Lecture Slides and R Sessions for Trevor Hastie and Rob Tibshinari's "Statistical Learning" Stanford course

This repository contains R code for exercices and plots in the famous book.

Full Bayesian Inference for Hidden Markov Models

This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.

🌲 broom helpers for decision tree methods (rpart, randomForest, and more!) 🌲

Solutions and code examples from An Introduction to Statistical Learning (Second Edition) by James, Witten, Hastie, and Tibshirani.

MoMA: Modern Multivariate Analysis in R

Univariate and multivariate time series forecasting, with uncertainty quantification (Python & R)

snpnet - Efficient Lasso Solver for Large-scale genetic variant data

Spatial error estimation and variable importance

StAtistical Models for the UnsupeRvised segmentAion of tIme-Series

several R scripts about Machine Learning that I code

An implementation of the gap statistic, a method for estimating the number of clusters in a set of data.

Functional Latent datA Models for clusterING heterogeneOus curveS

PREDICTIVE FINANCIAL ANALYTICS is about using statistical learning in Finance. Daniel Saxton uses GAM models to analyze cash flows, and Mark Bennett demonstrates how to predict security prices using corporate income statements.

Fast Sparse Linear Models for Big Data with SAGA

Generalized Linear Models with the Exclusive Lasso Penalty

An R package inspired by 'mixup: Beyond Empirical Risk Minimization'

Eigenfaces and Fisherfaces for Face Recognition. With implementation of algorithms as PCA, KNN, Fisher Discriminant Analysis.

Work related to statistical learning, computational statistics and applications to finance.