Package check result: OK

Changes to worse in reverse depends:

Package: tram
Check: tests
New result: ERROR
    Running ‘Coxph-Ex.R’ [5s/5s]
    Comparing ‘Coxph-Ex.Rout’ to ‘Coxph-Ex.Rout.save’ ... OK
    Running ‘KaplanMeier-Ex.R’ [1s/1s]
    Running ‘PI_OVL-Ex.R’ [2s/2s]
    Running ‘Polr-Ex.R’ [6s/6s]
    Comparing ‘Polr-Ex.Rout’ to ‘Polr-Ex.Rout.save’ ... OK
    Running ‘Survreg-Ex.R’ [2s/2s]
    Comparing ‘Survreg-Ex.Rout’ to ‘Survreg-Ex.Rout.save’ ... OK
    Running ‘bugfixes.R’ [40s/40s]
    Running ‘intercepts-Ex.R’ [2s/2s]
    Comparing ‘intercepts-Ex.Rout’ to ‘intercepts-Ex.Rout.save’ ... OK
    Running ‘mmlt-Ex.R’ [72s/72s]
    Comparing ‘mmlt-Ex.Rout’ to ‘mmlt-Ex.Rout.save’ ...
  931c931
  < [1] "Mean relative difference: 3.3"
  ---
  > [1] TRUE
    Running ‘mmlt-interface.R’ [4s/4s]
    Running ‘mtram-Ex.R’ [5s/5s]
    Comparing ‘mtram-Ex.Rout’ to ‘mtram-Ex.Rout.save’ ...
  63,64d62
  < attr(,"class")
  < [1] "vcmat_us" "matrix"   "array"   
    Running ‘stram-Ex.R’ [15s/15s]
    Comparing ‘stram-Ex.Rout’ to ‘stram-Ex.Rout.save’ ... OK
  Running the tests in ‘tests/mmlt-interface.R’ failed.
  Complete output:
    > 
    > library("tram")
    Loading required package: mlt
    Loading required package: basefun
    Loading required package: variables
    Loading required package: mvtnorm
    > library("survival")
    > library("sandwich")
    > library("numDeriv")
    > library("mvtnorm")
    > 
    > ### check mmlt for wild model combinations and weights
    > 
    > set.seed(290875)
    > 
    > chk <- function(x, y, tol = 1e-2) stopifnot(all.equal(x, y, tol = tol))
    > 
    > N <- 100
    > J <- 3
    > 
    > ### weights
    > w <- as.double(sample(0:5, size = N, replace = TRUE))
    > ### X1, X2, X3 correlated
    > z <- matrix(rnorm(N * J), nrow = J)
    > L <- ltMatrices(runif(J * (J - 1) / 2))
    > y <- t(solve(standardize(L), z))
    > ### uncorrelated ordered
    > x <- sample(gl(5, N / 5, ordered = TRUE))
    > ### X3 right censored
    > e <- sample(c(TRUE, FALSE), size = N, replace = TRUE)
    > X3 <- exp(y[,3])
    > X3 <- Surv(X3, event = e)
    > d <- data.frame(y[,1:2], X3, x, e)
    > dw <- d[rep(1:nrow(d), w),,drop = FALSE]
    > idx <- sample(1:N, size = N / 2)
    > 
    > ### global options (speed-up estimation)
    > M <- 200
    > args <- list(type = "ghalton", M = M)
    > op <- mltoptim()	### no hessian
    > 
    > ### marginal models (use Lm to reduce number of parameters)
    > m1 <- Lm(X1 ~ 1, data = d)
    > m2 <- Lm(X2 ~ 1, data = d)
    > m3 <- Lm(X3 ~ 1, data = d)
    > m4 <- Polr(x ~ 1, data = d, method = "probit")
    > 
    > ### joint distribution of X1, X2 (continuous), X3 (continuous and
    > ### right-censored) and x (discrete)
    > m <- mmlt(m1, m2, m3, m4, data = d, args = args, optim = op)
    > l1 <- logLik(m)
    > ## check gradient
    > s1 <- -colSums(estfun(m))
    > s2 <- grad(function(parm) logLik(m, parm = parm), coef(m))
    > chk(s2, unname(s1))
    > L1 <- aperm(coef(m, type = "Lambda"), perm = c("X1", "X2", "X3", "x"))
    Error in aperm.ltMatrices(coef(m, type = "Lambda"), perm = c("X1", "X2",  : 
      Cannot permute objects of class ltMatrices, 
              consider calling as.chol() or as.invchol() first
    Calls: aperm -> aperm.ltMatrices
    Execution halted