Nothing
performBayesianMCPMod()
where the model significance status from the MCP step was sometimes not correctly assigned to the fitted model in the Mod step.print.modelFit()
where sometimes the coefficients for the fitted model shapes were not printed correctly.getMED()
where quantile and evidence level could sometimes not be matched due to floating-point precision issues when using bootstrapped quantiles.getPosterior()
, getCritProb()
, and getContr()
to accept a covariance matrix instead of a standard deviation vector as argument.getBootstrapSamples()
.assessDesign()
output.future.apply
package optional.plot.modelFits()
that would plot credible bands based on incorrectly selected bootstrapped quantiles.getMED()
, a function to assess the minimally efficacious dose (MED) and integrated getMED()
into assessDesign()
and performBayesianMCPMod()
.getModelFits()
has an argument to fit an average model and this will be carried forward for all subsequent functions.getBootstrapSamples()
, a separate function for bootstrapping samples from the posterior distributions of the dose levels.getPosterior()
to allow the input of a fully populated variance-covariance matrix.assessDesign()
to optionally skip the Mod part of MCPMod.BayesianMCPMod
package.getBootstrapQuantiles()
that would return wrong bootstrapped quantiles.getBootstrapSamples()
, a separate function for bootstrapping samples.BayesianMCPMod
package.Any scripts or data that you put into this service are public.
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