variable-selection

There are 4 repositories under variable-selection topic.

avehtari / modelselection
Case studies on model assessment, model selection and inference after model selection
bayesian variable-selection stan rstanarm
Language:HTML 171
majianthu / pycopent
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
copula-entropy mutual-information causal-discovery variable-selection granger-causality transfer-entropy correlation normality-test information-theory causality python copula hypothesis-testing two-sample-test conditional-independence-test conditional-mutual-information change-point-detection entropy change-detection changepoint
Language:Python 166
Sundar0989 / WOE-and-IV
weight-of-evidence information-value variable-selection woe iv woe-and-iv
Language:Jupyter Notebook 136
HannaMeyer / CAST
Developer Version of the R package CAST: Caret Applications for Spatio-Temporal models
caret machine-learning autocorrelation spatial spatio-temporal overfitting feature-selection variable-selection predictive-modeling
Language:R 115
stan-dev / projpred
Projection predictive variable selection
rstanarm statistics bayesian variable-selection stan bayesian-inference r-package bayes
Language:R 112
boost-R / mboost
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).
mboost cran boosting-algorithms tutorials machine-learning r-language variable-selection modelling gam glm r-package
Language:HTML 75
merliseclyde / BAS
BAS R package for Bayesian Model Averaging and Variable Selection
bayesian bayesian-inference cran generalized-linear-models linear-regression logistic-regression mcmc model-selection poisson-regression predictive-modeling r-package regression variable-selection
Language:R 44
majianthu / copent
R package for estimating copula entropy (mutual information), transfer entropy (conditional mutual information), and the statistic for multivariate normality test and two-sample test
copula entropy mutualinf copula-entropy causal-discovery variable-selection transfer-entropy granger-causality correlation normality-test information-theory mutual-information r causality two-sample-test conditional-independence-test conditional-mutual-information change-point-detection
Language:R 42
mlpapers / feature-selection
Awesome papers on Feature Selection
feature-selection feature-importance variable-selection mutual-information correlation dimensionality-reduction awesome awesome-list
36
OmicSelector
kstawiski / OmicSelector
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.
feature-selection omics r r-package classification data-science data-mining bioinformatics bioinformatics-pipeline biomarkers biomarker-discovery docker-image variable-selection deep-learning deep-neural-networks next-generation-sequencing neural-network rna-seq qpcr mirna
Language:R 35
ELToulemonde / dataPreparation
Data preparation for data science projects.
data-preparation data-science date-conversion variable-selection variable-elimination r data-preprocessing speed
Language:R 31
jaredhuling / oem
Penalized least squares estimation using the Orthogonalizing EM (OEM) algorithm
cran group-lasso lasso machine-learning mcp oem oem-algorithm penalized-regression r-package scad variable-selection
Language:C++ 27
boost-R / gamboostLSS
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).
gamboostlss cran gamlss r-package variable-selection machine-learning boosting-algorithms r-language
Language:R 26
hofnerb / stabs
Stability Selection with Error Control
stability-selection cran r-package r-language variable-selection variable-importance machine-learning resampling
Language:R 26
Sundar0989 / Variable-Selection-Using-Python
variable-selection python feature-selection voting
Language:Jupyter Notebook 26
ConsBiol-unibern / SDMtune
Performs Variables selection and model tuning for Species Distribution Models (SDMs). It provides also several utilities to display results.
hyperparameter-tuning species-distribution-modelling variable-selection
Language:R 25
theochem / Selector
Python library of algorithms for selecting diverse subsets of data for machine-learning. Webserver is hosted at https://huggingface.co/spaces/QCDevs/Selector.
chemical-diversity compound-selection maximum-diversity-molecule maximum-dissimilarity-search variable-selection compound-acquisition chemical-library-design
Language:Jupyter Notebook 22
BANNs
lcrawlab / BANNs
Code and simulations using biologically annotated neural networks
bayesian-statistics genetics gwas gwas-tools interpretability machine-learning neural-networks variable-selection
Language:HTML 21
boost-R / FDboost
Boosting Functional Regression Models. The current release version can be found on CRAN (http://cran.r-project.org/package=FDboost).
