Packaged: 2021-11-24 09:04:32 UTC; Alessandro

Description: 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.
Packaged: 2022-03-05 14:09:27 UTC; Alessandro

Description: 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,
The item can be rotated into any orthogonal direction, and no further
Depends: R (>= 3.0.0), magrittr, data.table

Title: Package for Generalized Birnbaum-Saunders Distributions
Author: Michelli Barros <michelli.karinne@gmail.com>, Victor Leiva
<victor.leiva@uv.cl, victor.leiva@yahoo.com> and Gilberto A.
Description: A collection of utilities for analyzing censored and
uncensored data from generalized Birnbaum-Saunders

Title: Inference of Ploidy from (Genotyping-by-Sequencing) GBS Data
Description: Functions for inference of ploidy from (Genotyping-by-sequencing) GBS data, including a function to infer allelic ratios and allelic proportions in a Bayesian framework.

Title: Hyperparameter Search for Gradient Boosted Trees
Maintainer: Waley W. J. Liang <wliang10@gmail.com>
Description: An implementation of hyperparameter optimization for Gradient
Boosted Trees on binary classification and regression problems. The current
version provides two optimization methods: active learning and random search.
Imports: doParallel, doRNG, foreach, gbm, xgboost, earth