Function reference • MBNMAdose

All functions
`DR.comparisons()`	Adds placebo comparisons for dose-response relationship
`add_index()`	Add arm indices and agent identifiers to a dataset
`alog_pcfb`	Studies of alogliptin for lowering blood glucose concentration in patients with type II diabetes
`calc.edx()`	Calculates values for EDx from an Emax model, the dose at which x% of the maximal response (Emax) is reached
`changepd()`	Update model fit statistics depending on calculation for pD
`check.network()`	Check if all nodes in the network are connected (identical to function in `MBNMAtime`)
`cumrank()`	Plot cumulative ranking curves from MBNMA models
`default.priors()`	Sets default priors for JAGS model code
`demax()`	Emax dose-response function
`devdev()`	Dev-dev plot for comparing deviance contributions from two models
`devplot()`	Plot deviance contributions from an MBNMA model
`dexp()`	Exponential dose-response function
`dfpoly()`	Fractional polynomial dose-response function
`ditp()`	Integrated Two-Component Prediction (ITP) function
`dloglin()`	Log-linear (exponential) dose-response function
`dmulti()`	Agent-specific dose-response function
`dnonparam()`	Non-parameteric dose-response functions
`dpoly()`	Polynomial dose-response function
`drop.comp()`	Drop treatments from multi-arm (>2) studies for node-splitting
`drop.disconnected()`	Drop studies that are not connected to the network reference treatment
`dspline()`	Spline dose-response functions
`duser()`	User-defined dose-response function
`fitplot()`	Plot fitted values from MBNMA model
`gen.parameters.to.save()`	Automatically generate parameters to save for a dose-response MBNMA model
`genspline()`	Generates spline basis matrices for fitting to dose-response function
`get.prior()`	Get current priors from JAGS model code
`get.relative()`	Calculates league table of effects between treatments in MBNMA and/or NMA models
`getjagsdata()`	Prepares data for JAGS
`gout`	Studies of treatments for Serum Uric Acid reduction in patients with gout
`inconsistency.loops()`	Identify comparisons in loops that fulfill criteria for node-splitting
`mbnma.comparisons()`	Identify unique comparisons within a network
`plot(<mbnma.network>)` `mbnma.network()`	Create an mbnma.network object
`mbnma.nodesplit()` `plot(<nodesplit>)`	Node-splitting model for testing consistency at the treatment level using MBNMA
`mbnma.run()`	Run MBNMA dose-response models
`mbnma.update()`	Update MBNMA to monitor deviance nodes in the model
`mbnma.validate.data()`	Validates that a dataset fulfills requirements for MBNMA
`mbnma.write()`	Write MBNMA dose-response model JAGS code
`nma.nodesplit()` `plot(<nma.nodesplit>)`	Node-splitting model for testing consistency at the treatment-level
`plot(<nma>)` `nma.run()`	Run an NMA model
`norm2lnorm()`	Convert normal distribution parameters to corresponding log-normal distribution parameters
`osteopain`	Studies of treatments for pain relief in patients with osteoarthritis
`pDcalc()`	Calculate plugin pD from a JAGS model with univariate likelihood for studies with repeated measurements
`plot(<mbnma>)`	Forest plot for results from dose-response MBNMA models
`plot(<mbnma.predict>)`	Plots predicted responses from a dose-response MBNMA model
`plot(<mbnma.rank>)`	Plot histograms of rankings from MBNMA models
`predict(<mbnma>)`	Predict responses for different doses of agents in a given population based on MBNMA dose-response models
`print(<mbnma.network>)`	Print mbnma.network information to the console
`print(<mbnma.predict>)`	Print summary information from an mbnma.predict object
`print(<mbnma.rank>)`	Prints summary information about an mbnma.rank object
`print(<nma.nodesplit>)`	Prints summary results from an nma.nodesplit object
`print(<nodesplit>)`	Prints summary results from a nodesplit object
`print(<relative.array>)`	Print posterior medians (95% credible intervals) for table of relative effects/mean differences between treatments/classes
`psoriasis100`	Studies of biologics for treatment of moderate-to-severe psoriasis (100% improvement)
`psoriasis75`	Studies of biologics for treatment of moderate-to-severe psoriasis (>=75% improvement)
`psoriasis90`	Studies of biologics for treatment of moderate-to-severe psoriasis (>=90% improvement)
`rank()`	Set rank as a method
`rank(<mbnma>)`	Rank parameter estimates
`rank(<mbnma.predict>)`	Rank predicted doses of different agents
`rank(<relative.array>)`	Rank relative effects obtained between specific doses
`recode.agent()`	Assigns agent or class variables numeric identifiers
`ref.synth()`	Synthesise single arm dose = 0 / placebo studies to estimate E0
`rescale.link()`	Rescale data depending on the link function provided
`ssi_closure`	Studies of wound closure methods to reduce Surgical Site Infections (SSI)
`ssri`	Studies of Selective Serotonin Reuptake Inhibitors (SSRIs) for major depression
`summary(<mbnma>)`	Print summary of MBNMA results to the console
`summary(<mbnma.network>)`	Print summary mbnma.network information to the console
`summary(<mbnma.predict>)`	Produces a summary data frame from an mbnma.predict object
`summary(<mbnma.rank>)`	Generates summary data frames for an mbnma.rank object
`summary(<nma.nodesplit>)`	Generates a summary data frame for nma.nodesplit objects
`summary(<nodesplit>)`	Generates a summary data frame for nodesplit objects
`triptans`	Studies of triptans for headache pain relief

Reference

All functions