Sshaped: Nonparametric, Tuning-Free Estimation of an S-Shaped Function

Estimation of an S-shaped function and its corresponding inflection point via a least squares approach. A sequential mixed primal-dual bases algorithm is implemented for the fast computation of the estimator. The same algorithm can also be used to solve other shape-restricted regression problems, such as convex regression. For more details, see the PhD thesis of Feng (2021) <https://www.dpmms.cam.ac.uk/~oyf20/Thesis-oyf20-final.pdf>.

Version: 0.99
Depends: R (≥ 3.4.0)
Imports: Rcpp (≥ 1.0.5)
LinkingTo: Rcpp, RcppArmadillo
Published: 2021-03-02
Author: Oliver Y. Feng [aut], Yining Chen [aut, cre], Qiyang Han [aut], Raymond J. Carroll [aut], Richard J. Samworth [aut]
Maintainer: Yining Chen <y.chen101 at lse.ac.uk>
License: GPL-2
NeedsCompilation: yes
CRAN checks: Sshaped results

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Reference manual: Sshaped.pdf
Package source: Sshaped_0.99.tar.gz
Windows binaries: r-devel: Sshaped_0.99.zip, r-devel-UCRT: Sshaped_0.99.zip, r-release: Sshaped_0.99.zip, r-oldrel: Sshaped_0.99.zip
macOS binaries: r-release (arm64): Sshaped_0.99.tgz, r-release (x86_64): Sshaped_0.99.tgz, r-oldrel: Sshaped_0.99.tgz

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