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### Running command:
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###   /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:smoppix.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings smoppix_1.4.0.tar.gz
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* using log directory ‘/Users/biocbuild/bbs-3.23-bioc/meat/smoppix.Rcheck’
* using R version 4.6.0 Patched (2026-04-24 r89963)
* using platform: aarch64-apple-darwin23
* R was compiled by
    Apple clang version 17.0.0 (clang-1700.3.19.1)
    GNU Fortran (GCC) 14.2.0
* running under: macOS Tahoe 26.3.1
* using session charset: UTF-8
* current time: 2026-05-07 02:40:25 UTC
* using option ‘--no-vignettes’
* checking for file ‘smoppix/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘smoppix’ version ‘1.4.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘smoppix’ can be installed ... OK
* used C++ compiler: ‘Apple clang version 17.0.0 (clang-1700.6.4.2)’
* used SDK: ‘MacOSX26.2.sdk’
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... NOTE
Unexported object imported by a ':::' call: ‘lmerTest:::as_lmerModLT’
  See the note in ?`:::` about the use of this operator.
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... NOTE
plotCells: no visible binding for global variable ‘gene’
plotCells: no visible binding for global variable ‘cell’
plotWf: no visible binding for global variable ‘minP’
plotWf: no visible binding for global variable ‘maxP’
plotWf: no visible binding for global variable ‘Weight’
Undefined global functions or variables:
  Weight cell gene maxP minP
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking compiled code ... INFO
Note: information on .o files is not available
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                user system elapsed
estPis         4.454  1.273   2.007
plotTopResults 3.408  2.117   3.509
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘testthat.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 2 NOTEs
See
  ‘/Users/biocbuild/bbs-3.23-bioc/meat/smoppix.Rcheck/00check.log’
for details.

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### Running command:
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###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL smoppix
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* installing to library ‘/Library/Frameworks/R.framework/Versions/4.6/Resources/library’
* installing *source* package ‘smoppix’ ...
** this is package ‘smoppix’ version ‘1.4.0’
** using staged installation
** libs
using C++ compiler: ‘Apple clang version 17.0.0 (clang-1700.6.4.2)’
using SDK: ‘MacOSX26.2.sdk’
clang++ -arch arm64 -std=gnu++20 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG  -I'/Library/Frameworks/R.framework/Versions/4.6/Resources/library/Rcpp/include' -I/opt/R/arm64/include    -fPIC  -falign-functions=64 -Wall -g -O2   -c RcppExports.cpp -o RcppExports.o
clang++ -arch arm64 -std=gnu++20 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG  -I'/Library/Frameworks/R.framework/Versions/4.6/Resources/library/Rcpp/include' -I/opt/R/arm64/include    -fPIC  -falign-functions=64 -Wall -g -O2   -c crossdistFast.cpp -o crossdistFast.o
clang++ -arch arm64 -std=gnu++20 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG  -I'/Library/Frameworks/R.framework/Versions/4.6/Resources/library/Rcpp/include' -I/opt/R/arm64/include    -fPIC  -falign-functions=64 -Wall -g -O2   -c findRanksDist.cpp -o findRanksDist.o
clang++ -arch arm64 -std=gnu++20 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG  -I'/Library/Frameworks/R.framework/Versions/4.6/Resources/library/Rcpp/include' -I/opt/R/arm64/include    -fPIC  -falign-functions=64 -Wall -g -O2   -c negative-hypergeometric-distribution.cpp -o negative-hypergeometric-distribution.o
clang++ -arch arm64 -std=gnu++20 -dynamiclib -Wl,-headerpad_max_install_names -undefined dynamic_lookup -L/Library/Frameworks/R.framework/Resources/lib -L/opt/R/arm64/lib -o smoppix.so RcppExports.o crossdistFast.o findRanksDist.o negative-hypergeometric-distribution.o -F/Library/Frameworks/R.framework/.. -framework R
installing to /Library/Frameworks/R.framework/Versions/4.6/Resources/library/00LOCK-smoppix/00new/smoppix/libs
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** checking absolute paths in shared objects and dynamic libraries
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (smoppix)

