Statistical Modeling
Showing 36 of 40 projects
An open-source forecasting tool for time series data with multiple seasonality and linear or non-linear growth.
An automatic forecasting procedure for time series data with multiple seasonality and linear or non-linear growth.
A flexible, scalable deep probabilistic programming library built on PyTorch for universal representation of computable probability distributions.
A flexible, scalable deep probabilistic programming library built on PyTorch for universal probabilistic modeling.
A Python toolkit for causal and probabilistic reasoning using graphical models like Bayesian Networks and Structural Equation Models.
A lightweight probabilistic programming library using NumPy and JAX for autograd and JIT compilation to GPU/TPU/CPU.
A Python tutorial and cookbook for implementing Bayesian modeling techniques using PyMC3.
An open-source Python library for probabilistic time series modeling with both frequentist and Bayesian inference methods.
An R package for estimating causal effects in time series using Bayesian structural time-series models.
Python code and examples for Bayesian statistics from the book 'Think Bayes: Bayesian Statistics Made Simple'.
A Python library for building Generalized Additive Models (GAMs) with a scikit-learn-like API, emphasizing interpretability and performance.
An R package for fitting linear, generalized, and nonlinear mixed-effects models using S4 classes and RcppEigen.
A Julia package for fitting linear and generalized linear models with comprehensive statistical functionality.
An R package for tidy time series forecasting with models like ETS and ARIMA, integrated with the tidyverse.
A Julia implementation of the scikit-learn API, providing a uniform interface for machine learning models from both Julia and Python ecosystems.
A Scala toolkit for deployable probabilistic modeling using imperatively-defined factor graphs.
A Clojure library for high-performance Bayesian data analysis and machine learning on the GPU.
A JAX-powered probabilistic programming library focused on performant sampling methods for Bayesian inference on CPU, GPU, and TPU.
A curated collection of learning resources, R packages, and practical examples for understanding and applying topic modeling techniques.
A comprehensive family of R packages for analyzing spatial point pattern data and other spatial data types.
A high-performance Rust library for simulating stochastic processes, with applications in quantitative finance, statistical modeling, and synthetic data generation.
A full-featured Ruby implementation of Naive Bayes for probabilistic classification with customizable features.
Bayesian inference tools in Python for estimating Dirichlet priors and multinomial mixture models from discrete event data.
A Julia package for efficient large-scale Gaussian Mixture Models with support for diagonal/full covariance, parallel training, and variational Bayes.
A Node.js library for machine learning with linear regression and k-means clustering algorithms.
A Python library for unsupervised learning of hidden semi-Markov models with explicit durations.
A Python package for generating multidimensional synthetic data using Copula and fPCA models to preserve statistical properties.
An R package for statistical modeling of dynamic network data using actor-oriented and tie-based relational event models.
A Julia framework for probabilistic graphical models, enabling structured probabilistic modeling and inference.
A library for probabilistic computation and visualization in Idris, leveraging functorial, applicative, and monadic structures.
JavaScript implementation of multivariate Gaussian mixture model for unsupervised clustering of offline and real-time data streams.
Provides parsnip model wrappers for H2O machine learning algorithms, enabling integration with the tidymodels ecosystem.
A machine learning game that asks questions to guess objects using Bayesian inference and entropy minimization.
A Swift library for building predictions using linear regression with simple API and statistical insights.
A Julia framework for MCMC sampling and optimization with automatic gradient generation for user-defined models.
An R package for analyzing two-mode networks and extracting binary backbones from their one-mode projections.
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