High Dimensional Discriminant Analysis in R ✨

This module allows users to analyze k-means & hierarchical clustering, and visualize results of Principal Component, Correspondence Analysis, Discriminant analysis, Decision tree, Multidimensional scaling, Multiple Factor Analysis, Machine learning, and Prophet analysis.

Multi-distributional Discriminant Analysis using Generalised Linear Latent Variable Modelling in R ⭐

R package DiscriMiner

This repository includes the code for the paper "Detection of Prostate Cancer with Multi-Parametric MRI Utilizing the Anatomic Structure of the Prostate".

Classification, sampling, and model selection methods. Ch. 4-6 exercises (An Introduction to Statistical Learning: https://www.statlearning.com/)

Models in R

DA incorporates the commonly used linear and non-linear, local and global supervised learning approaches (discriminant analysis). These discriminant analyses can be used to do ecological and evolutionary inference. We show the examples of demographic history inference, species identification, and population structure inference in the vignettes …

Análisis Multivariado I

Probabilistic OPLS discriminant analysis

Data Mining project (Fall2023) involving the classification and clustering of Sars-Cov-2 gene expression RNA-seq data

Fit four different neural networks: (a) Two distinct single hidden layer neural networks. (b) Two distinct neural networks with two hidden layers. Compare the accuracy of these four Neural networks among them. Also compare it to other classification methods.

Comparing Classification Methods. We will code some Discriminant Analysis Methods and compare them to Support Vector Machines (SVMs).

R code for linear discriminant analysis

Using labelled classifed data to infer a learning algorithm in R

Case Study Based on Human Activity Recognition Using Smartphones Dataset

In our second semester in ISI, Delhi, we did this project titled "Multivariate Analysis on Milk Transportation Data". The aim of the project was to implement Multivariate Statistical tools to extract insights from Milk Transportation Data.

Psychometric techniques, including functions to analyze dichotomous variables, and applied examples of factor analysis and discriminant analysis. Produced within the classes "Principles and Methods of Measurement" and "Public Opinion", both taught at the University of Chicago in the Spring of 2021.

Multivariate data analysis using R Studio.