vertical.image.legend() is Copyright (C) 2013 by Jenise Swall

person("Manuela", "Zucknick", email = "manuela.zucknick@medisin.uio.no", role = c("ctb")),
person("Waldir", "Leoncio", email = "w.l.netto@medisin.uio.no", role = c("ctb")))

person("Waldir", "Leoncio", email = "w.l.netto@medisin.uio.no", role = c("ctb")),

Description: Bayesian seemingly unrelated regression with general variable selection and dense/sparse covariance matrix. The sparse seemingly unrelated regression is described in Bottolo et al. (2020) <doi:10.1101/467019>, and the software paper is in Zhao et al. (2021) <arXiv:2104.14008>.

Description: Bayesian seemingly unrelated regression with general variable selection and dense/sparse covariance matrix. The sparse seemingly unrelated regression is described in Bottolo et al. (2021) <doi:10.1111/rssc.12490>, and the software paper is in Zhao et al. (2021) <arXiv:2104.14008>.
are used with Copyright (C) 2006-2018 from Arseny Kapoulkine
and Copyright (C) 2003 from Kristen Wegner. The R function
vertical.image.legend() is used with Copyright (C) 2013 from

Description: Bayesian seemingly unrelated regression with general variable selection and dense/sparse covariance matrix. The sparse seemingly unrelated regression is described in Bottolo et al. (2021) <doi:10.1111/rssc.12490>, the software paper is in Zhao et al. (2021) <doi:10.18637/jss.v100.i11>, and the model with random effects is described in Zhao et al. (2021) <arXiv:2101.05899>.

person("Manuela", "Zucknick", email = "manuela.zucknick@medisin.uio.no", role = c("aut")))

Title: Bayes Linear Estimators for Finite Population
Maintainer: Pedro Soares Figueiredo <pedrosfig@hotmail.com>
Description: Allows the user to apply the Bayes Linear approach to finite population with the Simple Random Sampling - BLE_SRS() - and
the Stratified Simple Random Sampling design - BLE_SSRS() - (both without replacement) and to the Ratio estimator (using auxiliary
The Bayes linear estimation approach is applied to a general linear regression model for finite population prediction in BLE_Reg()
and it is also possible to achieve the design based estimators using vague prior distributions.
Based on Gonçalves, K.C.M, Moura, F.A.S and  Migon, H.S.(2014) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886>.
Author: Pedro Soares Figueiredo [aut, cre]