• using R Under development (unstable) (2026-07-23 r90295)
• using platform: x86_64-pc-linux-gnu
• R was compiled by     clang version 22.1.8 (https://github.com/llvm/llvm-project ca7933e47d3a3451d81e72ac174dcb5aa28b59d1)     flang version 22.1.8 (https://github.com/llvm/llvm-project ca7933e47d3a3451d81e72ac174dcb5aa28b59d1)
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• this is package ‘party’ version ‘1.3-21’
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• checking examples ... [8s/12s] ERROR Running examples in ‘party-Ex.R’ failed The error most likely occurred in: > ### Name: mob > ### Title: Model-based Recursive Partitioning > ### Aliases: mob mob-class coef.mob deviance.mob fitted.mob logLik.mob > ### predict.mob print.mob residuals.mob sctest.mob summary.mob > ### weights.mob > ### Keywords: tree > > ### ** Examples > > > set.seed(290875) > > if(require("mlbench")) { + + ## recursive partitioning of a linear regression model + ## load data + data("BostonHousing", package = "mlbench") + ## and transform variables appropriately (for a linear regression) + BostonHousing$lstat <- log(BostonHousing$lstat) + BostonHousing$rm <- BostonHousing$rm^2 + ## as well as partitioning variables (for fluctuation testing) + BostonHousing$chas <- factor(BostonHousing$chas, levels = 0:1, + labels = c("no", "yes")) + BostonHousing$rad <- factor(BostonHousing$rad, ordered = TRUE) + + ## partition the linear regression model medv ~ lstat + rm + ## with respect to all remaining variables: + fmBH <- mob(medv ~ lstat + rm | zn + indus + chas + nox + age + + dis + rad + tax + crim + b + ptratio, + control = mob_control(minsplit = 40), data = BostonHousing, + model = linearModel) + + ## print the resulting tree + fmBH + ## or better visualize it + plot(fmBH) + + ## extract coefficients in all terminal nodes + coef(fmBH) + ## look at full summary, e.g., for node 7 + summary(fmBH, node = 7) + ## results of parameter stability tests for that node + sctest(fmBH, node = 7) + ## -> no further significant instabilities (at 5% level) + + ## compute mean squared error (on training data) + mean((BostonHousing$medv - fitted(fmBH))^2) + mean(residuals(fmBH)^2) + deviance(fmBH)/sum(weights(fmBH)) + + ## evaluate logLik and AIC + logLik(fmBH) + AIC(fmBH) + ## (Note that this penalizes estimation of error variances, which + ## were treated as nuisance parameters in the fitting process.) + + + ## recursive partitioning of a logistic regression model + ## load data + data("PimaIndiansDiabetes", package = "mlbench") + ## partition logistic regression diabetes ~ glucose + ## wth respect to all remaining variables + fmPID <- mob(diabetes ~ glucose | pregnant + pressure + triceps + + insulin + mass + pedigree + age, + data = PimaIndiansDiabetes, model = glinearModel, + family = binomial()) + + ## fitted model + coef(fmPID) + plot(fmPID) + plot(fmPID, tp_args = list(cdplot = TRUE)) + } Loading required package: mlbench Warning in data("PimaIndiansDiabetes", package = "mlbench") :   data set ‘PimaIndiansDiabetes’ not found Error: object 'PimaIndiansDiabetes' not found Execution halted
• checking for unstated dependencies in ‘tests’ ... OK
• checking tests ... [62s/83s] ERROR   Running ‘Distributions.R’   Comparing ‘Distributions.Rout’ to ‘Distributions.Rout.save’ ... OK   Running ‘LinearStatistic-regtest.R’   Comparing ‘LinearStatistic-regtest.Rout’ to ‘LinearStatistic-regtest.Rout.save’ ... OK   Running ‘Predict-regtest.R’   Comparing ‘Predict-regtest.Rout’ to ‘Predict-regtest.Rout.save’ ... OK   Running ‘RandomForest-regtest.R’ [8s/11s]   Comparing ‘RandomForest-regtest.Rout’ to ‘RandomForest-regtest.Rout.save’ ... OK   Running ‘TestStatistic-regtest.R’   Comparing ‘TestStatistic-regtest.Rout’ to ‘TestStatistic-regtest.Rout.save’ ... OK   Running ‘TreeGrow-regtest.R’ [8s/11s]   Comparing ‘TreeGrow-regtest.Rout’ to ‘TreeGrow-regtest.Rout.save’ ... OK   Running ‘Utils-regtest.R’   Comparing ‘Utils-regtest.Rout’ to ‘Utils-regtest.Rout.save’ ... OK   Running ‘bugfixes.R’ [19s/25s]   Comparing ‘bugfixes.Rout’ to ‘bugfixes.Rout.save’ ... OK   Running ‘mob.R’ Running the tests in ‘tests/mob.R’ failed. Complete output:   > library("party")   Loading required package: grid   Loading required package: mvtnorm   Loading required package: modeltools   Loading required package: stats4   Loading required package: strucchange   Loading required package: zoo      Attaching package: 'zoo'      The following objects are masked from 'package:base':          as.Date, as.Date.numeric      Loading required package: sandwich   >   > data("BostonHousing", package = "mlbench")   > BostonHousing$lstat <- log(BostonHousing$lstat)   > BostonHousing$rm <- BostonHousing$rm^2   > BostonHousing$chas <- factor(BostonHousing$chas, levels = 0:1, labels = c("no", "yes"))   > BostonHousing$rad <- factor(BostonHousing$rad, ordered = TRUE)   > fmBH <- mob(medv ~ lstat + rm | zn + indus + chas + nox + age + dis + rad + tax + crim + b + ptratio,   + control = mob_control(minsplit = 40, verbose = TRUE),   + data = BostonHousing, model = linearModel)      -------------------------------------------   Fluctuation tests of splitting variables:                       zn indus chas nox age   statistic 3.363356e+01 6.532322e+01 2.275635e+01 8.136281e+01 3.675850e+01   p.value 1.023987e-04 1.363602e-11 4.993053e-04 3.489797e-15 2.263798e-05                      dis rad tax crim b   statistic 6.848533e+01 1.153641e+02 9.068440e+01 8.655065e+01 3.627629e+01   p.value 2.693904e-12 7.087680e-13 2.735524e-17 2.356348e-16 2.860686e-05                  ptratio   statistic 7.221524e+01   p.value 3.953623e-13      Best splitting variable: tax   Perform split? yes   -------------------------------------------      Node properties:   tax <= 432; criterion = 1, statistic = 115.364      -------------------------------------------   Fluctuation tests of splitting variables:                       zn indus chas nox age   statistic 27.785009791 21.3329346 8.0272421 23.774323202 11.9204284   p.value 0.001494064 0.0285193 0.4005192 0.009518732 0.7666366                      dis rad tax crim b   statistic 24.268011081 50.481593270 3.523250e+01 3.276813e+01 9.0363245   p.value 0.007601532 0.003437763 4.275527e-05 1.404487e-04 0.9871502                  ptratio   statistic 4.510680e+01   p.value 3.309747e-07      Best splitting variable: ptratio   Perform split? yes   -------------------------------------------      Node properties:   ptratio <= 15.2; criterion = 1, statistic = 50.482      -------------------------------------------   Fluctuation tests of splitting variables:                       zn indus chas nox age   statistic 3.233350e+01 22.26864036 12.93407112 22.10510234 20.41295354   p.value 1.229678e-04 0.01504788 0.05259509 0.01622098 0.03499731                    dis rad tax crim b   statistic 17.7204735 5.526565e+01 2.879128e+01 20.28503194 6.5549665   p.value 0.1091769 7.112214e-04 6.916307e-04 0.03706934 0.9999522                  ptratio   statistic 4.789850e+01   p.value 4.738855e-08      Best splitting variable: ptratio   Perform split? yes   -------------------------------------------      Node properties:   ptratio <= 19.6; criterion = 1, statistic = 55.266      -------------------------------------------   Fluctuation tests of splitting variables:                    zn indus chas nox age dis   statistic 14.971474 14.6477733 7.1172962 14.3455158 8.2176363 16.1112185   p.value 0.280361 0.3134649 0.5405005 0.3467974 0.9906672 0.1847818                     rad tax crim b ptratio   statistic 43.17824350 3.447271e+01 9.340075 8.7773142 