Errors in running code in vignettes:
when running code in 'Ch_analysing_longitudinal_dataI.Rnw'

> rm(list = ls())

> s <- search()[-1]

> s <- s[-match(c("package:base", "package:stats", "package:graphics", 
+     "package:grDevices", "package:utils", "package:datasets", 
+     "packag ..." ... [TRUNCATED] 

> if (length(s) > 0) sapply(s, detach, character.only = TRUE)

> if (!file.exists("tables")) dir.create("tables")

> if (!file.exists("figures")) dir.create("figures")

> set.seed(290875)

> options(prompt = "R> ", continue = "+  ", width = 63, 
+     show.signif.stars = FALSE, SweaveHooks = list(leftpar = function() par(mai = par("mai") .... [TRUNCATED] 

> HSAURpkg <- require("HSAUR2")
Loading required package: HSAUR2
Loading required package: tools

> if (!HSAURpkg) stop("cannot load package ", sQuote("HSAUR2"))

> rm(HSAURpkg)

> a <- Sys.setlocale("LC_ALL", "C")

> book <- TRUE

> refs <- cbind(c("AItR", "DAGD", "SI", "CI", "ANOVA", 
+     "MLR", "GLM", "DE", "RP", "GAM", "SA", "ALDI", "ALDII", "SIMC", 
+     "MA", "PCA", "MDS ..." ... [TRUNCATED] 

> ch <- function(x) {
+     ch <- refs[which(refs[, 1] == x), ]
+     if (book) {
+         return(paste("Chapter~\\\\ref{", ch[1], "}", sep = ""))
+  .... [TRUNCATED] 

> if (file.exists("deparse.R")) source("deparse.R")

> setHook(packageEvent("lattice", "attach"), function(...) {
+     lattice.options(default.theme = function() standard.theme("pdf", 
+         color = .... [TRUNCATED] 

> book <- FALSE

> library("Matrix")

> library("lme4")

  When sourcing 'Ch_analysing_longitudinal_dataI.R':
Error: there is no package called 'lme4'
Execution halted
when running code in 'Ch_analysing_longitudinal_dataII.Rnw'

> rm(list = ls())

> s <- search()[-1]

> s <- s[-match(c("package:base", "package:stats", "package:graphics", 
+     "package:grDevices", "package:utils", "package:datasets", 
+     "packag ..." ... [TRUNCATED] 

> if (length(s) > 0) sapply(s, detach, character.only = TRUE)

> if (!file.exists("tables")) dir.create("tables")

> if (!file.exists("figures")) dir.create("figures")

> set.seed(290875)

> options(prompt = "R> ", continue = "+  ", width = 63, 
+     show.signif.stars = FALSE, SweaveHooks = list(leftpar = function() par(mai = par("mai") .... [TRUNCATED] 

> HSAURpkg <- require("HSAUR2")
Loading required package: HSAUR2
Loading required package: tools

> if (!HSAURpkg) stop("cannot load package ", sQuote("HSAUR2"))

> rm(HSAURpkg)

> a <- Sys.setlocale("LC_ALL", "C")

> book <- TRUE

> refs <- cbind(c("AItR", "DAGD", "SI", "CI", "ANOVA", 
+     "MLR", "GLM", "DE", "RP", "GAM", "SA", "ALDI", "ALDII", "SIMC", 
+     "MA", "PCA", "MDS ..." ... [TRUNCATED] 

> ch <- function(x) {
+     ch <- refs[which(refs[, 1] == x), ]
+     if (book) {
+         return(paste("Chapter~\\\\ref{", ch[1], "}", sep = ""))
+  .... [TRUNCATED] 

> if (file.exists("deparse.R")) source("deparse.R")

> setHook(packageEvent("lattice", "attach"), function(...) {
+     lattice.options(default.theme = function() standard.theme("pdf", 
+         color = .... [TRUNCATED] 

> book <- FALSE

> options(digits = 3)

> if (!interactive()) {
+     print.summary.gee <- function(x, digits = NULL, quote = FALSE, 
+         prefix = "", ...) {
+         if (is.null(digi .... [TRUNCATED] 

> library("gee")

> data("BtheB", package = "HSAUR2")

> BtheB$subject <- factor(rownames(BtheB))

