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kernelPSI  

Post-Selection Inference for Nonlinear Variable Selection
View on CRAN: Click here


Download and install kernelPSI package within the R console
Install from CRAN:
install.packages("kernelPSI")

Install from Github:
library("remotes")
install_github("cran/kernelPSI")

Install by package version:
library("remotes")
install_version("kernelPSI", "1.1.1")



Attach the package and use:
library("kernelPSI")
Maintained by
Lotfi Slim
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-06-24
Latest Update:
Description:
Different post-selection inference strategies for kernelselection as described in kernelPSI a Post-Selection Inference Frameworkfor Nonlinear Variable Selection Slim et al. Proceedings of MachineLearning Research 2019 httpproceedings.mlr.pressv97slim19aslim19a.pdf. The strategies rest upon quadratic kernelassociation scores to measure the association between a given kernel and anoutcome of interest. The inference step tests for the joint effect of theselected kernels on the outcome. A fast constrained sampling algorithm isproposed to derive empirical p-values for the test statistics.
How to cite:
Lotfi Slim (2019). kernelPSI: Post-Selection Inference for Nonlinear Variable Selection. R package version 1.1.1, https://cran.r-project.org/web/packages/kernelPSI. Accessed 25 Jun. 2026.
Previous versions and publish date:
1.0.0 (2019-06-24 15:20), 1.1.0 (2019-09-08 19:00), 1.1.1 (2019-12-08 00:00)
Other packages that cited kernelPSI R package
View kernelPSI citation profile
Other R packages that kernelPSI depends, imports, suggests or enhances
Functions, R codes and Examples using the kernelPSI R package
Some associated functions: FOHSIC . HSIC . SKAT . adaFOHSIC . adaQ . anovaLR . forwardQ . kernelPSI . maxLR . pcaLR . quadHSIC . ridgeLR . sampleH . 
Some associated R codes: RcppExports.R . likelihood.R . skat.R . statistic.R .  Full kernelPSI package functions and examples
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