RStudio is an integrated development environment (IDE) for the R programming language. Some of its features include: Customizable workbench with all of the tools required to work with R in one place (console, source, plots, workspace, help, history, etc.); syntax highlighting editor with code completion; execute code directly from the source editor (line, selection, or file); full support for authoring Sweave and TeX documents. RStudio can also be run as a server, enabling multiple users to access the RStudio IDE using a web browser.

This fork of RStudio allows users to switch to different versions of R from the toolbar or project settings. R versions can be recorded in /etc/rstudio/r-versions and in the user's ~/.local/share/rstudio/r-versions.

Zpp transforms bash in a pre-processor for F90 source files. It offers a set of functions specifically tailored to build clean Fortran90 interfaces by generating code for all types, kinds, and array ranks supported by a given compiler.

Paraconf is a library that provides a simple query language to access a Yaml tree on top of libyaml.

FlatBuffers is a cross-platform serialization library for C++, C#, C, Go, Java, JavaScript, PHP, and Python. It was originally created for game development and other performance-critical applications.

Siconos is an open-source scientific software primarily targeted at modeling and simulating nonsmooth dynamical systems in C++ and in Python: Mechanical systems (rigid or solid) with unilateral contact and Coulomb friction and impact (nonsmooth mechanics, contact dynamics, multibody systems dynamics or granular materials). Switched Electrical Circuit such as electrical circuits with ideal and piecewise linear components: power converter, rectifier, Phase-Locked Loop (PLL) or Analog-to-Digital converter. Sliding mode control systems. Biology (Gene regulatory network).

Other applications are found in Systems and Control (hybrid systems, differential inclusions, optimal control with state constraints), Optimization (Complementarity systems and Variational inequalities), Fluid Mechanics, and Computer Graphics.

Siconos is an open-source scientific software primarily targeted at modeling and simulating nonsmooth dynamical systems in C++ and in Python: Mechanical systems (rigid or solid) with unilateral contact and Coulomb friction and impact (nonsmooth mechanics, contact dynamics, multibody systems dynamics or granular materials). Switched Electrical Circuit such as electrical circuits with ideal and piecewise linear components: power converter, rectifier, Phase-Locked Loop (PLL) or Analog-to-Digital converter. Sliding mode control systems. Biology (Gene regulatory network).

Other applications are found in Systems and Control (hybrid systems, differential inclusions, optimal control with state constraints), Optimization (Complementarity systems and Variational inequalities), Fluid Mechanics, and Computer Graphics.

Batsim is an infrastructure simulator that enables the study of resource management techniques. It can be used for scenarios such as:

• Comparing various scheduling heuristics (research prototypes or real implementations).

• Studying non simple phenomena such as network interference, energy consumption (DVFS, shutdown…) or I/O data movements.

SimGrid is a scientific instrument to study the behavior of large-scale distributed systems such as grids, "clouds", HPC, and P2P systems. It can be used to evaluate heuristics, prototype applications or even assess legacy MPI applications.

Modular plotting for ArviZ.

This package provides a toolbox implementing statistical methods to fit heavy-tailed distributions like power laws.

formulae is a Python library that implements Wilkinson's formulas for mixed-effects models. The main difference with other implementations like Patsy or formulaic is that formulae can work with formulas describing a model with both common and group specific effects (a.k.a. fixed and random effects, respectively).

Implements iterative statistics operators for mean, variance, high-order moments, extrema, covariance, threshold, quantile (experimental) and Sobol' indices.

Bambi is a high-level Bayesian model-building interface written in Python. It works with the PyMC probabilistic programming framework and is designed to make it extremely easy to fit Bayesian mixed-effects models common in biology, social sciences and other disciplines.

This package provides Kullback-Leibler projections for Bayesian model selection. Variable selection refers to the process of identifying the most relevant variables in a model from a larger set of predictors. When performing this process, we usually assume that variables contribute unevenly to the outcome, and we want to identify the most important ones. Sometimes we also care about the order in which variables are included in the model.

Base ArviZ features and converters.

Statistical computation and diagnostics for ArviZ.

skpro is a unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in Python.

It provides scikit-learn-like, scikit-base compatible interfaces to:

• tabular supervised regressors for probabilistic prediction

• tabular probabilistic time-to-event and survival prediction

• metrics to evaluate probabilistic predictions

• reductions to turn scikit-learn regressors into probabilistic skpro regressors

• building pipelines and composite models

• symbolic probability distributions

This package provides a powerful and scalable library that can be used for a variety of time series data mining tasks.

aeon is an open-source toolkit for time series machine learning. Fully compatible with scikit-learn, it brings together the latest machine learning methods alongside a wide range of classical approaches for tasks such as forecasting, clustering, and classification.

Skforecast is a Python library for time series forecasting using statistical and machine learning models. It works with any estimator compatible with the scikit-learn API, including popular options like LightGBM, XGBoost, CatBoost, Keras, and many others.

This is a Python library for time series data mining. It provides tools for time series classification, clustering and forecasting.

sktime is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks. Currently, this includes forecasting, time series classification, clustering, anomaly/changepoint detection, and other tasks. It comes with time series algorithms and scikit-learn compatible tools to build, tune, and validate time series models.

The package provides systematic time-series feature extraction by combining established algorithms from statistics, time-series analysis, signal processing, and nonlinear dynamics with a robust feature selection algorithm. In this context, the term time-series is interpreted in the broadest possible sense, such that any types of sampled data or even event sequences can be characterised.