This package provides useful utilities from Seminar fuer Statistik ETH Zurich, including many that are related to graphics.

MAPIE allows you to easily estimate prediction intervals (or prediction sets) using your favourite scikit-learn-compatible model for single-output regression or multi-class classification settings.

Prediction intervals output by MAPIE encompass both aleatoric and epistemic uncertainties and are backed by strong theoretical guarantees thanks to conformal prediction methods intervals.

Vega-Altair is a declarative statistical visualization library for Python.

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

GetDist is a Python package for analysing Monte Carlo samples, including correlated samples from Markov Chain Monte Carlo (MCMC).

This is a package to provide infrastructure for managing package parameters. Parameters are easy to get in relevant functions within a package, and rrror is thrown if a parameter is missing. Developers are able to register parameters and set their default value in a config file that is part of the package in YAML format, and users are able to override parameters using their own YAML. Users get an exception when trying to override a parameter that was not registered, and can load multiple parameters to the current environment.

R is a language and environment for statistical computing and graphics. It provides a variety of statistical techniques, such as linear and nonlinear modeling, classical statistical tests, time-series analysis, classification and clustering. It also provides robust support for producing publication-quality data plots. A large amount of 3rd-party packages are available, greatly increasing its breadth and scope.

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.

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

Command-line tool and C library for reading files from popular stats packages like SAS, Stata and SPSS.

This package provides R functions implementing a standard unit testing framework, with additional code inspection and report generation tools.

This package provides utility functions useful when programming and developing R packages.

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 provides functions to read flat or tabular text files from disk (or a connection).

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.

This package displays a progress bar in the R console for long running computations taking place in C++ code, and support for interrupting those computations even in multithreaded code, typically using OpenMP.

This is a package for Non-Negative Linear Models (NNLM). It implements fast sequential coordinate descent algorithms for non-negative linear regression and non-negative matrix factorization (NMF). It supports mean square error and Kullback-Leibler divergence loss. Many other features are also implemented, including missing value imputation, domain knowledge integration, designable W and H matrices and multiple forms of regularizations.

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.

tidyr is a reframing of the reshape2 package designed to accompany the tidy data framework, and to work hand-in-hand with magrittr and dplyr to build a solid pipeline for data analysis. It is designed specifically for tidying data, not the general reshaping that reshape2 does, or the general aggregation that reshape did. In particular, built-in methods only work for data frames, and tidyr provides no margins or aggregation.

Radian is an alternative console for the R program with multiline editing and rich syntax highlight. One would consider Radian as a IPython clone for R, though its design is more aligned to Julia.

This package implements importance sampling from the truncated multivariate normal using the Geweke-Hajivassiliou-Keane (GHK) simulator. Unlike Gibbs sampling which can get stuck in one truncation sub-region depending on initial values, this package allows truncation based on disjoint regions that are created by truncation of absolute values. The GHK algorithm uses simple Cholesky transformation followed by recursive simulation of univariate truncated normals hence there are also no convergence issues. Importance sample is returned along with sampling weights, based on which, one can calculate integrals over truncated regions for multivariate normals.

This package provides an integration of Eigen in R using a C++ template library for linear algebra: matrices, vectors, numerical solvers and related algorithms. It supports dense and sparse matrices on integer, floating point and complex numbers, decompositions of such matrices, and solutions of linear systems.