Fast methods for multivariate normal distributions

webPrices: Scrape pricing with rvest and the tidyverse

Subject during MA studies at SGH. Example of using basic libraries with practical use.

Visualize probability distributions

Estimation of Overlapping in Empirical Distributions.

This package implements both the discrete and continuous maximum likelihood estimators for fitting the power-law distribution to data. Additionally, a goodness-of-fit based approach is used to estimate the lower cutoff for the scaling region.

This Shiny app provides a visual analysis of the height distribution among women's basketball players.

This repository explores probability distributions across multiple cases, offering insights and analyses in a concise format.

This is a project I did in the Spring of 2017 for a graduate course in Statistical Computing. I was asked to find the descriptive statistics of 1000 simulations of a distribution, specifically to find the mean, median, midrange, and interquartile range of the simulations. I was asked to find these statistics for 1000 simulations of sample sizes …

Estimation of the parameters of returns for a stocks using the maximum likelihood estimation method.

Using a split violin plot to analyse the distribution of freehold and leasehold new and existing median purchase price in 2017

Kibble Distribution

Exploration of statistical probability distributions using the Central Limit Theorem in R. Random variable generation using functions of random variables.

R package for constructing and evaluating Markov chains with cashflows that depend on the state of the underlying markov chain using Thiele difference type equations

Scripts for species distribution modeling in R using MaxEnt

R program to analyze and calculate statistical values given a dataset of home prices that include information about the number of baths, bedrooms, year, square-feet, taxes, and other features. Learn more:

Distribution fitting using l-moments method

Code and data for three vignettes described in Kass et al. (2024) that highlight how pluralistic use of R package tools can enhance analyses