Title: Assessing Surrogacy with a Censored Outcome
person(given = "Xuan", family = "Wang", role = c("aut"),email = "xwang@hsph.harvard.edu"),
person(given = "Clara-Lea", family = "Bonzel", role = c("aut"), email = "clbonzel@hsph.harvard.edu"),
person(given = "Tianxi", family = "Cai", role = c("aut"), email = "tcai@hsph.harvard.edu"),
person(given = "Layla", family = "Parast", role = c("aut","cre"), email = "parast@austin.utexas.edu"),
person(family = "PARSE LTD", role = c("aut"), email = "software@parse-health.org")
Description: Identifies the optimal transformation of a surrogate marker and estimates the proportion of treatment explained (PTE) by the optimally-transformed surrogate at an earlier time point when the primary outcome of interest is a censored time-to-event outcome; details are described in Wang et al (2021) <doi:10.1002/sim.9185>.

Title: Optimal Trees Ensembles for Regression, Classification and Class
Author: Zardad Khan, Asma Gul, Aris Perperoglou, Osama Mahmoud, Werner Adler, Miftahuddin and Berthold Lausen
Description: Functions for creating ensembles of optimal trees for regression, classification (Khan, Z., Gul, A., Perperoglou, A., Miftahuddin, M., Mahmoud, O., Adler, W., & Lausen, B. (2019). (2019) <doi:10.1007/s11634-019-00364-9>) and class membership probability estimation (Khan, Z, Gul, A, Mahmoud, O, Miftahuddin, M, Perperoglou, A, Adler, W & Lausen, B (2016) <doi:10.1007/978-3-319-25226-1_34>) are given. A few trees are selected from an initial set of trees grown by random forest for the ensemble on the basis of their individual and collective performance. Three different methods of tree selection for the case of classification are given. The prediction functions return estimates of the test responses and their class membership probabilities. Unexplained variations, error rates, confusion matrix, Brier scores, etc. are also returned for the test data.

Description: Functions for creating ensembles of optimal trees for regression, classification and class membership probability estimation are given. A few trees are selected from an initial set of trees grown by random forest for the ensemble on the basis of their individual and collective performance. Trees are assessed on out-of-bag data and on an independent training data set for individual and collective performance respectively. The prediction functions return estimates of the test responses and their class membership probabilities. Unexplained variations, error rates, confusion matrix, Brier scores, etc. are also returned for the test data.