> nobs <- nrow(BtheB)

> BtheB_long <- reshape(BtheB, idvar = "subject", varying = c("bdi.2m", 
+     "bdi.3m", "bdi.5m", "bdi.8m"), direction = "long")

> BtheB_long$time <- rep(c(2, 3, 5, 8), rep(nobs, 4))

> names(BtheB_long)[names(BtheB_long) == "treatment"] <- "trt"

> osub <- order(as.integer(BtheB_long$subject))

> BtheB_long <- BtheB_long[osub, ]

> btb_gee <- gee(bdi ~ bdi.pre + trt + length + drug, 
+     data = BtheB_long, id = subject, family = gaussian, corstr = "independence")
Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
running glm to get initial regression estimate
(Intercept)     bdi.pre    trtBtheB   length>6m     drugYes 
      3.569       0.582      -3.237       1.458      -3.741 

> btb_gee1 <- gee(bdi ~ bdi.pre + trt + length + drug, 
+     data = BtheB_long, id = subject, family = gaussian, corstr = "exchangeable")
Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
running glm to get initial regression estimate
(Intercept)     bdi.pre    trtBtheB   length>6m     drugYes 
      3.569       0.582      -3.237       1.458      -3.741 

> summary(btb_gee)

 GEE:  GENERALIZED LINEAR MODELS FOR DEPENDENT DATA
 gee S-function, version 4.13 modified 98/01/27 (1998) 

Model:
 Link:                      Identity 
 Variance to Mean Relation: Gaussian 
 Correlation Structure:     Independent 

Call:
gee(formula = bdi ~ bdi.pre + trt + length + drug, id = subject, 
    data = BtheB_long, family = gaussian, corstr = "independence")

Summary of Residuals:
    Min      1Q  Median      3Q     Max 
-21.650  -5.849   0.113   5.584  28.187 


Coefficients:
            Estimate Naive S.E. Naive z Robust S.E. Robust z
(Intercept)    3.569     1.4833    2.41      2.2695    1.572
bdi.pre        0.582     0.0564   10.32      0.0916    6.355
trtBtheB      -3.237     1.1296   -2.87      1.7746   -1.824
length>6m      1.458     1.1380    1.28      1.4826    0.983
drugYes       -3.741     1.1766   -3.18      1.7827   -2.099

Estimated Scale Parameter:  79.3
Number of Iterations:  1

Working Correlation
     [,1] [,2] [,3] [,4]
[1,]    1    0    0    0
[2,]    0    1    0    0
[3,]    0    0    1    0
[4,]    0    0    0    1

> summary(btb_gee1)

 GEE:  GENERALIZED LINEAR MODELS FOR DEPENDENT DATA
 gee S-function, version 4.13 modified 98/01/27 (1998) 

Model:
 Link:                      Identity 
 Variance to Mean Relation: Gaussian 
 Correlation Structure:     Exchangeable 

Call:
gee(formula = bdi ~ bdi.pre + trt + length + drug, id = subject, 
    data = BtheB_long, family = gaussian, corstr = "exchangeable")

Summary of Residuals:
   Min     1Q Median     3Q    Max 
-23.96  -6.64  -1.11   4.26  25.45 


Coefficients:
            Estimate Naive S.E. Naive z Robust S.E. Robust z
(Intercept)    3.023     2.3039  1.3122      2.2320   1.3544
bdi.pre        0.648     0.0823  7.8741      0.0835   7.7583
trtBtheB      -2.169     1.7664 -1.2281      1.7361  -1.2495
length>6m     -0.111     1.7309 -0.0643      1.5509  -0.0718
drugYes       -3.000     1.8257 -1.6430      1.7316  -1.7323

Estimated Scale Parameter:  81.7
Number of Iterations:  5

Working Correlation
      [,1]  [,2]  [,3]  [,4]
[1,] 1.000 0.676 0.676 0.676
[2,] 0.676 1.000 0.676 0.676
[3,] 0.676 0.676 1.000 0.676
[4,] 0.676 0.676 0.676 1.000

> data("respiratory", package = "HSAUR2")

> resp <- subset(respiratory, month > "0")

> resp$baseline <- rep(subset(respiratory, month == 
+     "0")$status, rep(4, 111))

> resp$nstat <- as.numeric(resp$status == "good")