cran boosting-algorithms boosting function-on-function-regression scalar-on-function-regression function-on-scalar-regression machine-learning variable-selection
Language:R 20
joshloyal / sliced
sliced: scikit-learn compatible sufficient dimension reduction
machine-learning machine-learning-algorithms dimension-reduction dimensionality-reduction feature-selection variable-selection python sufficient-dimension-reduction
Language:Python 19
lorinanthony / RATE
Code for Variable Selection in Black Box Methods with RelATive cEntrality (RATE) Measures
bayesian-neural-networks deep-neural-networks genetics gp-regression kullback-leibler-divergence machine-learning neural-networks variable-importance variable-selection
Language:Jupyter Notebook 19
majianthu / aps2020
Code for the paper 'Variable Selection with Copula Entropy' published on Chinese Journal of Applied Probability and Statistics
copula-entropy distance-correlation feature-engineering feature-selection hsic mutual-information variable-importance variable-selection
Language:R 18
Mamba413 / bess
Best Subset Selection algorithm for Regression, Classification, Count, Survival analysis
variable-selection feature-selection regression-models classification-model survival-analysis poisson-regression sparse-linear-systems
Language:C++ 17
meowoodie / Regularized-RBM
A regularized version of RBM for unsupervised feature selection.
machine-learning statistics python data-mining embeddings skipgram tensorflow rbm events l1 regularization variable-selection unsupervised-learning feature-selection icassp-2018 icassp
Language:Python 13
nanxstats / msaenet
🧲 Multi-step adaptive estimation for reducing false positive selection in sparse regressions
high-dimensional-data variable-selection linear-regression machine-learning false-positive-control
Language:R 13
ducnh279 / Variable-Selection-Network-with-PyTorch
Variable Selection Network with PyTorch
deep-learning neural-networks python pytorch variable-selection
Language:Jupyter Notebook 11
lorinanthony / BAKR
Code and Simulations using Bayesian Approximate Kernel Regression (BAKR)
nonlinear-regression variable-selection gwas genetics bayesian-statistics kernel-regression
Language:C++ 11
hruffieux / locus
locus R package - Large-scale variational inference for variable selection in sparse multiple-response regression
variable-selection joint-models high-dimensional-data sparse-regression variational-inference biostatistics
Language:R 10
emirhankartal-py / python_bidirectional_stepwise_selection
Automated Bidirectional Stepwise Selection On Python
feature-selection feature python stepwise-regression bidirectional features variable-selection variable-importance forward backward machine-learning data science
Language:Python 9
slipguru / l1l2py
l1l2py is a Python package to perform variable selection by means of l1l2 regularization with double optimization.
python machinelearning variable-selection feature-selection fista l1l2-regularization
Language:Python 9
biona001 / GhostKnockoffGWAS
Knockoff-based analysis of GWAS summary statistics data
conditional-independence false-discovery-rate fdr genomics gwas knockoffs summary-statistics variable-selection
Language:Julia 8
SINATRA
lcrawlab / SINATRA
A statistical framework for feature selection and association mapping with 3D shapes
3d-reconstruction 3d-shapes centrality-measures euler-characteristics nonparametric-regression r r-package shape-analysis shape-reconstruction topological-data-analysis topological-features topological-summary-statistics variable-selection
Language:R 8
biona001 / Knockoffs.jl
Variable Selection with Knockoffs
bioinformatics conditional-independence conditional-independence-test false-discovery-rate fdr feature-selection genomics group-knockoffs hypothesis-testing knockoff-filter knockoffs lasso model-selection multiple-knockoffs variable-selection
Language:Julia 7
htpusa / scanoncorr
Sparse canonical correlation analysis
canonical-correlation-analysis matlab multivariate-analysis variable-selection sparse-canonical-correlation
Language:MATLAB 7
JacobSeedorff21 / BranchGLM
Efficient Variable Selection for GLMs in R
generalized-linear-models r-package regression statistics subset-selection variable-selection
Language:R 7
TuoooLiu666 / Applied-Biostatistics-Projects
This repository commits to the application of biostatistics knowledge on clinical, randomized trials and observational studies.
logistic-regression model-diagnostics survival-analysis variable-selection missing-value-imputation mixed-effects-models propensity-score-matching
Language:HTML 7