Currently serving26341packages,22656articles, and64225datasets by1265organizations,13662 maintainers and22192 contributors.
vimc
lcbc-uio
stan-dev
pharmaverse
r-spatial
tidyverse
ropengov
rstudio
r-lib
ropensci
bioc
r-forge
kwb-r
pik-piam
hypertidy
poissonconsulting
mrc-ide
tidymodels
pecanproject
insightsengineering
thinkr-open
mlr-org
inbo
ggseg
ohdsi
modeloriented
predictiveecology
paws-r
ropenspain
flr
bnosac
sciviews
repboxr
openvolley
rmi-pacta
mrcieu
epiverse-trace
nlmixr2
frbcesab
ices-tools-prod
yulab-smu
azure
statnet
riatelab
appsilon
mlverse
bips-hb
cloudyr
epiforecasts
rjdverse
tmsalab
usaid-oha-si
bupaverse
dreamrs
usepa
openpharma
hubverse-org
merck
coatless-rpkg
certe-medical-epidemiology
darwin-eu
business-science
ambiorix-web
easystats
traitecoevo
rsquaredacademy
spatstat
bluegreen-labs
uscbiostats
hugheylab
rikenbit
nutriverse
r-dbi
rspatial
ocbe-uio
epicentre-msf
apache
ctu-bern
ifpri
cogdisreslab
reconhub
ipeagit
biometris
terminological
aus-doh-safety-and-quality
nflverse
gesistsa
humaniverse
data-cleaning
csids
winvector
gamlss-dev
cynkra
rinterface
mazamascience
atsa-es
Want to learn more about r-universe? Have a look atropensci.org/r-universeor updates from the rOpenSci blog:
Showing 1 of total 1 results (show query)
johnatanlab
The 'BayesDLMfMRI' package performs statistical analysis for task-based functional magnetic resonance imaging (fMRI) data at both individual and group levels. The analysis to detect brain activation at the individual level is based on modeling the fMRI signal using Matrix-Variate Dynamic Linear Models (MDLM). The analysis for the group stage is based on posterior distributions of the state parameter obtained from the modeling at the individual level. In this way, this package offers several R functions with different algorithms to perform inference on the state parameter to assess brain activation for both individual and group stages. Those functions allow for parallel computation when the analysis is performed for the entire brain as well as analysis at specific voxels when it is required. References: Cardona-Jiménez (2021) <doi:10.1016/j.csda.2021.107297>; Cardona-Jiménez (2021) <arXiv:2111.01318>.
Maintained by Carlos Pérez. Last updated 1 years ago.
openblascppopenmp