AshwiniKV/TEHReview---MRC-CTU

Review: Data-driven methodology for detecting treatment effect heterogeneity

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RuzhangZhao/BenchmarkHVG_old

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lorinanthony/RATE

Code for Variable Selection in Black Box Methods with RelATive cEntrality (RATE) Measures

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sestelo/fwdselect

An R package for selecting variables in regression models

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adrian-io/pisa-clustered-ols

Prediction of all countries’ PISA scores with macroeconomic indicators using pooled OLS with clustered standard errors

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majianthu/aps2020

Code for the paper 'Variable Selection with Copula Entropy' published on Chinese Journal of Applied Probability and Statistics

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MalikHebbat/Thesis---Code

Code for Master Thesis

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S-Soluel/STAT-172-Final-Project

Final Project: Predictive and descriptive analysis, interpretations, and recommendations based on Austin Animal Center data on cats housed at the shelter. The main focus of this analysis is to determine which factors affect a cat's chances of being adopted or returned to their owner.

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JorgeCastilloMateo/GEVSSVS

GEV-SSVS regression

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abhianik/dpdSIS

Robust Sure Independence Screening using the Minimum Density Power Divergence Estimators

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matteopelagatti/indeptest

Code and data of the paper "A nonparametric test of independence between two random variables of any kind"

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nilotpalsanyal/GWASinlps

R package for Non-local Prior Based Iterative Variable Selection for Genome-Wide Association Studies, or Other High-Dimensional Data

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LyonsZhang/BAZE

Bayesian zero-constrained regression with compositional covariates

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marinavannucci/dmbvs Fork of duncanwadsworth/dmbvs

R/C code for Bayesian variable selection for Dirichlet-multinomial regression models. Accompany paper: Wadsworth et al. (2016). An Integrative Bayesian Dirichlet-Multinomial Regression Model for the Analysis of Taxonomic Abundances in Microbiome data. BMC Bioinformatics 18:94.

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eyllcyldrm/MantarSiniflandirma

SVC and KNN methods were used to predict whether mushrooms are poisonous or edible according to their properties. Random forest and chi-square variable selection methods were applied and the 10-fold cross validation method was used and f1 scores were calculated by re-estimating. Finally, the models were compared.

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liangyuanhu/Variable-selection-w-missing-data

A general variable selection approach in the presence of missing data in both covariates and outcomes. This approach exploits the flexibility of machine learning modeling techniques and bootstrap imputation, which is amenable to nonparametric methods in which the effect sizes of predictor variables are not naturally defined as in parametric models. Six methods are considered and compared: XGBoost, Random Forests, Conditional Random Forests, Bayesian Additive Regression Trees, lasso, stepwise backward selection.

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humburg/miPredict

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Heming0425/Model_Confidence_Bound

The MCB for variable selection identifies two nested models (upper and lower confidence bound models) containing the true model at a given confidence level.

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meowoodie/Regularized-RBM

A regularized version of RBM for unsupervised feature selection.

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steve303/stat420_variableSelection

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nanxstats/bcpm-msaenet

Solution for the precisionFDA Brain Cancer Predictive Modeling Challenge using msaenet

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elifdogandar/DataAnalysisandLinearModels-R

Exploratory data analysis, missing value imputation and linear models on carnicoma data

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stla/gfiUltra

Generalized fiducial inference for ultrahigh-dimensional regression.

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haziqj/phd-thesis

My PhD thesis: Regression modelling using priors depending on Fisher information covariance kernels (I-priors)

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scabras/varseldmmp

DPMP prior on model space

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weichengv/genee

A Recalibrated Hypothesis Test for SNP-Level Summary Statistics

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weichengv/BANNs

Code and Simulations using Biologically Annotated Neural Networks (BANNs)

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MinhDg00/penalized-mle-with-reparameterization

implement BCD to find l1-penalized MLE with appropriate parametrization multivariate GLM

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sarafinmahtab/MathJsWrapper

A wrapper of the mathjs.org (https://mathjs.org/) JavaScript library for Android to evaluate math expressions.

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alaradirik/variable-selection-using-R

R script to rank and select variables based on their importance/predictive power

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jkapila/vcvn

R package for Variable Selection, Curve Fitting, Variable Conversion and Normalisation

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aakbarie/Predicting_home_prices

Various variable selection methods are explored

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hanfu-bios/varsel

R package for generalized knockoffs filter for controlled variable selection

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