A workshop on using generalized additive models and the mgcv package.
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Updated
Feb 20, 2019 - R
A workshop on using generalized additive models and the mgcv package.
By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.
A python package for penalized generalized linear models that supports fitting and model selection for structured, adaptive and non-convex penalties.
Nonparametric regression and prediction using the highly adaptive lasso algorithm
Penalized least squares estimation using the Orthogonalizing EM (OEM) algorithm
Network-Based Regularization for Generalized Linear Models
LASSOPACK: Stata module for lasso, square-root lasso, elastic net, ridge, adaptive lasso estimation and cross-validation
Repo to keep track of work done in Dr. Robert McCulloch's Graduate Machine Learning course
Multi-source sparse Tweedie modelling
Biomarker selection in penalized regression models
an extremely basic Julia implementation of the Orthogonalizing EM (OEM) algorithm for penalized regression
Raw files for a document providing an overview of mixed models from varying perspectives.
Regression models for "epigenetic clock" estimation of canine chronological age
A Julia module that implements the (normalized) iterative hard thresholding algorithm(IHT) of Blumensath and Davies. IHT performs feature selection akin to LASSO- or MCP-penalized regression using a greedy selection approach.
University of Utah—MKTG 6600: Business Algorithms | Taken: Fall 2020
FAST Change Point Detection in R
We explored various approaches to deal with high-dimensional data in this study, and we compared them using simulation and soil datasets. We discovered that grouping had a significant impact on model correctness and error reduction. For the core projection step, we first looked at the properties of all the algorithms and how they function to com…
Experiments for Binarsity: a penalization for one-hot encoded features
An exploration in how discussion forum data can be used to measure faculty engagement and its effect on student outcomes
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