Running examples in ‘netmeta-Ex.R’ failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: netposet
> ### Title: Partial order of treatments in network meta-analysis
> ### Aliases: netposet print.netposet
> 
> ### ** Examples
> 
> ## Not run: 
> ##D # Use depression dataset
> ##D #
> ##D data(Linde2015)
> ##D 
> ##D # Define order of treatments
> ##D #
> ##D trts <- c("TCA", "SSRI", "SNRI", "NRI",
> ##D   "Low-dose SARI", "NaSSa", "rMAO-A", "Hypericum", "Placebo")
> ##D 
> ##D # Outcome labels
> ##D #
> ##D outcomes <- c("Early response", "Early remission")
> ##D 
> ##D # (1) Early response
> ##D #
> ##D p1 <- pairwise(treat = list(treatment1, treatment2, treatment3),
> ##D   event = list(resp1, resp2, resp3), n = list(n1, n2, n3),
> ##D   studlab = id, data = Linde2015, sm = "OR")
> ##D #
> ##D net1 <- netmeta(p1, common = FALSE,
> ##D   seq = trts, ref = "Placebo", small.values = "undesirable")
> ##D 
> ##D # (2) Early remission
> ##D #
> ##D p2 <- pairwise(treat = list(treatment1, treatment2, treatment3),
> ##D   event = list(remi1, remi2, remi3), n = list(n1, n2, n3),
> ##D   studlab = id, data = Linde2015, sm = "OR")
> ##D #
> ##D net2 <- netmeta(p2, common = FALSE,
> ##D   seq = trts, ref = "Placebo", small.values = "undesirable")
> ##D 
> ##D # Partial order of treatment rankings (two outcomes)
> ##D #
> ##D po <- netposet(netrank(net1), netrank(net2), outcomes = outcomes)
> ##D 
> ##D # Hasse diagram
> ##D #
> ##D hasse(po)
> ##D 
> ##D 
> ##D #
> ##D # Outcome labels
> ##D #
> ##D outcomes <- c("Early response", "Early remission",
> ##D   "Lost to follow-up", "Lost to follow-up due to AEs",
> ##D   "Adverse events (AEs)")
> ##D 
> ##D # (3) Loss to follow-up
> ##D #
> ##D p3 <- pairwise(treat = list(treatment1, treatment2, treatment3),
> ##D   event = list(loss1, loss2, loss3), n = list(n1, n2, n3),
> ##D   studlab = id, data = Linde2015, sm = "OR")
> ##D #
> ##D net3 <- netmeta(p3, common = FALSE,
> ##D   seq = trts, ref = "Placebo", small.values = "desirable")
> ##D 
> ##D # (4) Loss to follow-up due to adverse events
> ##D #
> ##D p4 <- pairwise(treat = list(treatment1, treatment2, treatment3),
> ##D   event = list(loss.ae1, loss.ae2, loss.ae3), n = list(n1, n2, n3),
> ##D   studlab = id, data = subset(Linde2015, id != 55), sm = "OR")
> ##D #
> ##D net4 <- netmeta(p4, common = FALSE,
> ##D   seq = trts, ref = "Placebo", small.values = "desirable")
> ##D 
> ##D # (5) Adverse events
> ##D #
> ##D p5 <- pairwise(treat = list(treatment1, treatment2, treatment3),
> ##D   event = list(ae1, ae2, ae3), n = list(n1, n2, n3),
> ##D   studlab = id, data = Linde2015, sm = "OR")
> ##D #
> ##D net5 <- netmeta(p5, common = FALSE,
> ##D   seq = trts, ref = "Placebo", small.values = "desirable")
> ##D 
> ##D # Partial order of treatment rankings (all five outcomes)
> ##D #
> ##D po.ranks <- netposet(netrank(net1), netrank(net2),
> ##D   netrank(net3), netrank(net4), netrank(net5), outcomes = outcomes)
> ##D 
> ##D # Same result
> ##D #
> ##D po.nets <- netposet(net1, net2, net3, net4, net5,
> ##D   outcomes = outcomes)
> ##D #
> ##D all.equal(po.ranks, po.nets)
> ##D 
> ##D # Print matrix with P-scores (random effects model)
> ##D #
> ##D po.nets$P.random
> ##D 
> ##D # Hasse diagram for all outcomes (random effects model)
> ##D #
> ##D hasse(po.ranks)
> ##D 
> ##D # Hasse diagram for outcomes early response and early remission
> ##D #
> ##D po12 <- netposet(netrank(net1), netrank(net2),
> ##D   outcomes = outcomes[1:2])
> ##D hasse(po12)
> ##D 
> ##D # Scatter plot
> ##D #
> ##D oldpar <- par(pty = "s")
> ##D plot(po12)
> ##D par(oldpar)
> ## End(Not run)
> 
> # Example using ranking matrix with P-scores
> #
> # Ribassin-Majed L, Marguet S, Lee A.W., et al. (2017):
> # What is the best treatment of locally advanced nasopharyngeal
> # carcinoma? An individual patient data network meta-analysis.
