statistical-modeling

BAyesian Model-Building Interface (Bambi) in Python.

an R package for structural equation modeling and more

rstanarm R package for Bayesian applied regression modeling

An R package for Bayesian structural equation modeling

Statistics and Mathematics for Machine Learning, Deep Learning , Deep NLP

Forecasted product sales using time series models such as Holt-Winters, SARIMA and causal methods, e.g. Regression. Evaluated performance of models using forecasting metrics such as, MAE, RMSE, MAPE and concluded that Linear Regression model produced the best MAPE in comparison to other models

Fast Bayesian Inference in Python. 50+ conjugate models with vectorized updates, sufficient statistic helpers, built-in plotting, and SciPy integration.

Learn about Probabilistic Time Series Forecasting, its key techniques, real-world applications, and advantages over traditional forecasting methods.

Basic statistical modelling examples.

The official implementation of "Joint Modeling of Image and Label Statistics for Enhancing Model Generalizability of Medical Image Segmentation" via Pytorch

An open-source toolkit for comparing and conflating Points of Interest (POIs) across major geospatial datasets.

2026 FIFA World Cup prediction research system — 7-component fusion (Dixon-Coles, Elo, Pi-Rating, Weibull, Market, Stacking, Conformal), auditable walk-forward backtesting, bilingual reports

IBM SPSS Statistics Pro for Windows — the complete professional build with every module enabled: statistical modeling, econometrics, and data analysis, no edition limits after install.

Code and resources to serve as a starting point for data science projects.

Recursive Partitioning for Structural Equation Models

A professional, research-grade comparison of Gaussian Copula and Variational Autoencoder (VAE) methods for synthetic tabular data generation. Includes full evaluation pipeline with distribution overlap, correlation analysis, PCA projections, pairplots, metrics, and automated visual reports.

農研機構統計研修「ベイズ統計モデリングとMCMC」

We present here a 1D convolutional neural network model to predict grain protein content using spectroscopic data of multiple cereals

An R package for Bayesian structural equation modeling using INLA