Author: Greg Ridgeway <gregridgeway@gmail.com> with contributions from

Depends: R (>= 2.9.0), survival, lattice, splines, parallel
Description: An implementation of extensions to Freund and

Imports: gridExtra, lattice, parallel, survival
Suggests: knitr, pdp, RUnit, splines, viridis
Description: An implementation of extensions to Freund and Schapire's AdaBoost
algorithm and Friedman's gradient boosting machine. Includes regression
methods for least squares, absolute loss, t-distribution loss, quantile
regression, logistic, multinomial logistic, Poisson, Cox proportional hazards
partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and
Learning to Rank measures (LambdaMart). Originally developed by Greg Ridgeway.
Author: Brandon Greenwell [aut, cre] (<https://orcid.org/0000-0002-8120-0084>),

Packaged: 2019-01-14 14:21:52 UTC; bgreenwell

Suggests: covr, gridExtra, knitr, pdp, RUnit, splines, tinytest, vip,

Title: Automated Boosted Regression Tree Modelling and Mapping Suite
person("Simon", "Dedman", email = "simondedman@gmail.com", role = c("aut", "cre")),
person("Hans", "Gerritsen", role = "aut"))
Description: Automates delta log-normal boosted regression tree abundance
prediction. Loops through parameters provided (LR (learning rate), TC (tree
complexity), BF (bag fraction)), chooses best, simplifies, & generates line,
dot & bar plots, & outputs these & predictions & a report, makes predicted
abundance maps, and Unrepresentativeness surfaces.
Package core built around 'gbm' (gradient boosting machine) functions in
'dismo' (Hijmans, Phillips, Leathwick & Jane Elith, 2020 & ongoing), itself
built around 'gbm' (Greenwell, Boehmke, Cunningham & Metcalfe, 2020 &
ongoing, originally by Ridgeway). Indebted to Elith/Leathwick/Hastie 2008
Appendix S3. See <http://www.simondedman.com/> for published guides and

Title: Convert GBM Object Trees to SAS Code
Maintainer: John R. Dixon <gbm2sas@gmail.com>
Description: Writes SAS code to get predicted values from every tree of a gbm.object.

Title: Group-Based Multivariate Trajectory Modeling
Description: Estimation and analysis of group-based multivariate trajectory models (Nagin, 2018 <doi:10.1177/0962280216673085>). The package implements an Expectation-Maximization (EM) algorithm allowing unbalanced panel and missing values, and provides several functionalities for prediction and graphical representation.
Packaged: 2021-11-23 13:43:04 UTC; Alessandro