person("Michele", "Scandola", email = "michele.scandola@univr.it",
role = c("aut", "cre"), comment = c(ORCID = "0000-0003-0853-8975"))
Michele Scandola [aut, cre] (<https://orcid.org/0000-0003-0853-8975>)

Analyse single case analyses against a control group.
Its purpose is to provide a flexible, with good power and
low first type error
approach that can manage at the same time controls' and patient's data.
The use of Bayesian statistics allows to test both the alternative and
null hypothesis.
Scandola, M., & Romano, D. (2020, August 3). <doi:10.31234/osf.io/sajdq>
Scandola, M., & Romano, D. (2021). <doi:10.1016/j.neuropsychologia.2021.107834>.
reshape2, gridExtra, bridgesampling, testthat, knitr,
rmarkdown, covr

Bayesian Modeling of Spatio-Temporal Data with R
c(person(given="Sujit K.", family="Sahu", email="S.K.Sahu@soton.ac.uk", role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-2315-3598")), person(given="Duncan P.", family="Lee",
email="Duncan.Lee@glasgow.ac.uk", role="aut"),  person(given="K. Shuvo", family="Bakar",
email="shuvo.bakar@gmail.com", role="aut"))
Fits, validates and compares a number of Bayesian models for
spatial and space time point referenced and areal unit data. Model fitting
is done using several packages: 'rstan', 'INLA', 'spBayes', 'spTimer',
'spTDyn', 'CARBayes' and 'CARBayesST'. Model comparison is performed using
the DIC and WAIC,  and K-fold cross-validation where the user is free
to select their own subset of data rows for validation. Sahu (2022)
<doi:10.1201/9780429318443> describes the methods in detail.
methods, spTimer, ggplot2, rstan, spBayes, CARBayes,
CARBayesST, spTDyn, MCMCpack, Rdpack
INLA (>= 20.03.17), coda, extraDistr, RcppParallel, maps,
rstantools (>= 2.0.0), xtable, MASS, mnormt, loo, cowplot,
ggrepel, ggspatial, sf, knitr, rmarkdown, testthat, ggsn,
ggpubr, akima, tidyr, huxtable, RColorBrewer, magick, markdown,
bookdown, inlabru, BiocStyle
Sujit K. Sahu [aut, cre] (<https://orcid.org/0000-0003-2315-3598>),
Duncan P. Lee [aut],
K. Shuvo Bakar [aut]