The DOMID (Detecting Outliers in MIxed-type Data) R package includes functions that can be used for detecting outliers in data sets consisting of mixed-type data (i.e. both continuous and discrete variables).

This repository hosts an R script tailored for preprocessing and cleansing LiDAR (Light Detection and Ranging) LAS files, specifically targeting data from the year 2020. Primarily, the script centers on outlier removal within point cloud data through statistical thresholding. Its workflow encompasses LAS file parsing, Z-based box plot generation.

FRASER - Find RAre Splicing Events in RNA-seq

OUTRIDER: OUTlier in RNA-seq fInDER is an R-based framework to find aberrantly expressed genes in RNA-seq data

Contains R codes for processing and analyzing data for Great Lakes Cladophora work

A find(e)r of influential cases and outliers in SEM

Outlier detection based on random forest models

Outlier Detection Tools for Functional Data Analysis

Linear regression, VIF, Auto Correlation.

Implementation of BISECT from [2] for Hidden Outlier generation.

The DOMID (Detecting Outliers in MIxed-type Data) R package includes functions that can be used for detecting outliers in data sets consisting of mixed-type data (i.e. both continuous and discrete variables).

Visualize multidimensional outlier detection algorithms

Purely presence-only species distribution modeling with isolation forest and its variations such as Extended isolation forest and SCiForest.

This repository contains different scripts to automate and visualize analysis performed for the "Integration of proteomics with genomics and transcriptomics increases the diagnosis rate of Mendelian disorders"

Data mining and machine learning libraries are used in this machine learning project to detect the fraud. More importantly, this report focuses on vehicle insurance company claim statistics to use the gathered knowledge from actuarial Science Course.

School work archives

Outlier Detection Using Cluster Analysis

This repo contains a proposal for functional time series outlier detection based on multivariate functional processes and functional depths.