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          9.945  0.155  12.540
Dong2013              8.105  0.149  10.190
netmetabin            7.804  0.016   9.714
netpairwise           7.283  0.024   8.792
netmeasures           7.226  0.016  10.060
forest.netmeta        6.956  0.018  10.150
netgraph.netmeta      6.508  0.012   8.289
invmat                6.447  0.012   8.389
netdistance           6.375  0.051   9.266
netmatrix             6.418  0.008   8.222
netmeta               6.406  0.020   8.526
netgraph              6.408  0.017   9.849
as.data.frame.netmeta 6.256  0.031   7.903
netbind               6.092  0.008   8.844
forest.netbind        5.958  0.008   8.136
forest.netcomparison  5.875  0.016   8.181
forest.netcomplex     5.841  0.016   7.698
netcomb               5.656  0.012   7.247
Franchini2012         5.532  0.096   7.408
netcontrib            5.419  0.036   6.212
netimpact             5.313  0.011   7.150
netgraph.netimpact    5.179  0.004   6.562
forest.netcomb        5.169  0.007   7.226
netcomplex            5.017  0.000   7.688
netcomparison         4.975  0.020   7.081
netgraph.netcomb      4.857  0.032   6.907
dietaryfat            4.851  0.001   5.297
Linde2016             4.641  0.004   5.076
netheat               4.520  0.028   5.400
decomp.design         4.234  0.008   5.728
Stowe2010             4.091  0.042   5.564
forest.netsplit       4.110  0.008   5.697
netleague             4.102  0.008   5.106
heatplot              3.654  0.012   5.443
heatplot.netmeta      3.536  0.004   5.053