Running ‘confintrob_test.R’ [3s/3s]
  Running ‘parallelTest.R’ [2s/6s]
  Running ‘varComprob_createParamSampleFunction.R’ [2s/3s]
Running the tests in ‘tests/confintrob_test.R’ failed.
Complete output:
  > require(confintROB)
  Loading required package: confintROB
  > require(lme4)
  Loading required package: lme4
  Loading required package: Matrix
  > require(robustlmm)
  Loading required package: robustlmm
  Warning message:
  In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
    there is no package called 'robustlmm'
  > 
  > test <- function(object, ...) {
  +   doTest <- function(method, ...) {
  +     cat(
  +       "Running test for object of class ",
  +       class(object),
  +       " with arguments method = ",
  +       method,
  +       "\n"
  +     )
  +     set.seed(1234)
  +     result <-
  +       confintROB(
  +         object = object,
  +         level = .95,
  +         method = method,
  +         nsim = 10,
  +         boot.type = "parametric",
  +         clusterID = "Subject",
  +         ...
  +       )
  +     print(result, digits = 2)
  +   }
  +   
  +   for (method in c("boot", "BCa", "Wald")) {
  +     doTest(method, ...)
  +   }
  + }
  > 
  > test.wild <- function(object, ...) {
  +   cat("Running test.wild for object of class ",  class(object), "\n")
  +   set.seed(123)
  +   y 		 <- getME(object, "y")
  +   X      <- as.matrix(getME(object, "X"))
  +   id     <- getME(object, "flist")[[1]]
  +   bet    <- unname(fixef(object))
  +   result <-
  +     confintROB:::createWildSampleFunction(y = y,
  +                                           X = X,
  +                                           id = id,
  +                                           bet = bet)(1)
  +   print(result, digits = 5)
  + }
  > 
  > 
  > control <- lmerControl(check.conv.grad = "ignore")
  > 
  > model.ds.ML <-
  +   lmer(Yield ~ (1 | Batch),
  +        Dyestuff,
  +        REML = FALSE,
  +        control = control)
  > print(summary(model.ds.ML), digits = 2)
  Linear mixed model fit by maximum likelihood  ['lmerMod']
  Formula: Yield ~ (1 | Batch)
     Data: Dyestuff
  Control: control
  
        AIC       BIC    logLik -2*log(L)  df.resid 
      333.3     337.5    -163.7     327.3        27 
  
  Scaled residuals: 
     Min     1Q Median     3Q    Max 
   -1.43  -0.80   0.15   0.77   1.80 
  
  Random effects:
   Groups   Name        Variance Std.Dev.
   Batch    (Intercept) 1388     37      
   Residual             2451     50      
  Number of obs: 30, groups:  Batch, 6
  
  Fixed effects:
              Estimate Std. Error t value
  (Intercept)     1528         18      86
  > test(model.ds.ML)
  Running test for object of class  lmerMod  with arguments method =  boot 
  Ignoring argument 'clusterID' as it's not needed for this combination of arguments
                           2.5 % 97.5 %
  (Intercept)               1503   1535
  Sigma Batch (Intercept)     17     42
  Sigma Residual              40     61
  attr(,"fullResults")
    Full results of confintROB, a list with components:
     "Percentile", "bootstrap_estimates" 
  Running test for object of class  lmerMod  with arguments method =  BCa 
  Ignoring argument 'clusterID' as it's not needed for this combination of arguments
                           2.5 % 97.5 %
  (Intercept)               1500   1534
  Sigma Batch (Intercept)     32     44
  Sigma Residual              41     62
  attr(,"fullResults")
    Full results of confintROB, a list with components:
     "BCa", "Percentile", "bootstrap_estimates", "biasBCa", "acc" 
  Running test for object of class  lmerMod  with arguments method =  Wald 
              2.5 % 97.5 %
  (Intercept)  1493   1562
  > test.wild(model.ds.ML, .export = "control")
  Running test.wild for object of class  lmerMod 
  [[1]]
       1      2      3      4      5      6      7      8      9     10     11 
  1516.5 1582.6 1582.6 1532.2 1494.4 1548.1 1572.9 1465.6 1581.1 1473.9 1485.0 
      12     13     14     15     16     17     18     19     20     21     22 
  1513.3 1478.7 1538.5 1507.0 1391.4 1383.1 1638.9 1424.4 1556.4 1638.9 1696.6 
      23     24     25     26     27     28     29     30 
  1506.9 1704.9 1688.4 1532.2 1573.2 1576.3 1557.4 1579.5 
  
