A document introducing generalized additive models.📈
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Updated
Jan 15, 2024 - R
A document introducing generalized additive models.📈
By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.
A workshop on using generalized additive models and the mgcv package.
R Package: Regularized Principal Component Analysis for Spatial Data
Computational Methods for Numerical Analysis
Training ensemble machine learning classifiers, with flexible templates for repeated cross-validation and parameter tuning
R package: Linear Splines with Convenient Parameterizations
R-script for the publication Testosterone and specific symptoms of depression: Evidence from NHANES 2011–2016, https://doi.org/10.1016/j.cpnec.2021.100044
Regularised B-splines projected Gaussian Process priors
See https://biometris.github.io/LMMsolver for a full description
R and C++ code for performing posterior inference for Bayesian Conditional Transformation models illustrated for three different applications.
Negative Binomial Additive Model for RNASeq Data
Repo for the famous SA Heart Disease dataset. We demonstrate the importance of considering non-linear terms, and using splines in R.
we fit various splines to model the COVID-19 daily positive case numbers in Florida from 3/3/20 – 3/7/21.
Orthonormal Basis Selection using Machine Learning. https://ranibasna.github.io/ddk/
Please read the readme.md file
R Package: Regularized Principal Component Analysis for Spatial Data
Supervised learning and unsupervised in R, with a focus on regression and classification methods.
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