> resp$month <- resp$month[, drop = TRUE]

> names(resp)[names(resp) == "treatment"] <- "trt"

> levels(resp$trt)[2] <- "trt"

> resp_glm <- glm(status ~ centre + trt + gender + baseline + 
+     age, data = resp, family = "binomial")

> resp_gee1 <- gee(nstat ~ centre + trt + gender + baseline + 
+     age, data = resp, family = "binomial", id = subject, corstr = "independence", 
+  .... [TRUNCATED] 
Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
running glm to get initial regression estimate
 (Intercept)      centre2       trttrt   gendermale 
     -0.9002       0.6716       1.2992       0.1192 
baselinegood          age 
      1.8820      -0.0182 

> resp_gee2 <- gee(nstat ~ centre + trt + gender + baseline + 
+     age, data = resp, family = "binomial", id = subject, corstr = "exchangeable", 
+  .... [TRUNCATED] 
Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
running glm to get initial regression estimate
 (Intercept)      centre2       trttrt   gendermale 
     -0.9002       0.6716       1.2992       0.1192 
baselinegood          age 
      1.8820      -0.0182 

> summary(resp_glm)

Call:
glm(formula = status ~ centre + trt + gender + baseline + age, 
    family = "binomial", data = resp)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-2.315  -0.855   0.434   0.895   1.925  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)
(Intercept)  -0.90017    0.33765   -2.67   0.0077
centre2       0.67160    0.23957    2.80   0.0051
trttrt        1.29922    0.23684    5.49  4.1e-08
gendermale    0.11924    0.29467    0.40   0.6857
baselinegood  1.88203    0.24129    7.80  6.2e-15
age          -0.01817    0.00886   -2.05   0.0404

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 608.93  on 443  degrees of freedom
Residual deviance: 483.22  on 438  degrees of freedom
AIC: 495.2

Number of Fisher Scoring iterations: 4


> summary(resp_gee1)

 GEE:  GENERALIZED LINEAR MODELS FOR DEPENDENT DATA
 gee S-function, version 4.13 modified 98/01/27 (1998) 

Model:
 Link:                      Logit 
 Variance to Mean Relation: Binomial 
 Correlation Structure:     Independent 

Call:
gee(formula = nstat ~ centre + trt + gender + baseline + age, 
    id = subject, data = resp, family = "binomial", corstr = "independence", 
    scale.fix = TRUE, scale.value = 1)

Summary of Residuals:
    Min      1Q  Median      3Q     Max 
-0.9313 -0.3062  0.0897  0.3302  0.8431 


Coefficients:
             Estimate Naive S.E. Naive z Robust S.E. Robust z
(Intercept)   -0.9002    0.33765  -2.666       0.460   -1.956
centre2        0.6716    0.23957   2.803       0.357    1.882
trttrt         1.2992    0.23684   5.486       0.351    3.704
gendermale     0.1192    0.29467   0.405       0.443    0.269
baselinegood   1.8820    0.24129   7.800       0.350    5.376
age           -0.0182    0.00886  -2.049       0.013   -1.397

Estimated Scale Parameter:  1
Number of Iterations:  1

Working Correlation
     [,1] [,2] [,3] [,4]
[1,]    1    0    0    0
[2,]    0    1    0    0
[3,]    0    0    1    0
[4,]    0    0    0    1

> summary(resp_gee2)

 GEE:  GENERALIZED LINEAR MODELS FOR DEPENDENT DATA
 gee S-function, version 4.13 modified 98/01/27 (1998) 

Model:
 Link:                      Logit 
 Variance to Mean Relation: Binomial 
 Correlation Structure:     Exchangeable 

Call:
gee(formula = nstat ~ centre + trt + gender + baseline + age, 
    id = subject, data = resp, family = "binomial", corstr = "exchangeable", 
    scale.fix = TRUE, scale.value = 1)

Summary of Residuals:
    Min      1Q  Median      3Q     Max 
-0.9313 -0.3062  0.0897  0.3302  0.8431 


Coefficients:
             Estimate Naive S.E. Naive z Robust S.E. Robust z
(Intercept)   -0.9002     0.4785  -1.881       0.460   -1.956
centre2        0.6716     0.3395   1.978       0.357    1.882
trttrt         1.2992     0.3356   3.871       0.351    3.704
gendermale     0.1192     0.4176   0.286       0.443    0.269
baselinegood   1.8820     0.3419   5.504       0.350    5.376
age           -0.0182     0.0126  -1.446       0.013   -1.397

