method. Features:

• fast & Robust Bayesian Inference

• efficient Markov Chain Monte Carlo (MCMC)

• black-box inference, no hand-tuning

• excellent performance in terms of autocorrelation time and convergence rate

• scale to multiple CPUs without any extra effort

• automated Convergence diagnostics

This package provides methods that simplify the setup of S3 generic functions and S3 methods. Major effort has been made in making definition of methods as simple as possible with a minimum of maintenance for package developers. For example, generic functions are created automatically, if missing, and naming conflict are automatically solved, if possible. The method setMethodS3() is a good start for those who in the future may want to migrate to S4.

This package provides a generic infrastructure for creating and using R package registries.

Given a regression model, segmented updates the model by adding one or more segmented (i.e., piecewise-linear) relationships. Several variables with multiple breakpoints are allowed.

This package embeds the SQLite database engine in R and provides an interface compliant with the DBI package. The source for the SQLite engine (version 3.8.8.2) is included.

JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind:

1. To have a cross-platform engine for the BUGS language;

2. To be extensible, allowing users to write their own functions, distributions and samplers;

3. To be a platform for experimentation with ideas in Bayesian modelling.

rpy2 is a redesign and rewrite of rpy. It is providing a low-level interface to R from Python, a proposed high-level interface, including wrappers to graphical libraries, as well as R-like structures and functions.

OpenTURNS is a scientific C++ and Python library including an internal data model and algorithms dedicated to the treatment of uncertainties. The main goal of this library is giving to specific applications all the functionalities needed to treat uncertainties in studies.

This Python package can be used to read and write SAS, SPSS and Stata files into/from Pandas DataFrames. It is a wrapper around the C library readstat.

The sourcetools package provides both an R and C++ interface for the tokenization of R code, and helpers for interacting with the tokenized representation of R code.

This package helps accessing files relative to a project root. It provides helpers for robust, reliable and flexible paths to files below a project root. The root of a project is defined as a directory that matches a certain criterion, e.g., it contains a certain regular file.

Nautilus is an pure-Python package for Bayesian posterior and evidence estimation. It utilizes importance sampling and efficient space exploration using neural networks. Compared to traditional MCMC and Nested Sampling codes, it often needs fewer likelihood calls and produces much larger posterior samples. Additionally, nautilus is highly accurate and produces Bayesian evidence estimates with percent precision. It is widely used in many areas of astrophysical research.

This package provides a collection of datasets used in Vega and Vega-Lite examples.

This package lets you calculate power for generalized linear mixed models, using simulation. It was designed to work with models fit using the lme4 package. The package is described in Green and MacLeod (2016).

Did you ever wish you could make scatter plots with cat shaped points? Now you can!

Visual predictive checks are a commonly used diagnostic plot in pharmacometrics, showing how certain statistics (percentiles) for observed data compare to those same statistics for data simulated from a model. The package can generate VPCs for continuous, categorical, censored, and (repeated) time-to-event data.