A lean C++ library for working with point cloud data

torchbearer: A model fitting library for PyTorch

Model manipulation and fitting library based on TensorFlow and optimised for simple and direct manipulation of probability density functions. Its main focus is on scalability, parallelisation and user friendly experience.

piecewise-regression (aka segmented regression) in python. For fitting straight line models to data with one or more breakpoints where the gradient changes.

SourceXtractor++, the next generation SExtractor

JetSeT a framework for self-consistent modeling and fitting of astrophysical relativistic jets

Course Project for the course CS 736

Package for fitting/optimization of NeuroML models

Python framework for multi-parameter optimization and evaluation of protein folding models

This notebook explores the housing dataset from Kaggle to predict Sales Prices of housing using advanced regression techniques such as feature engineering and gradient boosting.

Meta-analysis toolbox for basic research applications. Developed in MATLAB R2016b.

Develop a data science project using historical sales data to build a regression model that accurately predicts future sales. Preprocess the dataset, conduct exploratory analysis, select relevant features, and employ regression algorithms for model development. Evaluate model performance, optimize hyperparameters, and provide actionable insights.

tsiR: An R package for time-series Susceptible-Infected-Recovered models of epidemics

Jiles-Atherton system identification tool: Given a B(H) curve, finds the Jiles-Atherton model coefficients. Supports various JA model formulations. Has an interactive GUI.

Toolbox for change-point detection and ideal-observer analyses of IBL task data

Implementation of the algorithm described in the following paper. Korenberg, M., Billings, S.A. and Liu, Y.P. (1987) An Orthogonal Parameter Estimation Algorithm for Nonlinear Stochastic Systems

Tools for building and calibrating compartmental models of infectious disease.

An R package of S3 generic methods for Bayesian analyses that generate MCMC samples

General RANSAC solver with detailed examples.

The project involves projective geometry, geometric transformations, modelling of cameras, feature extraction, stereo vision, recognition and deep learning, 3d-modelling, geometry of surfaces and their silhouettes, tracking, and visualisation.