Running ‘testsAtoC.R’
  Running ‘testsD.R’ [30s/30s]
  Running ‘testsEtoF.R’ [27s/27s]
  Running ‘testsGtoJ.R’
  Running ‘testsK.R’
  Running ‘testsL.R’ [58s/59s]
  Running ‘testsM.R’
  Running ‘testsNtoO.R’
  Running ‘testsP1.R’
  Running ‘testsP2.R’ [16s/16s]
  Running ‘testsQ.R’
  Running ‘testsR1.R’
  Running ‘testsR2.R’ [23s/23s]
  Running ‘testsS.R’ [13s/13s]
  Running ‘testsT.R’
  Running ‘testsUtoZ.R’
Running the tests in ‘tests/testsD.R’ failed.
Complete output:
  > #'
  > #'   Header for all (concatenated) test files
  > #'
  > #'   Require spatstat.core
  > #'   Obtain environment variable controlling tests.
  > #'
  > #'   $Revision: 1.5 $ $Date: 2020/04/30 05:31:37 $
  > 
  > require(spatstat.core)
  Loading required package: spatstat.core
  Loading required package: spatstat.data
  Loading required package: spatstat.geom
  spatstat.geom 3.0-3
  Loading required package: spatstat.random
  spatstat.random 3.0-0
  Loading required package: nlme
  Loading required package: rpart
  spatstat.core 2.4-4
  > FULLTEST <- (nchar(Sys.getenv("SPATSTAT_TEST", unset="")) > 0)
  > ALWAYS   <- TRUE
  > cat(paste("--------- Executing",
  +           if(FULLTEST) "** ALL **" else "**RESTRICTED** subset of",
  +           "test code -----------\n"))
  --------- Executing **RESTRICTED** subset of test code -----------
  > #'
  > #'    tests/deltasuffstat.R
  > #' 
  > #'    Explicit tests of 'deltasuffstat'
  > #' 
  > #' $Revision: 1.4 $ $Date: 2021/01/22 08:08:48 $
  > 
  > if(!FULLTEST)
  +   spatstat.options(npixel=32, ndummy.min=16)
  > 
  > if(ALWAYS) {  # depends on C code
  + local({
  +   
  +   disagree <- function(x, y, tol=1e-7) { !is.null(x) && !is.null(y) && max(abs(x-y)) > tol }
  + 
  +   flydelta <- function(model, modelname="") {
  +     ## Check execution of different algorithms for 'deltasuffstat' 
  +     dSS <- deltasuffstat(model, sparseOK=TRUE)
  +     dBS <- deltasuffstat(model, sparseOK=TRUE,  use.special=FALSE, force=TRUE)
  +     dBF <- deltasuffstat(model, sparseOK=FALSE, use.special=FALSE, force=TRUE)
  +     ## Compare results
  +     if(disagree(dBS, dSS))
  +       stop(paste(modelname, "model: Brute force algorithm disagrees with special algorithm"))
  +     if(disagree(dBF, dBS))
  +       stop(paste(modelname, "model: Sparse and full versions of brute force algorithm disagree"))
  +     return(invisible(NULL))
  +   }
  + 
  +   modelS <- ppm(cells ~ x, Strauss(0.13), nd=10)
  +   flydelta(modelS, "Strauss")
  + 
  +   antsub <- ants[c(FALSE,TRUE,FALSE)]
  +   rmat <- matrix(c(130, 90, 90, 60), 2, 2)
  +   
  +   modelM <- ppm(antsub ~ 1, MultiStrauss(rmat), nd=16)
  +   flydelta(modelM, "MultiStrauss")
  +                 
  +   modelA <- ppm(antsub ~ 1, HierStrauss(rmat, archy=c(2,1)), nd=16)
  +   flydelta(modelA, "HierStrauss")
  + })
  + 
  + }
  > 
  > reset.spatstat.options()
  > #'
  > #'  tests/density.R
  > #'
  > #'  Test behaviour of density() methods,
  > #'                    relrisk(), Smooth()
  > #'                    and inhomogeneous summary functions
  > #'                    and idw, adaptive.density, intensity
  > #'
  > #'  $Revision: 1.61 $  $Date: 2022/04/17 00:54:34 $
  > #'
  > 
  > if(!FULLTEST)
  +   spatstat.options(npixel=32, ndummy.min=16)
  > 
  > local({
  + 
  +   # test all cases of density.ppp and densityfun.ppp
  +   
  +   tryit <- function(..., do.fun=TRUE, badones=FALSE) {
  +     Z <- density(cells, ..., at="pixels")
  +     Z <- density(cells, ..., at="points")
  +     if(do.fun) {
  +       f <- densityfun(cells, ...)
