FSDAM: Forward Stepwise Deep Autoencoder-Based Monotone NLDR

FS-DAM performs feature extraction through latent variables identification. Implementation is based on autoencoders with monotonicity and orthogonality constraints.

Version:	2020.11-18
Depends:	R (≥ 3.5.0)
Imports:	kyotil, reticulate (≥ 1.10)
Suggests:	R.rsp, RUnit
Published:	2020-11-20
Author:	Youyi Fong [cre], Jun Xu [aut]
Maintainer:	Youyi Fong <youyifong at gmail.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
Materials:	ChangeLog
CRAN checks:	FSDAM results

Documentation:

Reference manual:	FSDAM.pdf
Vignettes:	Fitting Threshold Regression Models Using chngpt

Downloads:

Package source:	FSDAM_2020.11-18.tar.gz
Windows binaries:	r-devel: FSDAM_2020.11-18.zip, r-release: FSDAM_2020.11-18.zip, r-oldrel: FSDAM_2020.11-18.zip
macOS binaries:	r-release (arm64): FSDAM_2020.11-18.tgz, r-oldrel (arm64): FSDAM_2020.11-18.tgz, r-release (x86_64): FSDAM_2020.11-18.tgz, r-oldrel (x86_64): FSDAM_2020.11-18.tgz

Linking:

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