Estimated Scale Parameter:  1
Number of Iterations:  1

Working Correlation
      [,1]  [,2]  [,3]  [,4]
[1,] 1.000 0.336 0.336 0.336
[2,] 0.336 1.000 0.336 0.336
[3,] 0.336 0.336 1.000 0.336
[4,] 0.336 0.336 0.336 1.000

> se <- summary(resp_gee2)$coefficients["trttrt", "Robust S.E."]

> coef(resp_gee2)["trttrt"] + c(-1, 1) * se * qnorm(0.975)
[1] 0.612 1.987

> exp(coef(resp_gee2)["trttrt"] + c(-1, 1) * se * qnorm(0.975))
[1] 1.84 7.29

> data("epilepsy", package = "HSAUR2")

> itp <- interaction(epilepsy$treatment, epilepsy$period)

> tapply(epilepsy$seizure.rate, itp, mean)
  placebo.1 Progabide.1   placebo.2 Progabide.2   placebo.3 
       9.36        8.58        8.29        8.42        8.79 
Progabide.3   placebo.4 Progabide.4 
       8.13        7.96        6.71 

> tapply(epilepsy$seizure.rate, itp, var)
  placebo.1 Progabide.1   placebo.2 Progabide.2   placebo.3 
      102.8       332.7        66.7       140.7       215.3 
Progabide.3   placebo.4 Progabide.4 
      193.0        58.2       126.9 

> layout(matrix(1:2, nrow = 1))

> ylim <- range(epilepsy$seizure.rate)

> placebo <- subset(epilepsy, treatment == "placebo")

> progabide <- subset(epilepsy, treatment == "Progabide")

> boxplot(seizure.rate ~ period, data = placebo, ylab = "Number of seizures", 
+     xlab = "Period", ylim = ylim, main = "Placebo")

> boxplot(seizure.rate ~ period, data = progabide, main = "Progabide", 
+     ylab = "Number of seizures", xlab = "Period", ylim = ylim)

> layout(matrix(1:2, nrow = 1))

> ylim <- range(log(epilepsy$seizure.rate + 1))

> boxplot(log(seizure.rate + 1) ~ period, data = placebo, 
+     main = "Placebo", ylab = "Log number of seizures", xlab = "Period", 
+     ylim = yli .... [TRUNCATED] 

> boxplot(log(seizure.rate + 1) ~ period, data = progabide, 
+     main = "Progabide", ylab = "Log number of seizures", xlab = "Period", 
+     ylim = .... [TRUNCATED] 

> per <- rep(log(2), nrow(epilepsy))

> epilepsy$period <- as.numeric(epilepsy$period)

> names(epilepsy)[names(epilepsy) == "treatment"] <- "trt"

> fm <- seizure.rate ~ base + age + trt + offset(per)

> epilepsy_glm <- glm(fm, data = epilepsy, family = "poisson")

> epilepsy_gee1 <- gee(fm, data = epilepsy, family = "poisson", 
+     id = subject, corstr = "independence", scale.fix = TRUE, 
+     scale.value = 1 .... [TRUNCATED] 
Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
running glm to get initial regression estimate
 (Intercept)         base          age trtProgabide 
     -0.1306       0.0227       0.0227      -0.1527 

> epilepsy_gee2 <- gee(fm, data = epilepsy, family = "poisson", 
+     id = subject, corstr = "exchangeable", scale.fix = TRUE, 
+     scale.value = 1 .... [TRUNCATED] 
Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
running glm to get initial regression estimate
 (Intercept)         base          age trtProgabide 
     -0.1306       0.0227       0.0227      -0.1527 

> epilepsy_gee3 <- gee(fm, data = epilepsy, family = "poisson", 
+     id = subject, corstr = "exchangeable", scale.fix = FALSE, 
+     scale.value =  .... [TRUNCATED] 
Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
running glm to get initial regression estimate
 (Intercept)         base          age trtProgabide 
     -0.1306       0.0227       0.0227      -0.1527 

> summary(epilepsy_glm)

