Case studies on model assessment, model selection and inference after model selection
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Updated
Jan 9, 2024 - HTML
Case studies on model assessment, model selection and inference after model selection
Estimating Copula Entropy (Mutual Information), Transfer Entropy (Conditional Mutual Information), and the statistics for multivariate normality test and two-sample test, and change point detection in Python
Projection predictive variable selection
Developer Version of the R package CAST: Caret Applications for Spatio-Temporal models
Boosting algorithms for fitting generalized linear, additive and interaction models to potentially high-dimensional data. The current relase version can be found on CRAN (http://cran.r-project.org/package=mboost).
BAS R package for Bayesian Model Averaging and Variable Selection
R package for estimating copula entropy (mutual information), transfer entropy (conditional mutual information), and the statistic for multivariate normality test and two-sample test
Awesome papers on Feature Selection
OmicSelector - Environment, docker-based application and R package for biomarker signiture selection (feature selection) & deep learning diagnostic tool development from high-throughput high-throughput omics experiments and other multidimensional datasets. Initially developed for miRNA-seq, RNA-seq and qPCR.
Data preparation for data science projects.
Penalized least squares estimation using the Orthogonalizing EM (OEM) algorithm
Boosting models for fitting generalized additive models for location, shape and scale (GAMLSS) to potentially high dimensional data. The current relase version can be found on CRAN (https://cran.r-project.org/package=gamboostLSS).
Stability Selection with Error Control
Performs Variables selection and model tuning for Species Distribution Models (SDMs). It provides also several utilities to display results.
Python library of algorithms for selecting diverse subsets of data for machine-learning.
Code and simulations using biologically annotated neural networks
sliced: scikit-learn compatible sufficient dimension reduction
Code for Variable Selection in Black Box Methods with RelATive cEntrality (RATE) Measures
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