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.📈

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.

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

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

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

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 ag…

Bindings for Additive TidyModels

a database for pediatric drug safety signals

biostatistical workflows in R covering regression and classification models

Generalised Additive Extreme Value Models for Location, Scale and Shape

{ffc} R 📦 to fit dynamic functional time series models and produce functional forecasts

Negative Binomial Additive Model for RNASeq Data

Analysis of data from the Framingham Heart Study using generalized linear models.

Smooth Hazard Ratio Curves Taking a Reference Value

Data and code repository for Lavaca Bay nutrient loading project.

An R package which provides a a neural network framework based on Generalized Additive Models

Using the derivative of a GAM smoother to determine when a forest canopy has closed.

Is it possible to predict the popularity of a song based on its technical features? Which aspect of a song should an artist focus on to get more visibility? Could record labels or a new up-and-coming artist benefit from these data-driven and data-informed decisions?