Package: CVarE
Type: Package
Title: Conditional Variance Estimator for Sufficient Dimension Reduction
Version: 1.1
Date: 2021-03-09
Maintainer: Daniel Kapla <daniel@kapla.at>
Author: Daniel Kapla [aut, cph, cre],
  Lukas Fertl [aut, cph],
  Efstathia Bura [ctb]
Authors@R: c(
  person("Daniel", "Kapla", role = c("aut", "cph", "cre"), email = "daniel@kapla.at"),
  person("Lukas", "Fertl", role = c("aut", "cph")),
  person("Efstathia", "Bura", role = "ctb")
  )
Description: Implementation of the Conditional Variance Estimation (CVE) method
  from Fertl and Bura (2021) <arXiv:2102.08782> and the Ensemble Conditional
  Variance Estimation (ECVE) method from Fertl and Bura (2021) <arXiv:2102.13435>.
  CVE and ECVE are Sufficient Dimension Reduction (SDR) methods
  in regressions with continuous response and predictors. CVE applies to general
  additive error regression models while ECVE generalizes to non-additive error
  regression models. They operate under the assumption that the predictors can
  be replaced by a lower dimensional projection without loss of information.
  It is a semiparametric forward regression model based exhaustive sufficient
  dimension reduction estimation method that is shown to be consistent under mild
  assumptions.
License: GPL-3
Contact: <daniel@kapla.at>
URL: https://git.art-ist.cc/daniel/CVE
Encoding: UTF-8
NeedsCompilation: yes
Imports: stats, mda
RoxygenNote: 7.0.2