Maximum a posteriori estimation for linear and generalized
linear mixed-effects models in a Bayesian setting. Extends
'lme4' by Douglas Bates, Martin Maechler, Ben Bolker, and Steve Walker.

c(
person("Vincent", "Dorie", role = c("aut", "cre"), email = "vdorie@gmail.com", comment = c(ORCID = "0000-0002-9576-3064")),
person("Douglas", "Bates", role = "ctb", comment = c("lme4 non-modular functions", ORCID = "0000-0001-8316-9503")),
person("Martin", "Maechler", role = "ctb", comment = c("lme4 non-modular functions", ORCID = "0000-0002-8685-9910")),
person("Ben", "Bolker", role = "ctb", comment = c("lme4 non-modular functions", ORCID = "0000-0002-2127-0443")),
person("Steven", "Walker", role = "ctb", comment = c("lme4 non-modular functions", ORCID = "0000-0002-4394-9078")))
Maximum a posteriori estimation for linear and generalized linear mixed-effects models in a Bayesian setting, implementing the methods of Chung, et al. (2013) <doi:10.1007/s11336-013-9328-2>. Extends package 'lme4' (Bates, Maechler, Bolker, and Walker (2015) <doi:10.18637/jss.v067.i01>).
Vincent Dorie [aut, cre] (<https://orcid.org/0000-0002-9576-3064>),
Douglas Bates [ctb] (lme4 non-modular functions,
<https://orcid.org/0000-0001-8316-9503>),
Martin Maechler [ctb] (lme4 non-modular functions,
<https://orcid.org/0000-0002-8685-9910>),
Ben Bolker [ctb] (lme4 non-modular functions,
<https://orcid.org/0000-0002-2127-0443>),
Steven Walker [ctb] (lme4 non-modular functions,
<https://orcid.org/0000-0002-4394-9078>)

Data Files and Functions Accompanying the Book "Bayesian Data
Analysis in Ecology using R, BUGS and Stan"
Fraenzi Korner-Nievergelt, Tobias Roth, Stefanie von Felten, Jerome Guelat, Bettina Almasi, Pius Korner-Nievergelt
Data files and functions accompanying the book Korner-Nievergelt, Roth, von Felten, Guelat, Almasi, Korner-Nievergelt (2015) "Bayesian Data Analysis in Ecology using R, BUGS and Stan", Elsevier, New York.

A Simple S3 Class for Representing Vectors of Binary Data
('BLOBS')
c(
person("Hadley", "Wickham", , "hadley@rstudio.com", role = c("aut", "cre")),
person("RStudio", role = "cph")
)
R's raw vector is useful for storing a single binary object.
What if you want to put a vector of them in a data frame? The blob
package provides the blob object, a list of raw vectors, suitable for
use as a column in data frame.