A series of Jupyter notebooks, to know about Machine Learning, its implementation, and identifying its best practices.

Learning mathematical methods of data analysis in the language R.

Testing effects of missing data on phylogenetic inferences

Library for processing and extracting assets for the 3D/ADV engine

Kernels for machine learning problems

Normalization on skewness and kurtosis of a dataset

An R package for causal discovery in heavy-tailed models

Determined the best regression model which represents the data

This is a homework I did in the Spring 2017. It involves conducting Chi Square Tests, Confidence Intervals, Kolmogorov-Smirnov Tests, and Shapiro-Wilk Normality Tests. It has problem numbers that are associated to problems in "Using R: Introductory Statistics".

All my R code used for statistical analyses - Hypothesis Testing, t-tests, etc.

👥 How social norms are formed, transmitted, and influence individual behavior in various social contexts.

Gaussian Navie Bayes Classifier was applied on IRIS dataset. Different types of normality tests were used to introduce the normality concepts.

This a project I did in the Spring of 2017 for the graduate course of Statistical Computing. This project includes T-Tests, Non-parametric Tests, Linear Regressions, Correlation Tests, Chi Square Tests, and tests for normality. This project also includes some basic graphing.

Estimate a suite of normalizing transformations, including a new adaptation of a technique based on ranks which can guarantee normally distributed transformed data if there are no ties: ordered quantile normalization (ORQ).

Linear Mixed-Effects Models for Non-Gaussian Repeated Measurement Data