CRAN Package Check Results for Package mvnma

Last updated on 2026-06-09 23:53:26 CEST.

Flavor	Version	Tinstall	Tcheck	Ttotal	Status	Flags
r-devel-linux-x86_64-debian-clang	0.1-0	18.31	140.77	159.08	OK
r-devel-linux-x86_64-debian-gcc	0.1-0	10.89	94.01	104.90	OK
r-devel-linux-x86_64-fedora-clang	0.1-0	30.00	216.39	246.39	OK
r-devel-linux-x86_64-fedora-gcc	0.1-0	29.00	207.20	236.20	OK
r-devel-windows-x86_64	0.1-0	18.00	133.00	151.00	ERROR
r-patched-linux-x86_64	0.1-0	11.89	128.79	140.68	OK
r-release-linux-x86_64	0.1-0	13.47	127.91	141.38	OK
r-release-macos-arm64	0.1-0	4.00	35.00	39.00	OK
r-release-macos-x86_64	0.1-0	12.00	137.00	149.00	OK
r-release-windows-x86_64	0.1-0	18.00	137.00	155.00	ERROR
r-oldrel-macos-arm64	0.1-0	4.00	37.00	41.00	OK
r-oldrel-macos-x86_64	0.1-0	12.00	110.00	122.00	OK
r-oldrel-windows-x86_64	0.1-0	24.00	173.00	197.00	ERROR

Check Details

Version: 0.1-0
Check: examples
Result: ERROR Running examples in 'mvnma-Ex.R' failed The error most likely occurred in: > ### Name: mvnma > ### Title: Perform a Bayesian multivariate network meta-analysis using a > ### single-correlation coefficient model > ### Aliases: mvnma print.mvnma > > ### ** Examples > > # Use 'pairwise' to obtain contrast based data for the first two outcomes > > # Early response > pw1 <- pairwise(treat = list(treatment1, treatment2, treatment3), + event = list(resp1, resp2, resp3), n = list(n1, n2, n3), + studlab = id, data = Linde2015, sm = "OR") > > # Early remissions > pw2 <- pairwise(treat = list(treatment1, treatment2, treatment3), + event = list(remi1, remi2, remi3), n = list(n1, n2, n3), + studlab = id, data = Linde2015, sm = "OR") > > # Define outcome labels > outcomes <- c("Early_Response", "Early_Remission", + "Adverse_events", "Loss_to_follow_up", "Loss_to_follow_up_AE") > > # Fit the model combining only the two efficacy outcomes > # (note, we are using only 10 iterations and 2 burnins to reduce the > # runtime of the example; in real applications use larger numbers) > set.seed(1910) > mvnma(pw1, pw2, + reference.group = "Placebo", outclab = outcomes[1:2], + n.iter = 10, n.burnin = 2) module glm loaded module dic loaded Compiling model graph Resolving undeclared variables Allocating nodes Graph information: Observed stochastic nodes: 66 Unobserved stochastic nodes: 19 Total graph size: 1155 Initializing model Outcome: Early_Response mean 95%-CI Rhat n.eff d[Hypericum] 0.7019 [ 0.5317; 0.8836] 1.0051 32 d[Low-dose SARI] 0.4968 [ 0.1448; 0.8842] 1.7056 7 d[NRI] 0.3336 [-0.0320; 0.6822] 1.3557 10 d[NaSSa] 0.0286 [-0.2603; 0.4717] 1.2386 12 d[SNRI] 0.6075 [ 0.3228; 0.7622] 1.0971 32 d[SSRI] 0.5036 [ 0.3406; 0.6187] 1.3800 9 d[TCA] 0.4853 [ 0.3401; 0.6146] 1.9755 6 d[rMAO-A] 0.2290 [-0.0745; 0.5619] 1.8812 6 Outcome: Early_Remission mean 95%-CI Rhat n.eff d[Hypericum] 0.6666 [ 0.4430; 0.9303] 1.0896 32 d[Low-dose SARI] 0.5718 [ 0.1695; 1.2443] 1.2365 13 d[NRI] 0.4750 [ 0.1616; 0.9478] 1.3958 10 d[NaSSa] 0.2851 [-0.0327; 0.7263] 1.1326 22 d[SNRI] 0.6516 [ 0.4192; 0.8417] 1.4666 9 d[SSRI] 0.5048 [ 0.3638; 0.6776] 2.3360 5 d[TCA] 0.5186 [ 0.3231; 0.6786] 3.0170 5 d[rMAO-A] 0.3046 [ 0.0229; 0.6260] 1.6204 7 > > > > > > cleanEx() Error: connections left open: model.code (textConnection) Execution halted Flavors: r-devel-windows-x86_64, r-release-windows-x86_64, r-oldrel-windows-x86_64