High-Performance Open-Source Archive
future.apply was not installed.plot.modelFits().assessDesign() to provide custom
simulated data and custom model estimates enabling complex data
simulation and analysis methods.assessDesign() for number of
bootstrap samples in case evidence_level is provided.plot.modelFits() to plot effect
sizes.set.seed() in vignette’s code blocks to
facilitate individual code block reproducibility.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.
Need mirroring services?
Contact our team at info@vpspulse.com.
Mirror powered by VPSpulse
Infrastructure sponsored by VPSPulse & Secure Payments by ArionPay.