  +       U <- f(0.1, 0.3)
  +       if(badones) {
  +         U2 <- f(1.1, 0.3)
  +         U3 <- f(1.1, 0.3, drop=FALSE)
  +       }
  +     }
  +     return(invisible(NULL))
  +   }
  + 
  +   if(ALWAYS) {
  +     tryit(0.05)
  +     tryit(0.05, diggle=TRUE)
  +     tryit(0.05, se=TRUE)
  +     tryit(0.05, weights=expression(x))
  +     tryit(0.07, kernel="epa")
  +     tryit(sigma=Inf)
  +     tryit(0.05, badones=TRUE)
  +   }
  +   if(FULLTEST) {
  +     tryit(0.07, kernel="quartic")
  +     tryit(0.07, kernel="disc")
  +     tryit(0.07, kernel="epa", weights=expression(x))
  +     tryit(sigma=Inf, weights=expression(x))
  +   }
  +   
  +   V <- diag(c(0.05^2, 0.07^2))
  + 
  +   if(ALWAYS) {
  +     tryit(varcov=V)
  +   }
  +   if(FULLTEST) {
  +     tryit(varcov=V, diggle=TRUE)
  +     tryit(varcov=V, weights=expression(x))
  +     tryit(varcov=V, weights=expression(x), diggle=TRUE)
  +     Z <- distmap(runifpoint(5, Window(cells)))
  +     tryit(0.05, weights=Z)
  +     tryit(0.05, weights=Z, diggle=TRUE)
  +   }
  + 
  +   trymost <- function(...) tryit(..., do.fun=FALSE) 
  +   wdf <- data.frame(a=1:42,b=42:1)
  +   if(ALWAYS) {
  +     trymost(0.05, weights=wdf)
  +     trymost(sigma=Inf, weights=wdf)
  +   }
  +   if(FULLTEST) {
  +     trymost(0.05, weights=wdf, diggle=TRUE)
  +     trymost(varcov=V, weights=wdf)
  +     trymost(varcov=V, weights=expression(cbind(x,y)))
  +   }
  + 
  +   ## check conservation of mass
  +   checkconserve <- function(X, xname, sigma, toler=0.01) {
  +     veritas <- npoints(X)
  +     vino <- integral(density(X, sigma, diggle=TRUE))
  +     relerr <- abs(vino - veritas)/veritas
  +     if(relerr > toler)
  +       stop(paste("density.ppp(diggle=TRUE) fails to conserve mass:",
  +                  vino, "!=", veritas,
  +                  "for", sQuote(xname)),
  +            call.=FALSE)
  +     return(relerr)
  +   }
  +   if(FULLTEST) {
  +     checkconserve(cells, "cells", 0.15)
  +   }
  +   if(ALWAYS) {
  +     checkconserve(split(chorley)[["lung"]], "lung", 2)
  +   }
  +   
  +   ## run C algorithm 'denspt'
  +   opa <- spatstat.options(densityC=TRUE, densityTransform=FALSE)
  +   if(ALWAYS) {
  +     tryit(varcov=V)
  +   }
  +   if(FULLTEST) {
  +     tryit(varcov=V, weights=expression(x))
  +     trymost(varcov=V, weights=wdf)
  +   }
  +   spatstat.options(opa)
  + 
  +   crossit <- function(..., sigma=NULL) {
  +     U <- runifpoint(20, Window(cells))
  +     a <- densitycrossEngine(cells, U, ..., sigma=sigma)
  +     a <- densitycrossEngine(cells, U, ..., sigma=sigma, diggle=TRUE)
  +     invisible(NULL)
  +   }
  +   if(ALWAYS) {
  +     crossit(varcov=V, weights=cells$x)
  +     crossit(sigma=Inf)
  +   }
  +   if(FULLTEST) {
  +     crossit(varcov=V, weights=wdf)
  +     crossit(sigma=0.1, weights=wdf)
  +     crossit(sigma=0.1, kernel="epa", weights=wdf)
  +   }
  +   
  +   ## apply different discretisation rules
  +   if(ALWAYS) {
  +     Z <- density(cells, 0.05, fractional=TRUE)
  +   }
  +   if(FULLTEST) {
  +     Z <- density(cells, 0.05, preserve=TRUE)
  +     Z <- density(cells, 0.05, fractional=TRUE, preserve=TRUE)
  +   }
  +         
  +   ## compare results with different algorithms
  +   crosscheque <- function(expr) {
  +     e <- as.expression(substitute(expr))
  +     ename <- sQuote(deparse(substitute(expr)))
  +     ## interpreted R
  +     opa <- spatstat.options(densityC=FALSE, densityTransform=FALSE)
  +     val.interpreted <- eval(e)
  +     ## established C algorithm 'denspt'
  +     spatstat.options(densityC=TRUE, densityTransform=FALSE)
  +     val.C <- eval(e)
  +     ## new C algorithm 'Gdenspt' using transformed coordinates
  +     spatstat.options(densityC=TRUE, densityTransform=TRUE)
  +     val.Transform <- eval(e)
  +     spatstat.options(opa)
  +     if(max(abs(val.interpreted - val.C)) > 0.001)
  +       stop(paste("Numerical discrepancy between R and C algorithms in",
  +                  ename))
  +     if(max(abs(val.C - val.Transform)) > 0.001)
  +       stop(paste("Numerical discrepancy between C algorithms",
  +                  "using transformed and untransformed coordinates in",
