API for tfojo13/RMP
Fits Bayesian models on repeated measures of multiple symptoms

Global functions
combine.MVGs Source code
conditionalize.MVG Source code
create.MVG Source code
create.event.predictor Man page Source code
create.gaussian.mixture Source code
create.measurement.predictor Man page Source code
create.slope.design.matrix Source code
create.sum.dimensions.matrix Source code
create.time.design.matrix Source code
dnorm.mix Source code
do.create.sum.dimensions.matrix Source code
flatten.list.first.level Source code
get.all.class.probabilities Man page Source code
get.class.probabilities Source code
get.fixed.beta.sums Source code
get.gaussian.mixtures.from.mvgs Source code
get.individual.beta Source code
get.individual.omega Source code
get.individual.sigma Source code
get.means.for.covariates Source code
get.means.for.iteration.class.and.covariates Source code
get.num.iterations Source code
get.obs.dists.for.iteration Source code
get.observation.distributions Source code
get.observation.distributions.and.weights Source code
get.omega.epsilon Source code
get.overall.covariance.matrix Man page Source code
get.posterior.beta.dist.for.iteration.and.class Source code
get.posterior.beta.distributions Source code
get.sigma.epsilon Source code
make.event.predictions Man page Source code
make.measurement.predictions Man page Source code
marginalize.MVG Source code
mean.logitnorm Source code
mean.mix Source code
multiply.MVG Source code
pnorm.mix Source code
predict.event Source code
predict.symptoms Source code
qnorm.mix Source code
r.mvg Source code
random.fixed.beta.and.sd.generator Man page Source code
rnorm.mix Source code
set.MVG.mean Source code
simulate.class Source code
test.from.inside Man page Source code
write.k.class.model Source code
write.one.class.model Source code
tfojo13/RMP documentation built on Dec. 7, 2017, 8:23 p.m.