* using log directory ‘/data/blackswan/ripley/R/packages/tests-devel/MBNMAdose.Rcheck’
* using R Under development (unstable) (2025-12-01 r89083)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (GCC) 14.2.1 20240912 (Red Hat 14.2.1-3)
    GNU Fortran (GCC) 14.2.1 20240912 (Red Hat 14.2.1-3)
* running under: Fedora Linux 40 (Workstation Edition)
* using session charset: UTF-8
* checking for file ‘MBNMAdose/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘MBNMAdose’ version ‘0.5.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... INFO
Package suggested but not available for checking: ‘mcmcplots’
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for executable files ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘MBNMAdose’ can be installed ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... [17s/18s] OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd line widths ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking LazyData ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking installed files from ‘inst/doc’ ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
* checking examples with --run-donttest ... [26m/26m] ERROR
Running examples in ‘MBNMAdose-Ex.R’ failed
The error most likely occurred in:

> ### Name: mbnma.run
> ### Title: Run MBNMA dose-response models
> ### Aliases: mbnma.run
> 
> ### ** Examples
> 
> ## No test: 
> # Using the triptans data
> network <- mbnma.network(triptans)
Values for `agent` with dose = 0 have been recoded to `Placebo`
agent is being recoded to enforce sequential numbering
> 
> 
> ######## Dose-response functions ########
> 
> # Fit a dose-response MBNMA with a linear function
> # with common treatment effects
> result <- mbnma.run(network, fun=dpoly(degree=1), method="common")
`likelihood` not given by user - set to `binomial` based on data provided
`link` not given by user - set to `logit` based on assigned value for `likelihood`
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 182
   Unobserved stochastic nodes: 77
   Total graph size: 3630

Initializing model

> 
> # Fit a dose-response MBNMA with a log-linear function
> # with random treatment effects
> result <- mbnma.run(network, fun=dloglin(), method="random")
`likelihood` not given by user - set to `binomial` based on data provided
`link` not given by user - set to `logit` based on assigned value for `likelihood`
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 182
   Unobserved stochastic nodes: 190
   Total graph size: 4074

Initializing model

> 
> # Fit a dose-response MBNMA with a fractional polynomial function
> # with random treatment effects
> # with a probit link function
> result <- mbnma.run(network, fun=dfpoly(), method="random", link="probit")
`likelihood` not given by user - set to `binomial` based on data provided
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 182
   Unobserved stochastic nodes: 190
   Total graph size: 4103

Initializing model

> 
> # Fit a user-defined function (quadratic)
> fun.def <- ~ (beta.1 * dose) + (beta.2 * (dose^2))
> result <- mbnma.run(network, fun=duser(fun=fun.def), method="common")
`likelihood` not given by user - set to `binomial` based on data provided
`link` not given by user - set to `logit` based on assigned value for `likelihood`
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 182
   Unobserved stochastic nodes: 84
   Total graph size: 3691

Initializing model

> 
> # Fit an Emax function
> # with a single random (exchangeable) parameter for ED50
> # with common treatment effects
> result <- mbnma.run(network, fun=demax(emax="rel", ed50="random"),
+               method="common")
`likelihood` not given by user - set to `binomial` based on data provided
`link` not given by user - set to `logit` based on assigned value for `likelihood`
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 182
   Unobserved stochastic nodes: 86
   Total graph size: 3689

Initializing model

> 
> # Fit an Emax function with a Hill parameter
> # with a fixed value of 5 for the Hill parameter
> # with random relative effects
> result <- mbnma.run(network, fun=demax(hill=5), method="random")
`likelihood` not given by user - set to `binomial` based on data provided
`link` not given by user - set to `logit` based on assigned value for `likelihood`
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 182
   Unobserved stochastic nodes: 197
   Total graph size: 4133

Initializing model

> 
> # Fit a model with natural cubic splines
> # with 3 knots at 10% 30% and 60% quartiles of dose ranges
> depnet <- mbnma.network(ssri) # Using the sSRI depression dataset
Values for `agent` with dose = 0 have been recoded to `Placebo`
agent is being recoded to enforce sequential numbering
> result <- mbnma.run(depnet, fun=dspline(type="ns", knots=c(0.1,0.3,0.6)))
`likelihood` not given by user - set to `binomial` based on data provided
`link` not given by user - set to `logit` based on assigned value for `likelihood`
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 145
   Unobserved stochastic nodes: 80
   Total graph size: 4022