  +                  ename))
  +     invisible(NULL)
  +   }
  + 
  +   ## execute & compare results of density(at="points") with different algorithms
  +   wdfr <- cbind(1:npoints(redwood), 2)
  +   if(ALWAYS) {
  +     crosscheque(density(redwood, at="points", sigma=0.13, edge=FALSE))
  +     crosscheque(density(redwood, at="points", sigma=0.13, edge=FALSE,
  +                         weights=wdfr[,1]))
  +     crosscheque(density(redwood, at="points", sigma=0.13, edge=FALSE,
  +                         weights=wdfr))
  +   }
  + 
  +   ## correctness of non-Gaussian kernel calculation
  +   leavein <- function(ker, maxd=0.025) {
  +     ZI <- density(redwood, 0.12, kernel=ker, edge=FALSE,
  +                   dimyx=256)[redwood]
  +     ZP <- density(redwood, 0.12, kernel=ker, edge=FALSE,
  +                   at="points", leaveoneout=FALSE)
  +     discrep <- max(abs(ZP - ZI))/npoints(redwood)
  +     if(discrep > maxd) 
  +       stop(paste("Discrepancy",
  +                  signif(discrep, 3),
  +                  "in calculation for", ker, "kernel"))
  +     return(invisible(NULL))
  +   }
  +   if(ALWAYS) {
  +     leavein("epanechnikov", 0.015)
  +   }
  +   if(FULLTEST) {
  +     leavein("quartic",      0.010)
  +     leavein("disc",         0.100)
  +   }
  + 
  +   ## bandwidth selection code blocks
  +   sigvec <- 0.01 * 2:15
  +   sigran <- range(sigvec)
  +   if(ALWAYS) {
  +     bw.ppl(redwood, sigma=sigvec)
  +     bw.CvL(redwood, sigma=sigvec)
  +   }
  +   if(FULLTEST) {
  +     bw.ppl(redwood, srange=sigran, ns=5)
  +     bw.CvL(redwood, srange=sigran, ns=5)
  +   }
  +   ## adaptive bandwidth
  +   if(ALWAYS) {
  +     a <- bw.abram(redwood)
  +   }
  +   if(FULLTEST) {
  +     a <- bw.abram(redwood, pilot=density(redwood, 0.2))
  +     a <- bw.abram(redwood, smoother="densityVoronoi", at="pixels")
  +   }
  +   
  +   ## Kinhom
  +   if(ALWAYS) {
  +     lam <- density(redwood)
  +     K <- Kinhom(redwood, lam)
  +   
  +     lamX <- density(redwood, at="points")
  +     KX <- Kinhom(redwood, lamX)
  +   }
  + 
  +   ## test all code cases of new 'relrisk.ppp' algorithm
  +   pants <- function(..., X=ants, sigma=100, se=TRUE) {
  +     a <- relrisk(X, sigma=sigma, se=se, ...)
  +     return(TRUE)
  +   }
  +   if(ALWAYS) {
  +     pants()
  +     pants(diggle=TRUE)
  +     pants(edge=FALSE)
  +     pants(at="points")
  +     pants(casecontrol=FALSE)
  +     pants(relative=TRUE)
  +     pants(sigma=Inf)
  +     pants(sigma=NULL, varcov=diag(c(100,100)^2))
  +   }
  +   if(FULLTEST) {
  +     pants(diggle=TRUE, at="points")
  +     pants(edge=FALSE, at="points")
  +     pants(casecontrol=FALSE, relative=TRUE)
  +     pants(casecontrol=FALSE,at="points")
  +     pants(relative=TRUE,at="points")
  +     pants(casecontrol=FALSE, relative=TRUE,at="points")
  +     pants(relative=TRUE, control="Cataglyphis", case="Messor")
  +     pants(relative=TRUE, control="Cataglyphis", case="Messor", at="points")
  +     pants(casecontrol=FALSE, case="Messor", se=FALSE)
  +     pants(case=2, at="pixels", relative=TRUE)
  +     pants(case=2, at="points", relative=TRUE)
  +     pants(case=2, at="pixels", relative=FALSE)
  +     pants(case=2, at="points", relative=FALSE)
  +   }
  +   if(ALWAYS) {
  +     ## underflow example from stackoverflow!
  +     funky <- scanpp("funky.tab", owin(c(4, 38), c(0.3, 17)))
  +     P <- relrisk(funky, 0.5)
  +     R <- relrisk(funky, 0.5, relative=TRUE)
  +   }
  +   ## more than 2 types
  +   if(ALWAYS) {
  +     pants(X=sporophores)
  +     pants(X=sporophores, sigma=20, at="points")
  +     bw.relrisk(sporophores, method="leastsquares")
  +   }
  +   if(FULLTEST) {
  +     pants(X=sporophores, sigma=20, relative=TRUE, at="points")
  +     pants(X=sporophores, sigma=20, at="pixels", se=FALSE)
  +     pants(X=sporophores, sigma=20, relative=TRUE, at="pixels", se=FALSE)
  +     bw.relrisk(sporophores, method="weightedleastsquares")
  +   }
  +   
  +   ## likewise 'relrisk.ppm'
  +   fit <- ppm(ants ~ x)
  +   rants <- function(..., model=fit) {
  +     a <- relrisk(model, sigma=100, se=TRUE, ...)
