R Under development (unstable) (2024-08-16 r87026 ucrt) -- "Unsuffered Consequences"
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> 
> set.seed(290875)
> 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
> 
> ### get rid of the NAMESPACE
> attach(list2env(as.list(asNamespace("party"))))
The following objects are masked from package:party:

    cforest, cforest_classical, cforest_control, cforest_unbiased,
    conditionalTree, ctree, ctree_control, edge_simple,
    initVariableFrame, mob, mob_control, node_barplot, node_bivplot,
    node_boxplot, node_density, node_hist, node_inner,
    node_scatterplot, node_surv, node_terminal, nodes, party_intern,
    prettytree, proximity, ptrafo, response, reweight, sctest.mob,
    treeresponse, varimp, varimpAUC, where

> 
> ### 
> ###
> ###    Regression tests for test statistics
> ###    
> ###    functions defined in file `./src/TestStatistic.c'    
> 
> ### tests for function C_maxabsTeststatistic
> xf <- gl(3, 10)
> yf <- gl(3, 10)[sample(1:30)]
> x <- sapply(levels(xf), function(l) as.numeric(xf == l))
> colnames(x) <- NULL
> y <- sapply(levels(yf), function(l) as.numeric(yf == l))
> colnames(y) <- NULL
> weights <- sample(1:30)
> linstat <- LinearStatistic(x, y, weights) 
> expcov <- ExpectCovarLinearStatistic(x, y, weights)
> maxabs <- max(abs(linstat - expcov@expectation) / sqrt(diag(expcov@covariance)))
> stopifnot(isequal(maxabs, 
+     maxabsTestStatistic(linstat, expcov@expectation, expcov@covariance, 1e-10)))
> expcov@covariance[1,1] <- 1e-12
> stopifnot(isequal(maxabs,
+     maxabsTestStatistic(linstat, expcov@expectation, expcov@covariance, 1e-10)))
> 
> ### tests for function C_quadformTeststatistic
> ### -> see LinearStatistic-regtest.R
> 
> 
> proc.time()
   user  system elapsed 
   1.04    0.17    1.20