10.8469969   p.value 0.03281124 4.281939e-05 0.952996 0.9772696 0.8202694      Best splitting variable: tax   Perform split? yes   -------------------------------------------      Node properties:   tax <= 265; criterion = 1, statistic = 43.178      -------------------------------------------   Fluctuation tests of splitting variables:                    zn indus chas nox age dis   statistic 11.998039 7.3971233 7.227770 9.2936189 14.3023962 8.9239826   p.value 0.574642 0.9931875 0.522447 0.9119621 0.2886603 0.9389895                    rad tax crim b ptratio   statistic 33.1746444 16.6666129 11.7143758 9.9050903 11.5927528   p.value 0.3926249 0.1206412 0.6153455 0.8539893 0.6328381      Best splitting variable: tax   Perform split? no   -------------------------------------------      -------------------------------------------   Fluctuation tests of splitting variables:                     zn indus chas nox age dis   statistic 10.9187926 9.0917078 2.754081e+01 17.39203006 4.6282349 11.9581600   p.value 0.7091039 0.9172303 4.987667e-05 0.08922543 0.9999992 0.5607267                   rad tax crim b ptratio   statistic 0.2557803 10.9076165 3.711175 3.158329 9.8865054   p.value 1.0000000 0.7106612 1.000000 1.000000 0.8410064      Best splitting variable: chas   Perform split? yes   -------------------------------------------      Splitting factor variable, objective function:    no   Inf      No admissable split found in 'chas'   > fmBH   1) tax <= 432; criterion = 1, statistic = 115.364     2) ptratio <= 15.2; criterion = 1, statistic = 50.482       3)* weights = 72   Terminal node model   Linear model with coefficients:   (Intercept) lstat rm        9.2349 -4.9391 0.6859        2) ptratio > 15.2       4) ptratio <= 19.6; criterion = 1, statistic = 55.266         5) tax <= 265; criterion = 1, statistic = 43.178           6)* weights = 63   Terminal node model   Linear model with coefficients:   (Intercept) lstat rm        3.9637 -2.7663 0.6881            5) tax > 265           7)* weights = 162   Terminal node model   Linear model with coefficients:   (Intercept) lstat rm       -1.7984 -0.2677 0.6539          4) ptratio > 19.6         8)* weights = 56   Terminal node model   Linear model with coefficients:   (Intercept) lstat rm       17.5865 -4.6190 0.3387      1) tax > 432     9)* weights = 153   Terminal node model   Linear model with coefficients:   (Intercept) lstat rm       68.2971 -16.3540 -0.1478      > summary(fmBH)   $`3`      Call:   NULL      Weighted Residuals:      Min 1Q Median 3Q Max   -7.910 0.000 0.000 0.000 6.632      Coefficients:               Estimate Std. Error t value Pr(>|t|)   (Intercept) 9.23488 3.95128 2.337 0.0223 *   lstat -4.93910 0.88285 -5.595 4.14e-07 ***   rm 0.68591 0.05136 13.354 < 2e-16 ***   ---   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1      Residual standard error: 3.413 on 69 degrees of freedom   Multiple R-squared: 0.922, Adjusted R-squared: 0.9197   F-statistic: 407.8 on 2 and 69 DF, p-value: < 2.2e-16         $`6`      Call:   NULL      Weighted Residuals:      Min 1Q Median 3Q Max   -4.614 0.000 0.000 0.000 12.473      Coefficients:               Estimate Std. Error t value Pr(>|t|)   (Intercept) 3.96372 5.00781 0.792 0.43177   lstat -2.76629 1.00406 -2.755 0.00776 **   rm 0.68813 0.07716 8.918 1.36e-12 ***   ---   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1      Residual standard error: 3.2 on 60 degrees of freedom   Multiple R-squared: 0.8176, Adjusted R-squared: 0.8115   F-statistic: 134.5 on 2 and 60 DF, p-value: < 2.2e-16         $`7`      Call:   NULL      Weighted Residuals:      Min 1Q Median 3Q Max   -9.092 0.000 0.000 0.000 10.236      Coefficients:               Estimate Std. Error t value Pr(>|t|)   (Intercept) -1.79839 2.84702 -0.632 0.529   lstat -0.26771 