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