Statistics functions for python

A Markov Model DNA sequence generator to generate pseudo-replicate sequences based on an input sequence

Call-center discrete event simulation project mostly done using numpy and simple data structures.

Phylogenetic model exploration with deep learning

Cegpy (/segpaɪ/) is a Python package for working with Chain Event Graphs. It supports learning the graphical structure of a Chain Event Graph from data, encoding of parametric and structural priors, estimating its parameters, and performing inference.

pydisagg is a Python package for disaggregating estimated count observations across groups under generalized proportionality assumptions.

An Open-Source, synthetic transtibial residual limb anatomic dataset

In this repository are contained all the codes for the data analysis of batch accumulation test for PHA production.

Escrevendo um plugin experimental para interpolação (IDW e Krigagem) no QGIS.

NCAA_smalldata_tournamentSimulation

💫 Models for the spaCy Natural Language Processing (NLP) library

This repository provides a systematic implementation and evaluation of advanced statistical measures for data repeatability, grounded in the methodologies outlined in "Statistical Analysis of Data Repeatability Measures" by Wang et al. (2020). It focuses on applying and extending metrics such as discriminability, fingerprinting, and rank sums

Generalized Additive Models in Python.

This repository contains an implement of a famous Quantitative Trading strategy known as "Statistical Arbitrage". For a detailed overview, please refer to the README file, for understanding the algorithm in depth, visit my website below.

The purpose of this analysis is to obtain a forecasting model by the relationship between different automobile component combination and their price and predict prices of possible automobile component combination.

Statistical models

calibrate ETAS, simulate using ETAS, estimate completeness magnitude & magnitude frequency distribution

Predicting House Prices using Machine Learning - Data Cleaning, Feature Engineering, and Model Selection (XGBoost)

An analysis of UK greenhouse gas emissions from 1990 to 2023, using ARIMA for time series forecasting, for a task issued by the National Audit Office (NAO). Includes exploratory analysis, stationarity tests, and visualisation with confidence intervals for a 5-year prediction.

A library for discrete-time Markov chains analysis.