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add_index()
 Add followup time and arm indices to a dataset

alog_pcfb
 Studies of alogliptin for lowering blood glucose concentration in patients with type II diabetes

binplot()
 Plot relative effects from NMAs performed at multiple timebins

copd
 Studies comparing Tiotropium, Aclidinium and Placebo for maintenance treatment of moderate to severe chronic obstructive pulmonary disease

cumrank()
 Plot cumulative ranking curves from MBNMA models

default.priors()
 Sets default priors for JAGS model code

devplot()
 Plot deviance contributions from an MBNMA model

diabetes
 Studies comparing treatments for type 2 diabetes

fitplot()
 Plot fitted values from MBNMA model

gen.parameters.to.save()
 Automatically generate parameters to save for a timecourse MBNMA model

genmaxcols()
 Get large vector of distinct colours using Rcolorbrewer

genspline()
 Generates spline basis matrices for fitting to timecourse function

get.closest.time()
 Create a dataset with a single time point from each study closest to specified time

get.earliest.time()
 Create a dataset with the earliest time point only

get.latest.time()
 Create a dataset with the latest time point only

get.model.vals()
 Get MBNMA model values

get.prior()
 Get current priors from JAGS model code

get.relative()
 Calculates relative effects/mean differences at a particular timepoint

getjagsdata()
 Prepares data for JAGS

getnmadata()
 Prepares NMA data for JAGS

goutSUA_CFB
 Studies of treatments for reducing serum uric acid in patients with gout

goutSUA_CFBcomb
 Studies of combined treatments for reducing serum uric acid in patients with gout

hyalarthritis
 Studies comparing hyaluronan (HA)–based viscosupplements for osteoarthritis

inconsistency.loops()
 Identify comparisons in loops that fulfil criteria for nodesplitting

mb.comparisons()
 Identify unique comparisons within a network (identical to MBNMAdose)

mb.make.contrast()
 Convert armbased MBNMA data to contrast data

plot(<mb.network>)
mb.network()
 Create an
mb.network
object

plot(<nodesplit>)
mb.nodesplit()
 Perform nodesplitting on a MBNMA timecourse network

mb.nodesplit.comparisons()
 Identify comparisons in timecourse MBNMA datasets that fulfil criteria for nodesplitting

mb.run()
 Run MBNMA timecourse models

mb.update()
 Update MBNMA to obtain deviance contributions or fitted values

mb.validate.data()
 Validates that a dataset fulfils requirements for MBNMA

mb.write()
 Write MBNMA timecourse models JAGS code

nma.run()
 Run an NMA model

obesityBW_CFB
 Studies of treatments for reducing body weight in patients with obesity

osteopain
 Studies of pain relief medications for osteoarthritis

pDcalc()
 Calculate plugin pD from a JAGS model with univariate likelihood for studies
with repeated measurements

plot(<mb.predict>)
 Plots predicted responses from a timecourse MBNMA model

plot(<mb.rank>)
 Plot histograms of rankings from MBNMA models

plot(<mbnma>)
 Forest plot for results from timecourse MBNMA models

predict(<mbnma>)
 Predict effects over time in a given population based on MBNMA timecourse
models

print(<mb.network>)
 Print mb.network information to the console

print(<mb.predict>)
 Print summary information from an mb.predict object

print(<mb.rank>)
 Prints a summary of rankings for each parameter

print(<nodesplit>)
 Prints basic results from a nodesplit to the console

print(<relative.array>)
 Print posterior medians (95% credible intervals) for table of relative effects/mean
differences between treatments/classes

radian.rescale()
 Calculate position of label with respect to vertex location within a circle

rank()
 Set rank as a method

rank(<mb.predict>)
 Rank predictions at a specific time point

rank(<mbnma>)
 Rank parameters from a timecourse MBNMA

rankauc()
 Calculates ranking probabilities for AUC from a timecourse MBNMA

ref.comparisons()
 Identify unique comparisons relative to study reference treatment within a
network

ref.synth()
 Synthesise single arm studies with repeated observations of the same
treatment over time

ref.validate()
 Checks the validity of ref.resp if given as data frame

remove.loops()
 Removes any loops from MBNMA model JAGS code that do not contain any
expressions

replace.prior()
 Replace original priors in an MBNMA model with new priors

summary(<mb.network>)
 Print summary mb.network information to the console

summary(<mb.predict>)
 Prints summary of mb.predict object

summary(<mbnma>)
 Print summary MBNMA results to the console

summary(<nodesplit>)
 Takes nodesplit results and produces summary data frame

temax()
 Emax timecourse function

tfpoly()
 Fractional polynomial timecourse function

timeplot()
 Plot raw responses over time by treatment or class

titp()
 Integrated TwoComponent Prediction (ITP) function

tloglin()
 Loglinear (exponential) timecourse function

tpoly()
 Polynomial timecourse function

tspline()
 Spline timecourse functions

tuser()
 Userdefined timecourse function

write.beta()
 Adds sections of JAGS code for an MBNMA model that correspond to beta
parameters

write.check()
 Checks validity of arguments for mb.write

write.cor()
 Adds correlation between timecourse relative effects

write.likelihood()
 Adds sections of JAGS code for an MBNMA model that correspond to the
likelihood

write.model()
 Write the basic JAGS model code for MBNMA to which other lines of model
code can be added

write.ref.synth()
 Write MBNMA timecourse models JAGS code for synthesis of studies
investigating reference treatment

write.timecourse()
 Adds sections of JAGS code for an MBNMA model that correspond to alpha
parameters