Packaged: 2017-02-01 21:27:42 UTC; chrissles

Packaged: 2015-10-13 10:09:55 UTC; christina

Packaged: 2018-06-25 08:54:18 UTC; ChristinaYassouridis

car, fields, calibrate, cluster, sm, plyr
Suggests: scatterplot3d, Funclustering, funHDDC, testthat
Packaged: 2018-07-28 22:04:23 UTC; chrissles

Title: Easy Computation of Alpha Functional Diversity Indices
Description: Computes 4 alpha-functional diversity indices: Functional
Divergence FDiv, Function Evenness Feve, Functional Richness FRic, and Rao's
entropy Q (reviewed in Villéger et al. 2008 <doi:10.1890/07-1206.1>). Provides
efficient and modular functions to compute functional diversity indices.
Author: Matthias Grenié [aut, cre] (<https://orcid.org/0000-0002-4659-7522>),

Description: Computes 5 alpha-functional diversity indices: Functional
Divergence (FDiv), Function Evenness (FEve), Functional Richness (FRic),
Functional Dispersion (FDis) and Rao's entropy (Q) (reviewed in Villéger
et al. 2008 <doi:10.1890/07-1206.1>). Provides efficient and modular functions
to compute functional diversity indices.
Imports: future.apply, geometry, Matrix, vegan
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, memoise

et al. 2008 <doi:10.1890/07-1206.1>). Provides efficient, modular, and
parallel functions to compute functional diversity indices.

Title: Easy Computation of Functional Diversity Indices
Description: Computes six functional diversity indices. These are namely,
Functional Divergence (FDiv), Function Evenness (FEve), Functional Richness
(FRic), Functional Richness intersections (FRic_intersect), Functional
Dispersion (FDis), and Rao's entropy (Q) (reviewed in Villéger et al. 2008
<doi:10.1890/07-1206.1>). Provides efficient, modular, and parallel functions
Suggests: future, knitr, memoise, rmarkdown, testthat (>= 3.0.0)

Title: Functional Ensemble Survival Tree for Dynamic Prediction
Authors@R: c(person("Yijun", "Xie", role = c("aut", "cre"),
person("Graham A.", "Colditz", role = "aut",
A fast implementation of functional ensemble survival tree is provided to facilitate dynamic prediction with right-censored data. Multiple time-varying covariates can be accommodated via multivariate principal component analysis. These extracted features along with baseline covariates are nested within the ensemble survival tree where dynamic prediction can be done under user-specified sliding windows. Prediction accuracy measures, Area under the receiver operating characteristic (ROC) curve and Brier score, are provided in this package.
Imports: MFPCA, funData, ranger, survival, pec, tdROC, prodlim, Rdpack,