  > 
  > model.ds.DAStau <-
  +   rlmer(
  +     Yield ~ (1 | Batch),
  +     Dyestuff,
  +     rho.sigma.e = psi2propII(smoothPsi, k = 2.28),
  +     rho.b = chgDefaults(smoothPsi, k = 5.14, s = 10),
  +     rho.sigma.b = chgDefaults(smoothPsi, k = 5.14, s = 10),
  +     init = function(...)
  +       lmer(..., control = control)
  +   )
  Error in rlmer(Yield ~ (1 | Batch), Dyestuff, rho.sigma.e = psi2propII(smoothPsi,  : 
    could not find function "rlmer"
  Execution halted
Running the tests in ‘tests/varComprob_createParamSampleFunction.R’ failed.
Complete output:
  > require(confintROB)
  Loading required package: confintROB
  > require(lme4)
  Loading required package: lme4
  Loading required package: Matrix
  > require(robustvarComp)
  Loading required package: robustvarComp
  Warning message:
  In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
    there is no package called 'robustvarComp'
  > 
  > test.varComprob <-
  +   function(object, data = sleepstudy) {
  +     cat("Running test for object of class ", class(object), "\n")
  +     sample <- confintROB:::createParamSampleFunction(model = object,
  +                                                      data = data)
  +     set.seed(123)
  +     result11 <- c(sample(1), sample(1))
  +     set.seed(123)
  +     result2 <- sample(2)
  +     names(result11) <- names(result2)
  +     stopifnot(all.equal(result11, result2))
  +     return(result2)
  +   }
  > 
  > participant <- sleepstudy$Subject
  > within <- sleepstudy$Days
  > 
  > # Build the argument "groups" of the varComprob() function
  > n <- length(unique(participant)) # the number of participants
  > J <-
  +   length(unique(within)) # the number of repeated observations per participant
  > groups <-
  +   cbind(rep(1:J, each = n), rep((1:n), J)) # a numeric matrix with two columns used to group the observations according to participant.
  > 
  > # Build the argument "varcov" of the varComprob() function
  > z1 <-
  +   rep(1, J) #Value for intercept (=1) for the J observations by clusters
  > z2 <- unique(within) # Value for the time variable
  > 
  > K <-
  +   list(
  +     # Matrix for intercept
  +     sigma2_u0 = tcrossprod(z1, z1),
  +     # Matrix of interaction Intercept by time variable
  +     Covariance = tcrossprod(z1, z2) + tcrossprod(z2, z1),
  +     # Matrix for time variable
  +     sigma2_u1 = tcrossprod(z2, z2)
  +   )
  > 
  > # Estimation with S-estimator
  > suppressWarnings(
  +   model.S <-
  +     varComprob(
  +       Reaction ~ 1 + Days,
  +       groups = groups,
  +       data = sleepstudy,
  +       varcov = K,
  +       control = varComprob.control(
  +         lower = c(0, -Inf, 0),
  +         method = "S",
  +         psi = "rocke",
  +         max.it = 1,
  +         init = list(
  +           beta = c("(Intercept)" = 253.835569743834, Days = 10.7736608268214),
  +           gamma = c(
  +             sigma2_u0 = 1.59549700005736,
  +             Covariance = -0.0711447985744645,
  +             sigma2_u1 = 0.0765023178239254
  +           ),
  +           eta0 = c("error variance" = 692.556625895202),
  +           scale = 10752.1432565101
  +         )
  +       )
  +     )
  + )
  Error in varComprob(Reaction ~ 1 + Days, groups = groups, data = sleepstudy,  : 
    could not find function "varComprob"
  Execution halted