> # Journal of Clinical Oncology, 35, 498-505
> #
> outcomes <- c("OS", "PFS", "LC", "DC")
> treatments <- c("RT", "IC-RT", "IC-CRT", "CRT",
+   "CRT-AC", "RT-AC", "IC-RT-AC")
> #
> # P-scores (from Table 1)
> #
> pscore.os  <- c(15, 33, 63, 70, 96, 28, 45) / 100
> pscore.pfs <- c( 4, 46, 79, 52, 94, 36, 39) / 100
> pscore.lc  <- c( 9, 27, 47, 37, 82, 58, 90) / 100
> pscore.dc  <- c(16, 76, 95, 48, 72, 32, 10) / 100
> #
> pscore.matrix <- data.frame(pscore.os, pscore.pfs, pscore.lc, pscore.dc)
> rownames(pscore.matrix) <- treatments
> colnames(pscore.matrix) <- outcomes
> pscore.matrix
           OS  PFS   LC   DC
RT       0.15 0.04 0.09 0.16
IC-RT    0.33 0.46 0.27 0.76
IC-CRT   0.63 0.79 0.47 0.95
CRT      0.70 0.52 0.37 0.48
CRT-AC   0.96 0.94 0.82 0.72
RT-AC    0.28 0.36 0.58 0.32
IC-RT-AC 0.45 0.39 0.90 0.10
> #
> po <- netposet(pscore.matrix)
> po12 <- netposet(pscore.matrix[, 1:2])
> po
         RT IC-RT IC-CRT CRT CRT-AC RT-AC IC-RT-AC
RT        0     0      0   0      0     0        0
IC-RT     1     0      0   0      0     0        0
IC-CRT    0     1      0   0      0     0        0
CRT       1     0      0   0      0     0        0
CRT-AC    0     0      0   1      0     1        0
RT-AC     1     0      0   0      0     0        0
IC-RT-AC  0     0      0   0      0     0        0
> po12
         RT IC-RT IC-CRT CRT CRT-AC RT-AC IC-RT-AC
RT        0     0      0   0      0     0        0
IC-RT     0     0      0   0      0     1        0
IC-CRT    0     1      0   0      0     0        1
CRT       0     1      0   0      0     0        1
CRT-AC    0     0      1   1      0     0        0
RT-AC     1     0      0   0      0     0        0
IC-RT-AC  0     0      0   0      0     1        0
> #
> hasse(po)
Error: Package 'hasseDiagram' missing.
  Please use the following R commands for installation:
  install.packages("BiocManager")
  BiocManager::install()
  BiocManager::install("Rgraphviz")
  install.packages("hasseDiagram")
Execution halted
Examples with CPU (user + system) or elapsed time > 5s
               user system elapsed
Gurusamy2011  5.738  0.235   7.152
Dong2013      4.444  0.072   5.654
netmetabin    4.409  0.034   5.345
netpairwise   4.148  0.052   5.218
netmeasures   4.034  0.071   5.113
Franchini2012 3.455  0.087   6.342