  +     return(TRUE)
  +   }
  +   if(ALWAYS) {
  +     rants()
  +     rants(diggle=TRUE)
  +     rants(edge=FALSE)
  +     rants(at="points")
  +     rants(casecontrol=FALSE)
  +     rants(relative=TRUE)
  +   }
  +   if(FULLTEST) {
  +     rants(diggle=TRUE, at="points")
  +     rants(edge=FALSE, at="points")
  +     rants(casecontrol=FALSE, relative=TRUE)
  +     rants(casecontrol=FALSE,at="points")
  +     rants(relative=TRUE,at="points")
  +     rants(casecontrol=FALSE, relative=TRUE,at="points")
  +     rants(relative=TRUE, control="Cataglyphis", case="Messor")
  +     rants(relative=TRUE, control="Cataglyphis", case="Messor", at="points")
  +   }
  +   ## more than 2 types
  +   fut <- ppm(sporophores ~ x)
  +   if(ALWAYS) {
  +     rants(model=fut)
  +   }
  +   if(FULLTEST) {
  +     rants(model=fut, at="points")
  +     rants(model=fut, relative=TRUE, at="points")
  +   }
  +   
  +   ## execute Smooth.ppp and Smoothfun.ppp in all cases
  +   stroke <- function(..., Y = longleaf, FUN=TRUE) {
  +     Z <- Smooth(Y, ..., at="pixels")
  +     Z <- Smooth(Y, ..., at="points", leaveoneout=TRUE)
  +     Z <- Smooth(Y, ..., at="points", leaveoneout=FALSE)
  +     if(FUN) {
  +       f <- Smoothfun(Y, ...)
  +       f(120, 80)
  +       f(Y[1:2])
  +       f(Y[FALSE])
  +       U <- as.im(f)
  +     }
  +     return(invisible(NULL))
  +   }
  +   if(ALWAYS) {
  +     stroke()
  +     stroke(5, diggle=TRUE)
  +     stroke(5, geometric=TRUE)
  +     stroke(1e-6) # generates warning about small bandwidth
  +     stroke(5, weights=expression(x))
  +     stroke(5, kernel="epa")
  +     stroke(sigma=Inf)
  +   }
  +   if(FULLTEST) {
  +     Z <- as.im(function(x,y){abs(x)+1}, Window(longleaf))
  +     stroke(5, weights=Z)
  +     stroke(5, weights=runif(npoints(longleaf)))
  +     stroke(varcov=diag(c(25, 36)))
  +     stroke(varcov=diag(c(25, 36)), weights=runif(npoints(longleaf)))
  +     stroke(5, Y=longleaf %mark% 1)
  +     stroke(5, Y=cut(longleaf,breaks=3))
  +     stroke(5, weights=Z, geometric=TRUE)
  +     g <- function(x,y) { dnorm(x, sd=10) * dnorm(y, sd=10) }
  +     stroke(kernel=g, cutoff=30, FUN=FALSE)
  +     stroke(kernel=g, cutoff=30, scalekernel=TRUE, sigma=1, FUN=FALSE)
  +   }
  +   
  +   markmean(longleaf, 9)
  +   
  +   strike <- function(..., Y=finpines) {
  +     Z <- Smooth(Y, ..., at="pixels")
  +     Z <- Smooth(Y, ..., at="points", leaveoneout=TRUE)
  +     Z <- Smooth(Y, ..., at="points", leaveoneout=FALSE)
  +     f <- Smoothfun(Y, ...)
