Implements adaptive tau leaping to approximate the
trajectory of a continuous-time stochastic process as
described by Cao et al. (2007) The Journal of Chemical Physics
<doi:10.1063/1.2745299>. This package is based upon work
supported by NSF DBI-0906041 and NIH K99-GM104158 to Philip
Johnson and NIH R01-AI049334 to Rustom
Antia.

Implements adaptive tau leaping to approximate the
trajectory of a continuous-time stochastic process as
described by Cao et al. (2007) The Journal of Chemical Physics
<doi:10.1063/1.2745299> (aka. the Gillespie stochastic
simulation algorithm).  This package is based upon work
supported by NSF DBI-0906041 and NIH K99-GM104158 to Philip
Johnson and NIH R01-AI049334 to Rustom Antia.

c(person("Anders", "Granholm",
email = "andersgran@gmail.com",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-5799-7655")),
person("Benjamin Skov", "Kaas-Hansen",
email = "epiben@hey.com",
role = "aut",
comment = c(ORCID = "0000-0003-1023-0371")),
person("Aksel Karl Georg", "Jensen",
email = "akje@sund.ku.dk",
role = "ctb",
comment = c(ORCID = "0000-0002-1459-0465")),
person("Theis", "Lange",
email = "thlan@sund.ku.dk",
role = "ctb",
comment = c(ORCID = "0000-0001-6807-8347")))
Package that simulates adaptive clinical trials using adaptive
stopping, adaptive arm dropping, and/or adaptive randomisation.
Developed as part of the INCEPT (Intensive Care Platform Trial) project
(<https://incept.dk/>), which is primarily supported by a grant
from Sygeforsikringen "danmark" (<https://www.sygeforsikring.dk/>).
covr, ggplot2, rmarkdown, knitr, testthat, vdiffr
Anders Granholm [aut, cre] (<https://orcid.org/0000-0001-5799-7655>),
Benjamin Skov Kaas-Hansen [aut]
(<https://orcid.org/0000-0003-1023-0371>),
Aksel Karl Georg Jensen [ctb] (<https://orcid.org/0000-0002-1459-0465>),
Theis Lange [ctb] (<https://orcid.org/0000-0001-6807-8347>)

This package contains R functions for estimating the blood
oxygenation level dependent (BOLD) effect by using functional
Magnetic Resonance Imaging (fMRI) data, based on adaptive Gauss
Markov random fields, for real as well as simulated data. The
implemented simulations make use of efficient Markov Chain
Monte Carlo methods.
R(>= 2.15.1), Matrix, coda, mvtnorm, MCMCpack, spatstat
'adaptive-gmrf-2covar.R' 'adaptive-gmrf.R'
'plot-weights-real.R' 'plot-weights-sim.R'
'sim-adaptive-gmrf-2covar.R' 'sim-adaptive-gmrf.R'
'sim-fmri-2covar.R' 'sim-fmri.R'