0.69581 -0.385 0.701   rm 0.65389 0.03757 17.404 <2e-16 ***   ---   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1      Residual standard error: 2.652 on 159 degrees of freedom   Multiple R-squared: 0.8173, Adjusted R-squared: 0.815   F-statistic: 355.6 on 2 and 159 DF, p-value: < 2.2e-16         $`8`      Call:   NULL      Weighted Residuals:      Min 1Q Median 3Q Max   -8.466 0.000 0.000 0.000 4.947      Coefficients:               Estimate Std. Error t value Pr(>|t|)   (Intercept) 17.58649 4.21666 4.171 0.000113 ***   lstat -4.61897 0.84025 -5.497 1.13e-06 ***   rm 0.33867 0.07574 4.472 4.13e-05 ***   ---   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1      Residual standard error: 2.197 on 53 degrees of freedom   Multiple R-squared: 0.6446, Adjusted R-squared: 0.6312   F-statistic: 48.07 on 2 and 53 DF, p-value: 1.238e-12         $`9`      Call:   NULL      Weighted Residuals:      Min 1Q Median 3Q Max   -10.56 0.00 0.00 0.00 24.28      Coefficients:                Estimate Std. Error t value Pr(>|t|)   (Intercept) 68.29709 3.83284 17.819 < 2e-16 ***   lstat -16.35401 0.96577 -16.934 < 2e-16 ***   rm -0.14779 0.05047 -2.928 0.00394 **   ---   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1      Residual standard error: 4.689 on 150 degrees of freedom   Multiple R-squared: 0.6649, Adjusted R-squared: 0.6604   F-statistic: 148.8 on 2 and 150 DF, p-value: < 2.2e-16         >   > ### check for one-node tree   > fmBH <- try(mob(medv ~ lstat + rm | zn, control = mob_control(minsplit = 4000, verbose = TRUE),   + data = BostonHousing, model = linearModel))   > stopifnot(class(fmBH) != "try-error")   >   >   > data("PimaIndiansDiabetes", package = "mlbench")   Warning message:   In data("PimaIndiansDiabetes", package = "mlbench") :     data set 'PimaIndiansDiabetes' not found   > fmPID <- mob(diabetes ~ glucose | pregnant + pressure + triceps + insulin + mass + pedigree + age,   + control = mob_control(verbose = TRUE),   + data = PimaIndiansDiabetes, model = glinearModel, family = binomial())   Error: object 'PimaIndiansDiabetes' not found   Execution halted
• checking for unstated dependencies in vignettes ... OK
• checking package vignettes ... OK
• checking running R code from vignettes ... [12s/15s] ERROR Errors in running code in vignettes: when running code in ‘MOB.Rnw’ > require("party") Loading required package: party Loading required package: grid Loading required package: mvtnorm Loading required package: modeltools Loading required package: stats4 Loading required package: strucchange Loading required package: zoo Attaching package: ‘zoo’ The following objects are masked from ‘package:base’:     as.Date, as.Date.numeric Loading required package: sandwich > options(useFancyQuotes = FALSE) > library("party") > data("BostonHousing", package = "mlbench") > BostonHousing$lstat <- log(BostonHousing$lstat) > BostonHousing$rm <- BostonHousing$rm^2 > BostonHousing$chas <- factor(BostonHousing$chas, levels = 0:1, + labels = c("no", "yes")) > BostonHousing$rad <- factor(BostonHousing$rad, ordered = TRUE) > ctrl <- mob_control(alpha = 0.05, bonferroni = TRUE, + minsplit = 40, objfun = deviance, verbose = TRUE) > fmBH <- mob(medv ~ lstat + rm | zn + indus + chas + + nox + age + dis + rad + tax + crim + b + ptratio, data = BostonHousing, + control = .... [TRUNCATED] ------------------------------------------- Fluctuation tests of splitting variables:                     zn indus chas nox age statistic 3.363356e+01 6.532322e+01 2.275635e+01 8.136281e+01 3.675850e+01 p.value 1.023987e-04 1.363602e-11 4.993053e-04 3.489797e-15 2.263798e-05                    dis rad tax crim b statistic 6.848533e+01 1.153641e+02 9.068440e+01 8.655065e+01 3.627629e+01 p.value 2.693904e-12 7.087680e-13 2.735524e-17 2.356348e-16 