  +     f(4, 1)
  +     f(Y[1:2])
  +     f(Y[FALSE])
  +     U <- as.im(f)
  +     return(invisible(NULL))
  +   }
  +   if(ALWAYS) {
  +     strike()
  +     strike(sigma=1.5, kernel="epa")
  +     strike(varcov=diag(c(1.2, 2.1)))
  +     strike(sigma=1e-6)
  +     strike(sigma=Inf)
  +   }
  +   if(FULLTEST) {
  +     strike(sigma=1e-6, kernel="epa")
  +     strike(1.5, weights=runif(npoints(finpines)))
  +     strike(1.5, weights=expression(y))
  +     strike(1.5, geometric=TRUE)
  +     strike(1.5, Y=finpines[FALSE])
  +     flatfin <- finpines %mark% data.frame(a=rep(1, npoints(finpines)), b=2)
  +     strike(1.5, Y=flatfin)
  +     strike(1.5, Y=flatfin, geometric=TRUE)
  +   }
  +   opx <- spatstat.options(densityTransform=FALSE)
  +   if(ALWAYS) {
  +     stroke(5, Y=longleaf[order(longleaf$x)], sorted=TRUE)
  +   }
  +   if(FULLTEST) {
  +     strike(1.5, Y=finpines[order(finpines$x)], sorted=TRUE)
  +   }
  +   spatstat.options(opx)
  + 
  +   ## detect special cases
  +   if(ALWAYS) {
  +     Smooth(longleaf[FALSE])
  +     Smooth(longleaf, minnndist(longleaf))
  +     Xconst <- cells %mark% 1
  +     Smooth(Xconst, 0.1)
  +     Smooth(Xconst, 0.1, at="points")
  +     Smooth(cells %mark% runif(42), sigma=Inf)
  +     Smooth(cells %mark% runif(42), sigma=Inf, at="points")
  +     Smooth(cells %mark% runif(42), sigma=Inf, at="points", leaveoneout=FALSE)
  +     Smooth(cut(longleaf, breaks=4))
  +   }
  +   
  +   ## code not otherwise reached
  +   if(ALWAYS) {
  +     smoothpointsEngine(cells, values=rep(1, npoints(cells)), sigma=0.2)
  +   }
  +   if(FULLTEST) {
  +     smoothpointsEngine(cells, values=runif(npoints(cells)), sigma=Inf)
  +     smoothpointsEngine(cells, values=runif(npoints(cells)), sigma=1e-16)
  +   }
  +   
  +   ## validity of Smooth.ppp(at='points')
  +   Y <- longleaf %mark% runif(npoints(longleaf), min=41, max=43)
  +   Z <- Smooth(Y, 5, at="points", leaveoneout=TRUE)
  +   rZ <- range(Z)
  +   if(rZ[1] < 40 || rZ[2] > 44)
  +     stop("Implausible results from Smooth.ppp(at=points, leaveoneout=TRUE)")
  + 
  +   Z <- Smooth(Y, 5, at="points", leaveoneout=FALSE)
  +   rZ <- range(Z)
  +   if(rZ[1] < 40 || rZ[2] > 44)
  +     stop("Implausible results from Smooth.ppp(at=points, leaveoneout=FALSE)")
  + 
  +   ## compare Smooth.ppp results with different algorithms
  +   if(ALWAYS) {
  +     crosscheque(Smooth(longleaf, at="points", sigma=6))
  +     wt <- runif(npoints(longleaf))
  +     crosscheque(Smooth(longleaf, at="points", sigma=6, weights=wt))
  +   }
  +   if(FULLTEST) {
  +     vc <- diag(c(25,36))
  +     crosscheque(Smooth(longleaf, at="points", varcov=vc))
  +     crosscheque(Smooth(longleaf, at="points", varcov=vc, weights=wt))
  +   }
  +   ## drop-dimension coding errors
  +   if(FULLTEST) {
  +     X <- longleaf
  +     marks(X) <- cbind(marks(X), 1)
  +     Z <- Smooth(X, 5)
  + 
  +     ZZ <- bw.smoothppp(finpines, hmin=0.01, hmax=0.012, nh=2) # reshaping problem
  +   }
  + 
  +   ## geometric-mean smoothing
  +   if(ALWAYS) {
  +     U <- Smooth(longleaf, 5, geometric=TRUE)
  +   }
  +   if(FULLTEST) {
  +     UU <- Smooth(X, 5, geometric=TRUE)
  +     V <- Smooth(longleaf, 5, geometric=TRUE, at="points")
  +     VV <- Smooth(X, 5, geometric=TRUE, at="points")
  +   }
  + })
  There were 17 warnings (use warnings() to see them)
  > 
  > reset.spatstat.options()
  > 
  > local({
  +   if(ALWAYS) {
  +     #' Kmeasure, second.moment.engine
  +     #' Expansion of window
  +     Zno  <- Kmeasure(redwood, sigma=0.2, expand=FALSE)
  +     Zyes <- Kmeasure(redwood, sigma=0.2, expand=TRUE)
  +     #' All code blocks
  +     sigmadouble <- rep(0.1, 2)
  +     diagmat <- diag(sigmadouble^2)
  +     generalmat <- matrix(c(1, 0.5, 0.5, 1)/100, 2, 2)
  +     Z <- Kmeasure(redwood, sigma=sigmadouble)
  +     Z <- Kmeasure(redwood, varcov=diagmat)
  +     Z <- Kmeasure(redwood, varcov=generalmat)
  +     A <- second.moment.calc(redwood, 0.1, what="all", debug=TRUE)
  +     B <- second.moment.calc(redwood, varcov=diagmat,    what="all")
  +     B <- second.moment.calc(redwood, varcov=diagmat,    what="all")
  +     D <- second.moment.calc(redwood, varcov=generalmat, what="all")
  +     PR <- pixellate(redwood)
  +     DRno  <- second.moment.calc(PR, 0.2, debug=TRUE, expand=FALSE,