Call:
glm(formula = fm, family = "poisson", data = epilepsy)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-4.436  -1.403  -0.503   0.484  12.322  

Coefficients:
              Estimate Std. Error z value Pr(>|z|)
(Intercept)  -0.130616   0.135619   -0.96   0.3355
base          0.022652   0.000509   44.48  < 2e-16
age           0.022740   0.004024    5.65  1.6e-08
trtProgabide -0.152701   0.047805   -3.19   0.0014

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 2521.75  on 235  degrees of freedom
Residual deviance:  958.46  on 232  degrees of freedom
AIC: 1732

Number of Fisher Scoring iterations: 5


> summary(epilepsy_gee1)

 GEE:  GENERALIZED LINEAR MODELS FOR DEPENDENT DATA
 gee S-function, version 4.13 modified 98/01/27 (1998) 

Model:
 Link:                      Logarithm 
 Variance to Mean Relation: Poisson 
 Correlation Structure:     Independent 

Call:
gee(formula = fm, id = subject, data = epilepsy, family = "poisson", 
    corstr = "independence", scale.fix = TRUE, scale.value = 1)

Summary of Residuals:
   Min     1Q Median     3Q    Max 
-4.920  0.181  1.707  4.885 69.966 


Coefficients:
             Estimate Naive S.E. Naive z Robust S.E. Robust z
(Intercept)   -0.1306   0.135619  -0.963     0.36515   -0.358
base           0.0227   0.000509  44.476     0.00124   18.332
age            0.0227   0.004024   5.651     0.01158    1.964
trtProgabide  -0.1527   0.047805  -3.194     0.17111   -0.892

Estimated Scale Parameter:  1
Number of Iterations:  1

Working Correlation
     [,1] [,2] [,3] [,4]
[1,]    1    0    0    0
[2,]    0    1    0    0
[3,]    0    0    1    0
[4,]    0    0    0    1

> summary(epilepsy_gee2)

 GEE:  GENERALIZED LINEAR MODELS FOR DEPENDENT DATA
 gee S-function, version 4.13 modified 98/01/27 (1998) 

Model:
 Link:                      Logarithm 
 Variance to Mean Relation: Poisson 
 Correlation Structure:     Exchangeable 

Call:
gee(formula = fm, id = subject, data = epilepsy, family = "poisson", 
    corstr = "exchangeable", scale.fix = TRUE, scale.value = 1)

Summary of Residuals:
   Min     1Q Median     3Q    Max 
-4.920  0.181  1.707  4.885 69.966 


Coefficients:
             Estimate Naive S.E. Naive z Robust S.E. Robust z
(Intercept)   -0.1306   0.200442  -0.652     0.36515   -0.358
base           0.0227   0.000753  30.093     0.00124   18.332
age            0.0227   0.005947   3.824     0.01158    1.964
trtProgabide  -0.1527   0.070655  -2.161     0.17111   -0.892

Estimated Scale Parameter:  1
Number of Iterations:  1

Working Correlation
      [,1]  [,2]  [,3]  [,4]
[1,] 1.000 0.395 0.395 0.395
[2,] 0.395 1.000 0.395 0.395
[3,] 0.395 0.395 1.000 0.395
[4,] 0.395 0.395 0.395 1.000

> summary(epilepsy_gee3)

 GEE:  GENERALIZED LINEAR MODELS FOR DEPENDENT DATA
 gee S-function, version 4.13 modified 98/01/27 (1998) 

Model:
 Link:                      Logarithm 
 Variance to Mean Relation: Poisson 
 Correlation Structure:     Exchangeable 

Call:
gee(formula = fm, id = subject, data = epilepsy, family = "poisson", 
    corstr = "exchangeable", scale.fix = FALSE, scale.value = 1)

Summary of Residuals:
   Min     1Q Median     3Q    Max 
-4.920  0.181  1.707  4.885 69.966 


Coefficients:
             Estimate Naive S.E. Naive z Robust S.E. Robust z
(Intercept)   -0.1306     0.4522  -0.289     0.36515   -0.358
base           0.0227     0.0017  13.339     0.00124   18.332
age            0.0227     0.0134   1.695     0.01158    1.964
trtProgabide  -0.1527     0.1594  -0.958     0.17111   -0.892