2.860686e-05                ptratio statistic 7.221524e+01 p.value 3.953623e-13 Best splitting variable: tax Perform split? yes ------------------------------------------- Node properties: tax <= 432; criterion = 1, statistic = 115.364 ------------------------------------------- Fluctuation tests of splitting variables:                     zn indus chas nox age statistic 27.785009791 21.3329346 8.0272421 23.774323202 11.9204284 p.value 0.001494064 0.0285193 0.4005192 0.009518732 0.7666366                    dis rad tax crim b statistic 24.268011081 50.481593270 3.523250e+01 3.276813e+01 9.0363245 p.value 0.007601532 0.003437763 4.275527e-05 1.404487e-04 0.9871502                ptratio statistic 4.510680e+01 p.value 3.309747e-07 Best splitting variable: ptratio Perform split? yes ------------------------------------------- Node properties: ptratio <= 15.2; criterion = 1, statistic = 50.482 ------------------------------------------- Fluctuation tests of splitting variables:                     zn indus chas nox age statistic 3.233350e+01 22.26864036 12.93407112 22.10510234 20.41295354 p.value 1.229678e-04 0.01504788 0.05259509 0.01622098 0.03499731                  dis rad tax crim b statistic 17.7204735 5.526565e+01 2.879128e+01 20.28503194 6.5549665 p.value 0.1091769 7.112214e-04 6.916307e-04 0.03706934 0.9999522                ptratio statistic 4.789850e+01 p.value 4.738855e-08 Best splitting variable: ptratio Perform split? yes ------------------------------------------- Node properties: ptratio <= 19.6; criterion = 1, statistic = 55.266 ------------------------------------------- Fluctuation tests of splitting variables:                  zn indus chas nox age dis statistic 14.971474 14.6477733 7.1172962 14.3455158 8.2176363 16.1112185 p.value 0.280361 0.3134649 0.5405005 0.3467974 0.9906672 0.1847818                   rad tax crim b ptratio statistic 43.17824350 3.447271e+01 9.340075 8.7773142 10.8469969 p.value 0.03281124 4.281939e-05 0.952996 0.9772696 0.8202694 Best splitting variable: tax Perform split? yes ------------------------------------------- Node properties: tax <= 265; criterion = 1, statistic = 43.178 ------------------------------------------- Fluctuation tests of splitting variables:                  zn indus chas nox age dis statistic 11.998039 7.3971233 7.227770 9.2936189 14.3023962 8.9239826 p.value 0.574642 0.9931875 0.522447 0.9119621 0.2886603 0.9389895                  rad tax crim b ptratio statistic 33.1746444 16.6666129 11.7143758 9.9050903 11.5927528 p.value 0.3926249 0.1206412 0.6153455 0.8539893 0.6328381 Best splitting variable: tax Perform split? no ------------------------------------------- ------------------------------------------- Fluctuation tests of splitting variables:                   zn indus chas nox age dis statistic 10.9187926 9.0917078 2.754081e+01 17.39203006 4.6282349 11.9581600 p.value 0.7091039 0.9172303 4.987667e-05 0.08922543 0.9999992 0.5607267                 rad tax crim b ptratio statistic 0.2557803 10.9076165 3.711175 3.158329 9.8865054 p.value 1.0000000 0.7106612 1.000000 1.000000 0.8410064 Best splitting variable: chas Perform split? yes ------------------------------------------- Splitting factor variable, objective function:  no Inf No admissable split found in 'chas' > fmBH 1) tax <= 432; criterion = 1, statistic = 115.364   2) ptratio <= 15.2; criterion = 1, statistic = 50.482     3)* weights = 72 Terminal node model Linear model with coefficients: (Intercept) lstat rm      9.2349 -4.9391 0.6859   2) ptratio > 15.2     4) ptratio <= 19.6; criterion = 1, statistic = 55.266       5) tax <= 265; criterion = 1, statistic = 43.178         6)* weights = 63 Terminal node model Linear model with coefficients: (Intercept) lstat rm      3.9637 -2.7663 0.6881       5) tax > 265         