Efficient Bayesian Algorithms for Binary and Categorical Data
Regression Models
c(person("Gregor","Zens",role=c("aut","cre"),email="gzens@wu.ac.at"),
person("Sylvia","Frühwirth-Schnatter", role="aut"),
person("Helga","Wagner", role="aut"))
Gregor Zens [aut, cre],
Sylvia Frühwirth-Schnatter [aut],
Helga Wagner [aut]
Efficient Bayesian implementations of probit, logit, multinomial logit and binomial logit models. Functions for plotting and tabulating the estimation output are available as well. Estimation is based on Gibbs sampling where the Markov chain Monte Carlo algorithms are based on the latent variable representations and marginal data augmentation algorithms outlined in Frühwirth-Schnatter S., Zens G., Wagner H. (2020) <arXiv:2011.06898>.
ggplot2, knitr, matrixStats, mnormt, pgdraw, reshape2, coda,
truncnorm

Unsupervised Photometric Membership Assignment in Stellar
Clusters
An implementation of the UPMASK method for performing membership
assignment in stellar clusters in R. It is prepared to use photometry and
spatial positions, but it can take into account other types of data. The
method is able to take into account arbitrary error models, and it is
unsupervised, data-driven, physical-model-free and relies on as few
assumptions as possible. The approach followed for membership assessment is
based on an iterative process, principal component analysis, a clustering
algorithm and a kernel density estimation.

c(
person("Alberto", "Krone-Martins", email = "algol@sim.ul.pt", role = c("aut", "cre")),
person("Andre", "Moitinho", email = "andre@sim.ul.pt", role = c("aut")),
person("Eduardo", "Bezerra", email = "ebezerra@cefet-rj.br", role = c("ctb")),
person("Leonardo", "Lima", email = "leonardo.lima@cefet-rj.br", role = c("ctb")),
person("Tristan", "Cantat-Gaudin", email = "tcantat@fqa.ub.edu", role = c("ctb"))
)
An implementation of the UPMASK method for performing membership
assignment in stellar clusters in R. It is prepared to use photometry and
spatial positions, but it can take into account other types of data. The
method is able to take into account arbitrary error models, and it is
unsupervised, data-driven, physical-model-free and relies on as few
assumptions as possible. The approach followed for membership assessment is
based on an iterative process, dimensionality reduction, a clustering
algorithm and a kernel density estimation.
parallel, MASS, RSQLite, DBI, dimRed, loe
Alberto Krone-Martins [aut, cre],
Andre Moitinho [aut],
Eduardo Bezerra [ctb],
Leonardo Lima [ctb],
Tristan Cantat-Gaudin [ctb]