statistical-modeling

BAyesian Model-Building Interface (Bambi) in Python.

rstanarm R package for Bayesian applied regression modeling

an R package for structural equation modeling and more

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

An R package for Bayesian structural equation modeling

Basic statistical modelling examples.

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

Recursive Partitioning for Structural Equation Models

Simple statistical prediction of the survival chances of the passengers in the testing set, given certain conditions as input. Refer to README.md for more detail

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

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

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

農研機構統計研修「ベイズ統計モデリングとMCMC」

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

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

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

Simulation Methods in Econometrics: Theory and Applications in MATLAB is a book that aims to bridge the gap between econometric theory and the practical use of simulation techniques. It emphasizes intuition, reproducibility, and accessibility.

rstanarm R package for Bayesian applied regression modeling