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.

PythonFMU is a lightweight framework that enables the packaging of Python 3 code or CSV files as co-simulation FMUs (following FMI version 2.0).

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.

The FMI library is intended as a foundation for applications interfacing FMUs that follow FMI Standard. This version of the library supports the FMI 1.0, FMI 2.0 and FMI 3.0 standards.

PyFMI is a package for loading and interacting with FMUs, which are compiled dynamic models compliant with the FMI standard.

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.

Statistical computation and diagnostics for ArviZ.

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

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.

Modular plotting for ArviZ.

Mixmod is a software package for Model-Based supervised and unsupervised classification on qualitative, quantitative and mixed data.

Main Statistical functionalities:

• Likelihood maximization with EM, CEM and SEM algorithm

• Parsimonious models

• Selection criteria: BIC, ICL, NEC, CV

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

Base ArviZ features and converters.

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 package performs alignment, PCA, and modeling of multidimensional and unidimensional functions using the square-root velocity framework. This framework allows for elastic analysis of functional data through phase and amplitude separation.

thompson provides algorithms for solving the multi-armed bandit problem with Thompson Sampling, UCB-Upper confidence Bound, and randomized sampling.

benfordslaw is Python package to test if an empirical (observed) distribution differs significantly from a theoretical (expected, Benfords) distribution. The law states that in many naturally occurring collections of numbers, the leading significant digit is likely to be small. This method can be used if you want to test whether your set of numbers may be artificial (or manipulated). If a certain set of values follows Benford’s Law then model’s for the corresponding predicted values should also follow Benford’s Law. Normal data (unmanipulated) does trend with Benford’s Law, whereas manipulated or fraudulent data does not.

distfit is a Python package for probability density fitting of univariate distributions for random variables. The distfit library can determine the best fit for over 90 theoretical distributions. The goodness-of-fit test is used to score for the best fit and after finding the best-fitted theoretical distribution, the loc, scale, and arg parameters are returned. It can be used for parametric, non-parametric, and discrete distributions.

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).

pca is a python package to perform PCA and to create insightful plots. The core of PCA is built on sklearn functionality to find maximum compatibility when combining with other packages. But this PCA package can do a lot more. Besides the regular Principal Components, it can also perform SparsePCA, TruncatedSVD, and provide you with the information that can be extracted from the components.

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 a Python implementation of catch22, a collection of 22 time-series features.

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

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.