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Title: Nonparametric, Tuning-Free Estimation of an S-Shaped Function
person("Oliver Y.", "Feng", email = "oyf20@cam.ac.uk", role = "aut"),
person("Yining", "Chen", email = "y.chen101@lse.ac.uk", role = c("aut", "cre")),
person("Qiyang", "Han", email = "qh85@stat.rutgers.edu", role = "aut"),
person("Raymond J.", "Carroll", email = "carroll@stat.tamu.edu", role = "aut"),
person("Richard J.", "Samworth", email = "r.samworth@statslab.cam.ac.uk", role = "aut"))
Description: 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>.

Title: Nonparametric, Tuning-Free Estimation of S-Shaped Functions
Description: Estimation of an S-shaped function and its corresponding inflection point via a least squares approach. A sequential mixed primal-dual based algorithm is implemented for the fast computation. Details can be found in Feng et al. (2021)  <arXiv:2107.07257>.

Description: Estimation of an S-shaped function and its corresponding inflection point via a least squares approach. A sequential mixed primal-dual based algorithm is implemented for the fast computation. Details can be found in Feng et al. (2022) <doi:10.1111/rssb.12481>.

Title: Bootstrap Stacking of Random Forest Models for Heterogeneous
Description: Generates and predicts a set of linearly stacked Random Forest models using bootstrap sampling. Individual datasets may be heterogeneous (not all samples have full sets of features). Contains support for parallelization but the user should register their cores before running. This is an extension of the method found in Matlock (2018) <doi:10.1186/s12859-018-2060-2>.
Imports: randomForest, foreach, dplyr, parallel, doParallel