Initializing model

> 
> # Fit a model with different dose-response functions for each agent
> multifun <- dmulti(list(dloglin(), # for placebo (can be any function)
+                        demax(), # for eletriptan
+                        demax(), # for sumatriptan
+                        dloglin(), # for frovatriptan
+                        demax(), # for almotriptan
+                        demax(), # for zolmitriptan
+                        dloglin(), # for naratriptan
+                        demax())) # for rizatriptan
> 
> multidose <- mbnma.run(network, fun=multifun)
`likelihood` not given by user - set to `binomial` based on data provided
`link` not given by user - set to `logit` based on assigned value for `likelihood`
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 182
   Unobserved stochastic nodes: 91
   Total graph size: 3938

Initializing model

> 
> 
> ########## Class effects ##########
> 
>  # Using the osteoarthritis dataset
>  pain.df <- osteopain
> 
>  # Set a shared class (NSAID) only for Naproxcinod and Naproxen
>  pain.df <- pain.df %>% dplyr::mutate(
+                 class = dplyr::case_when(agent %in% c("Naproxcinod", "Naproxen") ~ "NSAID",
+                         !agent %in% c("Naproxcinod", "Naproxen") ~ agent
+                         )
+                 )
> 
>  # Run an Emax MBNMA with a common class effect on emax
>  painnet <- mbnma.network(pain.df)
Values for `agent` with dose = 0 have been recoded to `Placebo`
agent is being recoded to enforce sequential numbering
Values for `class` with dose = 0 have been recoded to `Placebo`
class is being recoded to enforce sequential numbering
>  result <- mbnma.run(painnet, fun = demax(),
+                 class.effect = list(emax = "common"))
`likelihood` not given by user - set to `normal` based on data provided
`link` not given by user - set to `identity` based on assigned value for `likelihood`
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 74
   Unobserved stochastic nodes: 33
   Total graph size: 876

Initializing model

> 
> 
> ####### Priors #######
> 
> # Obtain priors from a fractional polynomial function
> result <- mbnma.run(network, fun=dfpoly(degree=1), method="random")
`likelihood` not given by user - set to `binomial` based on data provided
`link` not given by user - set to `logit` based on assigned value for `likelihood`
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 182
   Unobserved stochastic nodes: 190
   Total graph size: 4103

Initializing model

> print(result$model.arg$priors)
$mu
[1] "dnorm(0,0.0001)"

$beta.1
[1] "dnorm(0,0.0001)"

$sd
[1] "dunif(0, 6.021)"

> 
> # Change the prior distribution for the power
> newpriors <- list(power.1 = "dnorm(0,0.001) T(0,)")
> newpriors <- list(sd = "dnorm(0,0.5) T(0,)")
> 
> result <- mbnma.run(network, fun=dfpoly(degree=1), method="random",
+               priors=newpriors)
`likelihood` not given by user - set to `binomial` based on data provided
`link` not given by user - set to `logit` based on assigned value for `likelihood`
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 182
   Unobserved stochastic nodes: 190
   Total graph size: 4103

Initializing model

> 
> 
> ########## Sampler options ##########
> 
> # Change the number of MCMC iterations, the number of chains, and the thin
> result <- mbnma.run(network, fun=dloglin(), method="random",
+               n.iter=5000, n.thin=5, n.chains=4)
`likelihood` not given by user - set to `binomial` based on data provided
`link` not given by user - set to `logit` based on assigned value for `likelihood`
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 182
   Unobserved stochastic nodes: 190
   Total graph size: 4074

Initializing model

> 
> ####### Examine MCMC diagnostics (using mcmcplots or coda packages) #######
> 
> # Density plots
> mcmcplots::denplot(result)
Error in loadNamespace(x) : there is no package called ‘mcmcplots’
Calls: loadNamespace -> withRestarts -> withOneRestart -> doWithOneRestart
Execution halted
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘testthat.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking re-building of vignette outputs ... [456s/458s] OK
* checking PDF version of manual ... OK
* checking for non-standard things in the check directory ... OK
* checking for detritus in the temp directory ... OK
* checking for new files in some other directories ... OK
* DONE

Status: 1 ERROR
See
  ‘/data/blackswan/ripley/R/packages/tests-devel/MBNMAdose.Rcheck/00check.log’
for details.

Command exited with non-zero status 1
Time 35:16.93, 2078.43 + 26.52