  +                                 npts=npoints(redwood), obswin=Window(redwood))
  +     DRyes <- second.moment.calc(PR, 0.2, debug=TRUE, expand=TRUE,
  +                                 npts=npoints(redwood), obswin=Window(redwood))
  +     DR2 <- second.moment.calc(solist(PR, PR), 0.2, debug=TRUE, expand=TRUE,
  +                               npts=npoints(redwood), obswin=Window(redwood))
  +     Gmat <- generalmat * 100
  +     isoGauss <- function(x,y) {dnorm(x) * dnorm(y)}
  +     ee <- evaluate2Dkernel(isoGauss, runif(10), runif(10),
  +                            varcov=Gmat, scalekernel=TRUE)
  +     isoGaussIm <- as.im(isoGauss, square(c(-3,3)))
  +     gg <- evaluate2Dkernel(isoGaussIm, runif(10), runif(10),
  +                            varcov=Gmat, scalekernel=TRUE)
  +     ## experimental code
  +     op <- spatstat.options(developer=TRUE)
  +     DR <- density(redwood, 0.1)
  +     spatstat.options(op)
  +   }
  + })
  smooth: maximum imaginary part= 1.27e-18
  smooth: mass error= 0
  2nd moment measure: maximum imaginary part= 4.1e-17
  2nd moment measure: mass error= 0
  smooth: maximum imaginary part= 8.4e-19
  smooth: mass error= 0
  2nd moment measure: maximum imaginary part= 3.08e-17
  2nd moment measure: mass error= 0
  smooth: maximum imaginary part= 2.52e-18
  smooth: mass error= -3.55e-14
  2nd moment measure: maximum imaginary part= 1.33e-16
  2nd moment measure: mass error= -2.73e-12
  smooth component 1 : maximum imaginary part= 2.52e-18
  smooth component 1 : mass error= -3.55e-14
  smooth component 2 : maximum imaginary part= 2.52e-18
  smooth component 2 : mass error= -3.55e-14
  2nd moment measure: maximum imaginary part= 1.33e-16
  2nd moment measure: mass error= -2.73e-12
  2nd moment measure: maximum imaginary part= 1.33e-16
  2nd moment measure: mass error= -2.73e-12
  > 
  > local({
  +   if(FULLTEST) {
  +     #' bandwidth selection
  +     op <- spatstat.options(n.bandwidth=8)
  +     bw.diggle(cells) 
  +     bw.diggle(cells, method="interpreted") # undocumented test
  +     ##  bw.relrisk(urkiola, hmax=20) is tested in man/bw.relrisk.Rd
  +     bw.relrisk(urkiola, hmax=20, method="leastsquares")
  +     bw.relrisk(urkiola, hmax=20, method="weightedleastsquares")
  +     ZX <- density(swedishpines, at="points")
  +     bw.pcf(swedishpines, lambda=ZX)
  +     bw.pcf(swedishpines, lambda=ZX,
  +            bias.correct=FALSE, simple=FALSE, cv.method="leastSQ")
  +     spatstat.options(op)
  +   }
  + })
  > 
  > local({
  +   if(FULLTEST) {
  +     #' code in kernels.R
  +     kernames <- c("gaussian", "rectangular", "triangular",
  +                   "epanechnikov", "biweight", "cosine", "optcosine")
  +     X <- rnorm(20)
  +     U <- runif(20)
  +     for(ker in kernames) {
  +       dX <- dkernel(X, ker)
  +       fX <- pkernel(X, ker)
  +       qU <- qkernel(U, ker)
  +       m0 <- kernel.moment(0, 0, ker)
  +       m1 <- kernel.moment(1, 0, ker)
  +       m2 <- kernel.moment(2, 0, ker)
  +       m3 <- kernel.moment(3, 0, ker)
  +     }
  +   }
  + })
  > 
  > local({
  +   if(FULLTEST) {
  +     ## idw
  +     Z <- idw(longleaf, power=4)
  +     Z <- idw(longleaf, power=4, se=TRUE)
  +     ZX <- idw(longleaf, power=4, at="points")
  +     ZX <- idw(longleaf, power=4, at="points", se=TRUE)
  +   }
  +   if(ALWAYS) {
  +     ## former bug in densityVoronoi.ppp 
  +     X <- redwood[1:2]
  +     A <- densityVoronoi(X, f=0.51, counting=FALSE, fixed=FALSE, nrep=50, verbose=FALSE)
  +     ## dodgy code blocks in densityVoronoi.R
  +     A <- adaptive.density(nztrees, nrep=2, f=0.5, counting=TRUE)
  +     B <- adaptive.density(nztrees, nrep=2, f=0.5, counting=TRUE, fixed=TRUE)
  +     D <- adaptive.density(nztrees, nrep=2, f=0.5, counting=FALSE)
  +     E <- adaptive.density(nztrees, nrep=2, f=0.5, counting=FALSE, fixed=TRUE)
  +   }
  +   if(FULLTEST) {
  +     #' adaptive kernel estimation
  +     d10 <- nndist(nztrees, k=10)
  +     d10fun <- distfun(nztrees, k=10)
  +     d10im  <- as.im(d10fun)
  +     uN <- 2 * runif(npoints(nztrees))
  +     AA <- densityAdaptiveKernel(nztrees, bw=d10)
  +     BB <- densityAdaptiveKernel(nztrees, bw=d10, weights=uN)
  +     DD <- densityAdaptiveKernel(nztrees, bw=d10fun, weights=uN)
  +     EE <- densityAdaptiveKernel(nztrees, bw=d10im, weights=uN)
  +   }
  + })
  Computing 2 intensity estimates...1,  2.
