🔗 Methods for Correlation Analysis

2D Outlier Analysis using Shiny

Mean and Covariance Matrix Estimation under Heavy Tails

Imputation of Financial Time Series with Missing Values and/or Outliers

An implementation of Isolation forest

Transform Univariate Time Series

RoBTT R package for estimating robust Bayesian t-test

Predicting disease spread, a DrivenData competition. I'am currently participating in this competition. I used it as submission for the second capstone project in the course 'Professional Certificate in Data Science' provided by Harvard University (HarvardX) on EDX.

A beginners tutorial for using R for exploratory analyses

Applied analysis on the Bayesian student-t "Robust" regression model with Jeffrey's prior. Compared its model performance and robustness of posterior distributions with the Gaussian model when outliers are present.

R package as a result of Scout Jarman's URCO grant reaserch.

Outlier Detection Using Cluster Analysis

The HotellingEllipse package helps draw the Hotelling's T-squared ellipse on a PCA or PLS score scatterplot by computing the Hotelling's T-squared statistic and providing the ellipse's coordinates, semi-minor, and semi-major axes lengths.

In this repository, using the statistical software R, are been analyzed robust techniques to estimate multivariate linear regression in presence of outliers, using the Bootstrap, a simulation method where the construction of sample distribution of given statistics occurring through resampling the same observed sample.

EDA is a must to do step in the data science workflow. Working on data, wrangling & transforming them is time consuming, and it determine the success degree of the next steps (pre preocessing, modelling, communicating outputs & decision making). This repo will show you how to perform EDA in R using the tidyverse ecosystem, and will introduce a c…

CU M.Sc. Project

outlier dytection

Repositorio para datos y documentos de la asignatura del máster en Ciencia de Datos: Minería de datos, detección de anomalías y aprendizaje no supervisado.