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GEInter
View on CRAN: Click
here
Download and install GEInter package within the R console
Install from CRAN:
install.packages("GEInter")
Install from Github:
library("remotes")
install_github("cran/GEInter")
Install by package version:
library("remotes")
install_version("GEInter", "0.3.2")
Attach the package and use:
library("GEInter")
Maintained by
Xing Qin
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-01-21
Latest Update: 2022-05-19
Description:
Description: For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that gene-environment interactions play important roles beyond the main genetic and environmental effects. In practical interaction analyses, outliers in response variables and covariates are not uncommon. In addition, missingness in environmental factors is routinely encountered in epidemiological studies. The developed package consists of five robust approaches to address the outliers problems, among which two approaches can also accommodate missingness in environmental factors. Both continuous and right censored responses are considered. The proposed approaches are based on penalization and sparse boosting techniques for identifying important interactions, which are realized using efficient algorithms. Beyond the gene-environment analysis, the developed package can also be adopted to conduct analysis on interactions between other types of low-dimensional and high-dimensional data. (Mengyun Wu et al (2017), ; Mengyun Wu et al (2017), ; Yaqing Xu et al (2018), ; Yaqing Xu et al (2019), ; Mengyun Wu et al (2021), ).
How to cite:
Xing Qin (2021). GEInter: Robust Gene-Environment Interaction Analysis. R package version 0.3.2, https://cran.r-project.org/web/packages/GEInter
Previous versions and publish date:
Other packages that cited GEInter R package
View GEInter citation profile
Other R packages that GEInter depends,
imports, suggests or enhances
Functions, R codes and Examples using
the GEInter R package
Some associated functions: AR . Augmented.data . BLMCP . HNSCC . Miss.boosting . PTReg . QPCorr.matrix . QPCorr.pval . RobSBoosting . Rob_data . bic.BLMCP . bic.PTReg . coef.BLMCP . coef.PTReg . coef.RobSBoosting . coef.bic.BLMCP . coef.bic.PTReg . plot.BLMCP . plot.Miss.boosting . plot.PTReg . plot.RobSBoosting . plot.bic.BLMCP . plot.bic.PTReg . predict.BLMCP . predict.Miss.boosting . predict.PTReg . predict.RobSBoosting . predict.bic.BLMCP . predict.bic.PTReg . simulated_data .
Some associated R codes: AR.R . Augmented.data.R . BLMCP.R . KMExpection.R . MCP_Hier.R . Miss.boosting.R . PTReg.R . QPCorr.matrix.R . QPCorr.pval.R . RobSBoosting.R . basis.matrix.R . bic.BLMCP.R . bic.PTReg.R . boosting.robust.spline.R . boosting_impute.R . coef.BLMCP.R . coef.PTReg.R . coef.RobSBoosting.R . coef.bic.BLMCP.R . coef.bic.PTReg.R . combine_results.R . cqpcorr.R . data.R . design.matrix.R . emprand.R . kernel_function.R . kmw.R . lambda_funtion.R . ls.spline.R . pen.ls.R . pen.m.R . plot.BLMCP.R . plot.Miss.boosting.R . plot.PTReg.R . plot.RobSBoosting.R . plot.bic.BLMCP.R . plot.bic.PTReg.R . predict.BLMCP.R . predict.Miss.boosting.R . predict.PTReg.R . predict.RobSBoosting.R . predict.bic.BLMCP.R . predict.bic.PTReg.R . predict_boosting2.R . ptr1.R . reweight.R . robust.spline.R . simulated_data.R . soft_threshold.R . which.column.R . Full GEInter package functions and examples
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