  Done.
  Computing 2 intensity estimates...1,  2.
  Done.
  Computing 2 intensity estimates...1,  2.
  Done.
  Computing 2 intensity estimates...1,  2.
  Done.
  > 
  > local({
  +   if(ALWAYS) {
  +     ## unnormdensity
  +     x <- rnorm(20) 
  +     d0 <- unnormdensity(x, weights=rep(0, 20))
  +     dneg <- unnormdensity(x, weights=c(-runif(19), 0))
  +   }
  +   if(FULLTEST) {
  +     ## cases of 'intensity' etc
  +     a <- intensity(amacrine, weights=expression(x))
  +     SA <- split(amacrine)
  +     a <- intensity(SA, weights=expression(x))
  +     a <- intensity(SA, weights=amacrine$x)
  +     a <- intensity(ppm(amacrine ~ 1))
  + 
  +     ## check infrastructure for 'densityfun'
  +     f <- densityfun(cells, 0.05)
  +     Z <- as.im(f)
  +     Z <- as.im(f, W=square(0.5))
  +   }
  + })
  > 
  > reset.spatstat.options()
  > 
  > #'
  > #'      tests/diagnostique.R
  > #'
  > #'  Diagnostic tools such as diagnose.ppm, qqplot.ppm
  > #'
  > #'  $Revision: 1.6 $  $Date: 2020/04/28 12:58:26 $
  > #'
  > 
  > if(FULLTEST) {
  + local({
  +   fit <- ppm(cells ~ x)
  +   diagE <- diagnose.ppm(fit, type="eem")
  +   diagI <- diagnose.ppm(fit, type="inverse")
  +   diagP <- diagnose.ppm(fit, type="Pearson")
  +   plot(diagE, which="all")
  +   plot(diagI, which="smooth")
  +   plot(diagP, which="x")
  +   plot(diagP, which="marks", plot.neg="discrete")
  +   plot(diagP, which="marks", plot.neg="contour")
  +   plot(diagP, which="smooth", srange=c(-5,5))
  +   plot(diagP, which="smooth", plot.smooth="contour")
  +   plot(diagP, which="smooth", plot.smooth="image")
  + 
  +   fitS <- ppm(cells ~ x, Strauss(0.08))
  +   diagES <- diagnose.ppm(fitS, type="eem", clip=FALSE)
  +   diagIS <- diagnose.ppm(fitS, type="inverse", clip=FALSE)
  +   diagPS <- diagnose.ppm(fitS, type="Pearson", clip=FALSE)
  +   plot(diagES, which="marks", plot.neg="imagecontour")
  +   plot(diagPS, which="marks", plot.neg="discrete")
  +   plot(diagPS, which="marks", plot.neg="contour")
  +   plot(diagPS, which="smooth", plot.smooth="image")
  +   plot(diagPS, which="smooth", plot.smooth="contour")
  +   plot(diagPS, which="smooth", plot.smooth="persp")
  +   
  +   #' infinite reach, not border-corrected
  +   fut <- ppm(cells ~ x, Softcore(0.5), correction="isotropic")
  +   diagnose.ppm(fut)
  + 
  +   #' 
  +   diagPX <- diagnose.ppm(fit, type="Pearson", cumulative=FALSE)
  +   plot(diagPX, which="y")
  + 
  +   #' simulation based
  +   e <- envelope(cells, nsim=4, savepatterns=TRUE, savefuns=TRUE)
  +   Plist <- rpoispp(40, nsim=5)
  + 
  +   qf <- qqplot.ppm(fit, nsim=4, expr=e, plot.it=FALSE)
  +   print(qf)
  +   qp <- qqplot.ppm(fit, nsim=5, expr=Plist, fast=FALSE)
  +   print(qp)
  +   qp <- qqplot.ppm(fit, nsim=5, expr=expression(rpoispp(40)), plot.it=FALSE)
  +   print(qp)
  +   qg <- qqplot.ppm(fit, nsim=5, style="classical", plot.it=FALSE)
  +   print(qg)
  +   
  +   #' lurking.ppm
  +   #' covariate is numeric vector
  +   fitx <- ppm(cells ~ x)
  +   yvals <- coords(as.ppp(quad.ppm(fitx)))[,"y"]
  +   lurking(fitx, yvals)
  +   #' covariate is stored but is not used in model
  +   Z <- as.im(function(x,y){ x+y }, Window(cells))
  +   fitxx <- ppm(cells ~ x, data=solist(Zed=Z), allcovar=TRUE)
  +   lurking(fitxx, expression(Zed))
  +   #' envelope is a ppplist; length < nsim; glmdata=NULL
