R package ldr: Methods for likelihood-based dimension reduction in regression. Functions, methods, and data sets for fitting likelihood-based dimension reduction in regression, using principal fitted components (pfc), likelihood acquired directions (lad), covariance reducing models (core).
Keywords for this software
References in zbMATH (referenced in 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
- Liu, Xiaoyu; Guillas, Serge: Dimension reduction for Gaussian process emulation: an application to the influence of bathymetry on tsunami heights (2017)
- Adragni, Kofi P.; Al-Najjar, Elias; Martin, Sean; Popuri, Sai K.; Raim, Andrew M.: Group-wise sufficient dimension reduction with principal fitted components (2016)
- Prendergast, Luke A.; Healey, Alan F.: Improving estimated sufficient summary plots in dimension reduction using minimization criteria based on initial estimates (2016)
- Adragni, Kofi P.; Karmakar, Moumita: A sequential test for variable selection in high dimensional complex data (2015)
- Kofi Adragni; Andrew Raim: ldr: An R Software Package for Likelihood-Based Sufficient Dimension Reduction (2014) not zbMATH