Minimalist plotting for Python, inspired by Edward Tufte’s principles of data visualization. Maximising the data–ink ratio: remove non-essential lines, marks, and colours. Content-driven spines and ...

This repository contains comprehensive implementations and solutions for statistical analysis, data science methodologies, and computational mathematics assignments. Each assignment demonstrates ...

The triangular distribution is a continuous probability distribution widely used in scenarios with limited data, such as project management, risk analysis, and Monte Carlo simulations. Its named for ...

Quantities calculated from molecular simulations are often subject to an initial bias due to unrepresentative starting configurations. Initial data are usually discarded to reduce bias. Chodera’s ...

Decarbonatization initiatives have rapidly increased the demand for lithium. This study uses public waste compliance reports and Monte Carlo approaches to estimate total lithium mass yields from ...

The authors present a critique of current usage of principal component analysis in geometric morphometrics, making a convincing case with benchmark data that standard techniques perform poorly. The ...

Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge ...

The SwarmFACE package utilizes magnetic field measurements by the Swarm satellites to study systems of field-aligned currents (FACs). Improvements of well-established techniques as well as novel ...

Isotopic composition modelling is a key aspect in many environmental studies. This work presents Isocompy, an open source Python library that estimates isotopic compositions through machine learning ...

The Empirical Cumulative Distribution-based Outlier Detection (ECOD) has a very intuitive approach: Outliers are the rare events in the tails of a distribution, they can be identified by measuring the ...