Description: Provides a common framework for optimization of black-box
functions for other packages, e.g. 'mlr3tuning' or 'mlr3fselect'. It
offers various optimization methods e.g. grid search, random search,
generalized simulated annealing and iterated racing.
Suggests: adagio, GenSA, irace, knitr, nloptr, progressr, rmarkdown,
'bibentries.R' 'helper.R' 'helper_irace.R' 'sugar.R' 'zzz.R'

Suggests: adagio, emoa, GenSA, irace, knitr, nloptr, progressr,
Collate: 'Archive.R' 'ArchiveBest.R' 'Codomain.R' 'Objective.R'

Suggests: adagio, emoa, GenSA, irace (>= 3.5), knitr, nloptr,
progressr, rmarkdown, testthat (>= 3.0.0)

Title: Bessel and Beta Regressions via Expectation-Maximization
Authors@R: c(person("Vinicius", "Mayrink", role = c("aut","cre"), email = "vdinizm@gmail.com"),
person("Wagner", "Barreto-Souza", role = c("aut")),
person("Alexandre", "Simas", role = c("aut")))
Description: Functions to fit, via Expectation-Maximization (EM) algorithm, the Bessel and Beta regressions to a data set with a bounded continuous response variable. The Bessel regression is a new and robust approach proposed in the literature. The EM version for the well known Beta regression is another major contribution of this package. See the reference Barreto-Souza, Mayrink and Simas (2020) <arXiv:2003.05157> for details.
Imports: betareg, Formula, expint, statmod

Imports: pbapply, Formula, expint, statmod
Author: Vinicius Mayrink [cre, aut] (<https://orcid.org/0000-0002-5683-8326>),

Description: Functions to fit, via Expectation-Maximization (EM) algorithm, the Bessel and Beta regressions to a data set with a bounded continuous response variable. The Bessel regression is a new and robust approach proposed in the literature. The EM version for the well known Beta regression is another major contribution of this package. See details in the references Barreto-Souza, Mayrink and Simas (2022) <doi:10.1111/anzs.12354> and Barreto-Souza, Mayrink and Simas (2020) <arXiv:2003.05157>.

Title: Bayesian Methods and Graphical Model Structures for Statistical
Description: A class of frequently used Bayesian parametric and nonparametric model structures, as well as a set of tools for common analytical tasks. Structures include Gaussian and Normal-Inverse-Wishart conjugate structure, Gaussian and Normal-Inverse-Gamma conjugate structure, Categorical and Dirichlet conjugate structure, Dirichlet Process on positive integers, Dirichlet Process in general, Hierarchical Dirichlet Process ... Tasks include updating posteriors, calculating marginal likelihood, calculating posterior predictive densities, sampling from posterior predictive distributions, calculating "Maximum A Posteriori" (MAP) estimates ... See Murphy (2012, <doi:10.1080/09332480.2014.914768>), Koller and Friedman (2009, <doi:10.1017/s0269888910000275>) and Andrieu, de Freitas, Doucet and Jordan (2003, <doi:10.1023/A:1020281327116>) for more information. See <https://chenhaotian.github.io/Bayesian-Bricks/> to get started.
Author: Haotian Chen [aut, cre] (<https://orcid.org/0000-0001-9751-2093>)