Solve the Empirical Bayes Normal Means Problem
c(person("Willwerscheid", "Jason", role = "aut"),
person("Stephens", "Matthew", role = "aut"),
person("Carbonetto", "Peter", role = c("aut", "cre"),
email = "peter.carbonetto@gmail.com"),
person("Goldstein", "Andrew", role = "ctb"))
Provides simple, fast, and stable functions to fit the normal
means model using empirical Bayes. For available models and details, see
function ebnm(). A comprehensive introduction to the package is provided
by Willwerscheid and Stephens (2021) <arXiv:2110.00152>.
stats, methods, ashr, mixsqp, truncnorm, trust, horseshoe,
deconvolveR, magrittr, rlang, dplyr, ggplot2
Willwerscheid Jason [aut],
Stephens Matthew [aut],
Carbonetto Peter [aut, cre],
Goldstein Andrew [ctb]

Implementation of the Empirical Bayes Method
Implements a Bayesian-like approach to the high-dimensional sparse linear regression problem based on an empirical or data-dependent prior distribution, which can be used for estimation/inference on the model parameters, variable selection, and prediction of a future response. The method was first presented in Martin, Ryan and Mess, Raymond and Walker, Stephen G (2017) <doi:10.3150/15-BEJ797>. More details focused on the prediction problem are given in Martin, Ryan and Tang, Yiqi (2019) <arXiv:1903.00961>.

Working with netCDF for Essential Biodiversity Variables
c(person(given = "Luise",
family = "Quoss",
role = c("aut", "cre"),
email = "luise.quoss@idiv.de",
comment = c(ORCID = "0000-0002-9910-1252")),
person(given = "Nestor",
family = "Fernandez",
role = c("aut"),
email = "nestor.fernandez@idiv.de",
comment = c(ORCID = "0000-0002-9645-8571")),
person(given = "Christian",
family = "Langer",
role = c("aut"),
email= "christian.langer@idiv.de",
comment = c(ORCID = "0000-0003-1446-3527")),
person(given = "Jose",
family = "Valdez",
role = c("aut"),
email="jose.valdez@idiv.de",
comment = c(ORCID = "0000-0003-2690-9952")),
person(given = "Henrique Miguel",
family = "Pereira",
role = c("aut"),
email = "henrique.pereira@idiv.de",
comment = c(ORCID = "0000-0003-1043-1675"))
)
The concept of Essential Biodiversity Variables (EBV) comes with a
data structure based on the Network Common Data Form (netCDF).
The 'ebvcube' 'R' package provides functionality to easily create, access and
visualise this data. The EBV netCDFs can be downloaded from the EBV Data
Portal: Christian Langer/ iDiv (2020) <https://portal.geobon.org/>.
checkmate, curl, DelayedArray, ggplot2, HDF5Array, jsonlite,
memuse, methods, ncdf4, ncmeta, reshape2, rhdf5, stringr,
terra, tidyterra, withr