  +   fit <- ppm(cells ~ 1)
  +   stuff <- lurking(fit, expression(x), envelope=Plist, plot.sd=FALSE)
  +   #' plot.lurk
  +   plot(stuff, shade=NULL)
  + })
  + }
  > 
  > #'
  > #'  tests/discarea.R
  > #'
  > #'   $Revision: 1.3 $ $Date: 2020/04/28 12:58:26 $
  > #'
  > 
  > if(ALWAYS) {
  + local({
  +   u <- c(0.5,0.5)
  +   B <- owin(poly=list(x=c(0.3, 0.5, 0.7, 0.4), y=c(0.3, 0.3, 0.6, 0.8)))
  +   areaGain(u, cells, 0.1, exact=TRUE)
  +   areaGain(u, cells, 0.1, W=NULL)
  +   areaGain(u, cells, 0.1, W=B)
  + 
  +   X <- cells[square(0.4)]
  +   areaLoss(X, 0.1, exact=TRUE)  # -> areaLoss.diri
  +   areaLoss(X, 0.1, exact=FALSE) # -> areaLoss.grid
  +   areaLoss.poly(X, 0.1)
  + 
  +   areaLoss(X, 0.1, exact=FALSE, method="distmap")          # -> areaLoss.grid
  +   areaLoss(X, c(0.1, 0.15), exact=FALSE, method="distmap") # -> areaLoss.grid
  + })
  + }
              [,1]        [,2]
  [1,] 0.010449219 0.002197266
  [2,] 0.025009766 0.016718750
  [3,] 0.019658203 0.004248047
  [4,] 0.004970703 0.000000000
  [5,] 0.014111328 0.004951172
  [6,] 0.014394531 0.000156250
  [7,] 0.013779297 0.003691406
  > #'
  > #'   tests/disconnected.R
  > #'
  > #'   disconnected linear networks
  > #'
  > #'    $Revision: 1.4 $ $Date: 2020/04/28 12:58:26 $
  > 
  > 
  > #'
  > #'    tests/deepeepee.R
  > #'
  > #'    Tests for determinantal point process models
  > #' 
  > #'    $Revision: 1.9 $ $Date: 2022/04/24 09:14:46 $
  > 
  > local({
  +   if(ALWAYS) {
  +     #' simulate.dppm
  +     jpines <- residualspaper$Fig1
  +     fit <- dppm(jpines ~ 1, dppGauss)
  +     set.seed(10981)
  +     simulate(fit, W=square(5))
  +   }
  +   if(FULLTEST) {
  +     #' simulate.detpointprocfamily - code blocks
  +     model <- dppGauss(lambda=100, alpha=.05, d=2)
  +     simulate(model, seed=1999, correction="border")
  +     u <- is.stationary(model)
  +     #' other methods for dppm
  +     kay <- Kmodel(fit)
  +     gee <- pcfmodel(fit)
  +     lam <- intensity(fit)
  +     arr <- reach(fit)
  +     pah <- parameters(fit)
  +     #' a user bug report - matrix dimension error
  +     set.seed(256)
  +     dat <- simulate( dppGauss(lambda = 8.5, alpha = 0.1, d = 2), nsim = 1)
  +   }
  +   if(FULLTEST) {
  +     ## cover print.summary.dppm
  +     jpines <- japanesepines[c(TRUE,FALSE,FALSE,FALSE)]
  +     print(summary(dppm(jpines ~ 1, dppGauss)))
  +     print(summary(dppm(jpines ~ 1, dppGauss, method="c")))
  +     print(summary(dppm(jpines ~ 1, dppGauss, method="p")))
  +     print(summary(dppm(jpines ~ 1, dppGauss, method="a")))
  +   }
  +   #' dppeigen code blocks
  +   if(ALWAYS) {
  +     mod <- dppMatern(lambda=2, alpha=0.01, nu=1, d=2)
  +     uT <- dppeigen(mod, trunc=1.1,  Wscale=c(1,1), stationary=TRUE)
  +   }
  +   if(FULLTEST) {
  +     uF <- dppeigen(mod, trunc=1.1,  Wscale=c(1,1), stationary=FALSE)
  +     vT <- dppeigen(mod, trunc=0.98, Wscale=c(1,1), stationary=TRUE)
  +     vF <- dppeigen(mod, trunc=0.98, Wscale=c(1,1), stationary=FALSE)
  +   }
  + })
  Loading required namespace: spatstat.model
  Failed with error:  'there is no package called 'spatstat.model''
  The package 'spatstat.model' is required
  Error in info$selfstart(X) : attempt to apply non-function
  Calls: local ... eval -> kppm -> kppm.ppp -> kppmMinCon -> clusterfit
  Execution halted