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

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.

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.

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.

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

Tools for building and calibrating compartmental models of infectious disease.

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

A Python library for thermal analysis and reaction kinetics. Supports DSC, TGA, and dilatometry with tools for model-fitting (JMAK, Kissinger), model-free (Friedman, KAS, OFW) analysis, data processing, and visualization.

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.

Package for fitting reinforcement learning model to behavior data under multi-armed bandits

Elegant Mathematica-style model manipulation, fitting and exploration in MATLAB.

GEARS a toolbox for Global parameter Estimation with Automated Regularisation via Sampling by Jake Alan Pitt and Julio R. Banga

Tutorial on Bayesian model fitting with (Py)VBMC.

HVAC model fitting tool from measured data, basic tkinter gui

Solution in the form of a tutorial article wherein the key decisions made in conducting a CFA are validated through recent literature and presented within a dynamic document framework.

Visualization and model fitting for Kobe Bryant shots over the course of his career. Data comes from the Kobe Bryant Shot Selection Kaggle Competition

Investigated factors that affect the likelihood of charity donations being made based on real census data. Developed a naive classifier to compare testing results to. Trained and tested several supervised machine learning models on preprocessed census data to predict the likelihood of donations. Selected the best model based on accuracy, a modified F-scoring metric, and algorithm efficiency.

Wrangled real estate data from multiple sources and file formats, brought it into a single consistent form and analysed the results.