covr, gridExtra, knitr, pdp, RUnit, splines, tinytest, vip,
viridis

Automated Boosted Regression Tree Modelling and Mapping Suite
c(
person("Simon", "Dedman", email = "simondedman@gmail.com", role = c("aut", "cre")),
person("Hans", "Gerritsen", role = "aut"))
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
'Working Guide' <doi:10.1111/j.1365-2656.2008.01390.x>; workflow follows
Appendix S3. See <http://www.simondedman.com/> for published guides and
papers using this package.
Simon Dedman [aut, cre],
Hans Gerritsen [aut]

Writes SAS code to get predicted values from every tree of a gbm.object.

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.

Estimation and analysis of group-based multivariate trajectory models (Nagin, 2018 <doi:10.1177/0962280216673085>; Magrini, 2022 <doi:10.1007/s10182-022-00437-9>). The package implements an Expectation-Maximization (EM) algorithm allowing unbalanced panel and missing values, and provides several functionalities for prediction and graphical representation.

Basic infrastructure and several algorithms for 1d-4d bin packing
problem. This package provides a set of c-level classes and solvers for
1d-4d bin packing problem, and an r-level solver for 4d bin packing problem,
which is a wrapper over the c-level 4d bin packing problem solver.
The 4d bin packing problem solver aims to solve bin packing problem, a.k.a
container loading problem, with an additional constraint on weight.
Given a set of rectangular-shaped items, and a set of rectangular-shaped bins
with weight limit, the solver looks for an orthogonal packing solution
such that minimizes the number of bins and maximize volume utilization.
Each rectangular-shaped item i = 1, .. , n is characterized by length l_i,
depth d_i, height h_i, and weight w_i, and each rectangular-shaped bin
j = 1, .. , m is specified similarly by length l_j, depth d_j, height h_j,
and weight limit w_j.
The item can be rotated into any orthogonal direction, and no further
restrictions implied.