A document introducing generalized additive models.📈

By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.

Regression Spline Functions and Classes

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-script for the publication Testosterone and specific symptoms of depression: Evidence from NHANES 2011–2016, https://doi.org/10.1016/j.cpnec.2021.100044

See https://biometris.github.io/LMMsolver for a full description

R package: Linear Splines with Convenient Parameterizations

Regularised B-splines projected Gaussian Process priors

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/

Timeseries analysis of -omics data can be carried out by fitting spline curves to the data and using limma for hypothesis testing. For this, the right spline freedom and further hyperparameters must be identified, and the obtained hits clustered based on the spline shape.The R package SplineOmics streamlines this whole process and generates reports

Please read the readme.md file

Exploring how MLB player performance ages with splines and statistics.