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

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

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

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

農研機構統計研修「ベイズ統計モデリングとMCMC」

Recursive Partitioning for Structural Equation Models

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

OCaml Random Forests

An autoencoder neural network and a Gaussian mixture model are used for generating movement quality scores for rehabilitation exercises.

Portfolio of personal projects & curriculum from Springboard

rstanarm R package for Bayesian applied regression modeling

Expand broom::tidy() output for categorical parameter estimates

Wrapper on top of libsvm-tools

2022-11_brainhack_DetecSpikMotifs: Automatic detection of spiking motifs in neurobiological data. This project aims to develop a method for the automated detection of repeating spiking motifs, possibly noisy, in ongoing activity.

MATH-342 Time Series course taken at EPFL during Spring 17-18.

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