7)* weights = 162 Terminal node model Linear model with coefficients: (Intercept) lstat rm     -1.7984 -0.2677 0.6539     4) ptratio > 19.6       8)* weights = 56 Terminal node model Linear model with coefficients: (Intercept) lstat rm     17.5865 -4.6190 0.3387 1) tax > 432   9)* weights = 153 Terminal node model Linear model with coefficients: (Intercept) lstat rm     68.2971 -16.3540 -0.1478 > plot(fmBH) > coef(fmBH)   (Intercept) lstat rm 3 9.234880 -4.939096 0.6859136 6 3.963720 -2.766287 0.6881287 7 -1.798387 -0.267707 0.6538864 8 17.586490 -4.618975 0.3386744 9 68.297087 -16.354006 -0.1477939 > summary(fmBH, node = 7) Call: NULL Weighted Residuals:    Min 1Q Median 3Q Max -9.092 0.000 0.000 0.000 10.236 Coefficients:             Estimate Std. Error t value Pr(>|t|) (Intercept) -1.79839 2.84702 -0.632 0.529 lstat -0.26771 0.69581 -0.385 0.701 rm 0.65389 0.03757 17.404 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.652 on 159 degrees of freedom Multiple R-squared: 0.8173, Adjusted R-squared: 0.815 F-statistic: 355.6 on 2 and 159 DF, p-value: < 2.2e-16 > sctest(fmBH, node = 7)                  zn indus chas nox age dis statistic 11.998039 7.3971233 7.227770 9.2936189 14.3023962 8.9239826 p.value 0.574642 0.9931875 0.522447 0.9119621 0.2886603 0.9389895                  rad tax crim b ptratio statistic 33.1746444 16.6666129 11.7143758 9.9050903 11.5927528 p.value 0.3926249 0.1206412 0.6153455 0.8539893 0.6328381 > mean(residuals(fmBH)^2) [1] 12.03518 > logLik(fmBH) 'log Lik.' -1310.506 (df=24) > AIC(fmBH) [1] 2669.013 > nt <- NROW(coef(fmBH)) > nk <- NCOL(coef(fmBH)) > data("PimaIndiansDiabetes2", package = "mlbench") Warning in data("PimaIndiansDiabetes2", package = "mlbench") :   data set 'PimaIndiansDiabetes2' not found > PimaIndiansDiabetes <- na.omit(PimaIndiansDiabetes2[, + -c(4, 5)])   When sourcing 'MOB.R': Error: error in evaluating the argument 'object' in selecting a method for function 'na.omit': object 'PimaIndiansDiabetes2' not found Execution halted   ‘MOB.Rnw’... failed   ‘party.Rnw’... OK
• checking re-building of vignette outputs ... [25s/28s] ERROR Error(s) in re-building vignettes: --- re-building ‘MOB.Rnw’ using Sweave Loading required package: party Loading required package: grid Loading required package: mvtnorm Loading required package: modeltools Loading required package: stats4 Loading required package: strucchange Loading required package: zoo Attaching package: ‘zoo’ The following objects are masked from ‘package:base’:     as.Date, as.Date.numeric Loading required package: sandwich Warning in data("PimaIndiansDiabetes2", package = "mlbench") :   data set 'PimaIndiansDiabetes2' not found Error: processing vignette 'MOB.Rnw' failed with diagnostics:  chunk 16 Error in (function (cond) :   error in evaluating the argument 'object' in selecting a method for function 'na.omit': object 'PimaIndiansDiabetes2' not found --- failed re-building 'MOB.Rnw' --- re-building ‘party.Rnw’ using Sweave Loading required package: party Loading required package: grid Loading required package: mvtnorm Loading required package: modeltools Loading required package: stats4 Loading required package: strucchange Loading required package: zoo Attaching package: ‘zoo’ The following objects are masked from ‘package:base’:     as.Date, as.Date.numeric Loading required package: sandwich Loading required package: coin Loading required package: survival --- finished re-building ‘party.Rnw’ SUMMARY: processing the following file failed:   ‘MOB.Rnw’ Error: Vignette re-building failed. Execution halted
• checking PDF version of manual ... [9s/10s] OK
• checking HTML version of manual ... OK
• checking for non-standard things in the check directory ... OK
• checking for detritus in the temp directory ... OK
• DONE

Status: 4 ERRORs