Collection of must read papers for Data Science, or Machine Learning / Deep Learning Engineer

ggplot-based graphics and useful functions for GAMs fitted using the mgcv package

{mvgam} R 📦 to fit Dynamic Bayesian Generalized Additive Models for multivariate modeling and forecasting

A document introducing generalized additive models.📈

Ecological forecasting using Dynamic Generalized Additive Models with R 📦's {mvgam} and {brms}

An R package for estimating generalized additive mixed models with latent variables

This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.

Fit, evaluate, and visualise generalised additive models (GAMs) in native Julia

A workshop on using generalized additive models and the mgcv package.

ggplot-based graphics and useful functions for GAMs fitted using the mgcv package

A function that takes as input a cropped text line image, and outputs the dewarped image.

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Toolbox to estimate, automatically regularize, and select between Generalized Additive Mixed Models and their extensions in Python

R code to replicate analyses in Clark et al 2025 (Beyond single-species models: leveraging multispecies forecasts to navigate the dynamics of ecological predictability)

Workshop 8 - Generalized additive models (GAMs)

The dataset used for the "A non-contact SpO2 estimation using video magnification and infrared data" publication

The dataset used for the "Non-Contact Blood Pressure Estimation using infrared motion magnified facial video" publication. The code developed is to fit the data to the reference Blood Pressure values.

GAM workshop for NHS-R Community Conference 2023

An introduction to GAM(M)s

This repository contains the script and figures of the conference paper selected for presentation at the Latin American Conference of Computationa Intelligence 2018. The abstract of the paper is as follows: Crime is an important social and economic problem of South Africa. Though certain categories of crimes are of serious proportions, yet on aggregate the overall crime situation in the country has considerably improved in the last decade or so. A number of previous studies across other countries have shown a positive or negative relationship between crime and economic growth. On a microeconomic/provincial scale, this paper studies the relationship between various categories of crimes and economic growth using the non-linear modeling technique of Generalized Additive Models. Such a modeling approach helps in understanding how various categories of crimes complexly affect GDP.

Paper on identifying patterns in economic development using statistical learning

Bindings for Additive TidyModels

Resolution-independent normalization of Hi-C data

a database for pediatric drug safety signals

biostatistical workflows in R covering regression and classification models

Rcpp package implementing automatic smoothing for multiple generalized additive models.

A workshop on ecological forecasting with the {mvgam} package for the Ecological Society of Australia 2024 Conference

structured additive regression models talk

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Generalised Additive Extreme Value Models for Location, Scale and Shape