Authors@R: c(person("Stefano M.", "Iacus", email = "siacus@iq.harvard.edu", role = c("aut", "cre")),
Maintainer: Stefano M. Iacus <siacus@iq.harvard.edu>

Depends: nlme, lattice, randomForest, tcltk, combinat

Authors@R: c(person('Carlos', 'Relvas', role = c('aut'),
person('Andre', 'Fujita', role = c('aut','cre'),
Description: Functions to fit the 'CemCO' algorithm, a model-based (Gaussian)
removes/minimizes the effects of undesirable covariates during the
clustering process both in cluster centroid and in cluster covariance
structures (Relvas C. & Fujita A., (2020) <arXiv:2004.02333>).
Imports: clusteval, doParallel, nnet, rootSolve, foreach, MASS, mclust,
Packaged: 2020-04-24 13:41:30 UTC; carlosrelvas

Description: Conditional expectation manifolds are an approach to
Packaged: 2011-03-24 20:39:20 UTC; kristinpotter

Description: Conditional expectation manifolds are an approach to compute principal curves and surfaces.

Depends: R (>= 2.10), plotrix, RColorBrewer, rgl, vegan
Packaged: 2013-06-09 00:00:20 UTC; samgerber

Imports: plotrix, rgl, vegan, stats, graphics, grDevices
Packaged: 2015-11-11 19:55:08 UTC; samgerber

Title: Censored Regression with Smooth Terms
Description: Implementation of Tobit type I and type II families for censored regression using the 'mgcv' package, based on methods detailed in Woods (2016) <doi:10.1080/01621459.2016.1180986>.

Title: Estimation of the Time-Dependent ROC Curve and AUC for Censored
Author: Kassu Mehari Beyene and Anouar El Ghouch
Maintainer: Kassu Mehari Beyene <kasu.beyene@uclouvain.be>
Description: Contains functions to estimate a smoothed and a non-smoothed (empirical) time-
dependent ROC curve (receiver operating characteristic curve) and the corresponding
area under the ROC curve (AUC) for the right censored survival data, as described in
Beyene and El Ghouch (2019) <https://dial.uclouvain.be/pr/boreal/object/boreal:219643>.

Description: Contains functions to estimate a smoothed and a non-smoothed (empirical)
time-dependent ROC curve (receiver operating characteristic curve) and
the corresponding area under the ROC curve (AUC) and the optimal cutoff
point for the right and interval censored survival data, as described in
Beyene and El Ghouch (2019)<https://dial.uclouvain.be/pr/boreal/object/boreal:219643>.
Imports: Rcpp (>= 1.0.0), icenReg, condSURV, survival, stats, graphics