{mvgam} R 📦 to fit Dynamic Bayesian Generalized Additive Models for time series analysis and forecasting
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Updated
Jul 23, 2024 - R
{mvgam} R 📦 to fit Dynamic Bayesian Generalized Additive Models for time series analysis and forecasting
Functions for using GAMs to model time series
This is the repository used to make the project titled 'Grass Pollen in Cape Town: A Comparison of Generalised Additive Models and Random Forests' by Sky Cope and Chloë Stipinovich.
Prediction of the adjusted electricity sales using simple machine learning algorithms like linear regression, GAMs, MARS and Random Forests
Shiny App to visualise and fit a GAM
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