Estimated Scale Parameter:  5.09
Number of Iterations:  1

Working Correlation
      [,1]  [,2]  [,3]  [,4]
[1,] 1.000 0.395 0.395 0.395
[2,] 0.395 1.000 0.395 0.395
[3,] 0.395 0.395 1.000 0.395
[4,] 0.395 0.395 0.395 1.000

> library("lme4")

  When sourcing 'Ch_analysing_longitudinal_dataII.R':
Error: there is no package called 'lme4'
Execution halted
when running code in 'Ch_simultaneous_inference.Rnw'

> rm(list = ls())

> s <- search()[-1]

> s <- s[-match(c("package:base", "package:stats", "package:graphics", 
+     "package:grDevices", "package:utils", "package:datasets", 
+     "packag ..." ... [TRUNCATED] 

> if (length(s) > 0) sapply(s, detach, character.only = TRUE)

> if (!file.exists("tables")) dir.create("tables")

> if (!file.exists("figures")) dir.create("figures")

> set.seed(290875)

> options(prompt = "R> ", continue = "+  ", width = 63, 
+     show.signif.stars = FALSE, SweaveHooks = list(leftpar = function() par(mai = par("mai") .... [TRUNCATED] 

> HSAURpkg <- require("HSAUR2")
Loading required package: HSAUR2
Loading required package: tools

> if (!HSAURpkg) stop("cannot load package ", sQuote("HSAUR2"))

> rm(HSAURpkg)

> a <- Sys.setlocale("LC_ALL", "C")

> book <- TRUE

> refs <- cbind(c("AItR", "DAGD", "SI", "CI", "ANOVA", 
+     "MLR", "GLM", "DE", "RP", "GAM", "SA", "ALDI", "ALDII", "SIMC", 
+     "MA", "PCA", "MDS ..." ... [TRUNCATED] 

> ch <- function(x) {
+     ch <- refs[which(refs[, 1] == x), ]
+     if (book) {
+         return(paste("Chapter~\\\\ref{", ch[1], "}", sep = ""))
+  .... [TRUNCATED] 

> if (file.exists("deparse.R")) source("deparse.R")

> setHook(packageEvent("lattice", "attach"), function(...) {
+     lattice.options(default.theme = function() standard.theme("pdf", 
+         color = .... [TRUNCATED] 

> book <- FALSE

> library("multcomp")
Loading required package: mvtnorm
Loading required package: survival
Loading required package: TH.data
Loading required package: MASS

Attaching package: 'TH.data'

The following object is masked from 'package:MASS':

    geyser


> library("coin")

> library("sandwich")

> library("lme4")

  When sourcing 'Ch_simultaneous_inference.R':
Error: there is no package called 'lme4'
Execution halted

  'Ch_analysing_longitudinal_dataI.Rnw' using 'UTF-8'... failed
  'Ch_analysing_longitudinal_dataII.Rnw' using 'UTF-8'... failed
  'Ch_analysis_of_variance.Rnw' using 'UTF-8'... OK
  'Ch_cluster_analysis.Rnw' using 'UTF-8'... OK
  'Ch_conditional_inference.Rnw' using 'UTF-8'... OK
  'Ch_density_estimation.Rnw' using 'UTF-8'... OK
  'Ch_errata.Rnw' using 'UTF-8'... OK
  'Ch_gam.Rnw' using 'UTF-8'... OK
  'Ch_graphical_display.Rnw' using 'UTF-8'... OK
  'Ch_introduction_to_R.Rnw' using 'UTF-8'... OK
  'Ch_logistic_regression_glm.Rnw' using 'UTF-8'... OK
  'Ch_meta_analysis.Rnw' using 'UTF-8'... OK
  'Ch_multidimensional_scaling.Rnw' using 'UTF-8'... OK
  'Ch_multiple_linear_regression.Rnw' using 'UTF-8'... OK
  'Ch_principal_components_analysis.Rnw' using 'UTF-8'... OK
  'Ch_recursive_partitioning.Rnw' using 'UTF-8'... OK
  'Ch_simple_inference.Rnw' using 'UTF-8'... OK
  'Ch_simultaneous_inference.Rnw' using 'UTF-8'... failed
  'Ch_survival_analysis.Rnw' using 'UTF-8'... OK