R version 4.6.0 Patched (2026-04-24 r89963) -- "Because it was There"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin23

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(testthat)
> library(smoppix)
> library(spatstat.random)
Loading required package: spatstat.data
Loading required package: spatstat.univar
spatstat.univar 3.1-7
Loading required package: spatstat.geom
spatstat.geom 3.7-3
spatstat.random 3.4-5
> library(BiocParallel)
> n <- 4000 # number of molecules
> ng <- 8 # number of genes
> nfov <- 3 # Number of fields of view
> conditions <- 3
> # sample xy-coordinates in [0, 1]
> x <- runif(n)
> y <- runif(n)
> # assign each molecule to some gene-cell pair
> gs <- paste0("gene", seq(ng))
> gene <- sample(gs, n, TRUE)
> fov <- as.character(sample(nfov, n, TRUE))
> condition <- as.character(sample(conditions, n, TRUE))
> f <- paste(fov, condition, sep = "_")
> age <- unsplit(lapply(split(integer(n), f = f), function(x) {rep(runif(1, 18, 98))}), f = f)
> # construct data.frame of molecule coordinates
> df <- data.frame(gene, x, y, fov, condition = condition, age = age)
> # A list of point patterns
> listPPP <- tapply(seq(nrow(df)), df$fov, function(i) {
+     ppp(x = df$x[i], y = df$y[i], marks = df[i, c("gene", "condition", "fov", "age"), drop = FALSE])
+ }, simplify = FALSE)
> # Regions of interest (roi): Diamond in the center plus four triangles
> w1 <- owin(poly = list(x = c(0, 0.5, 1, 0.5), y = c(0.5, 0, 0.5, 1)))
> w2 <- owin(poly = list(x = c(0, 0, 0.5), y = c(0.5, 0, 0)))
> w3 <- owin(poly = list(x = c(0, 0, 0.5), y = c(1, 0.5, 1)))
> w4 <- owin(poly = list(x = c(1, 1, 0.5), y = c(0.5, 1, 1)))
> w5 <- owin(poly = list(x = c(1, 1, 0.5), y = c(0, 0.5, 0)))
> wWrong <- owin(poly = list(x = c(0, 1, 1, 0), y = c(0.75, 0.5, 0.75, 1)))
> hypFrame <- buildHyperFrame(df, coordVars = c("x", "y"), imageVars = c(
+     "condition", "fov", "age"
+ ), imageIdentifier = c("fov", "condition"))
Found 9 unique images
> nDesignFactors <- length(unique(hypFrame$image))
> wList <- lapply(seq_len(nDesignFactors), function(x) {
+     Li <- list(w1 = w1, w2 = w2, w3 = w3, w4 = w4, w5 = w5)
+     names(Li) <- paste0(names(Li), "_", x)
+     Li
+ })
> wList2 <- lapply(seq_len(nDesignFactors), function(x) {
+     list(w1 = w1, w2 = w2, w3 = w3, w4 = w4, w5 = w5, wWrong = wWrong)
+ })
> unCells <- unlist(lapply(wList, names))
> cellTypesDf <- data.frame(cell = unCells, cellType = sample(paste0("CellType_", LETTERS[seq_len(5)]),
+     length(unCells),
+     replace = TRUE
+ ))
> names(wList) <- names(wList2) <- rownames(hypFrame)
> hypFrame2 <- addCell(hypFrame, wList, cellTypes = cellTypesDf, verbose = FALSE)
> # The nuclei
> n1 <- owin(poly = list(x = c(0.2, .4, 0.8, .4), y = c(.4, .2, .4, .8)))
> n2 <- owin(poly = list(x = c(0.1, 0.1, .4), y = c(.4, .1, .1)))
> n3 <- owin(poly = list(x = c(0.1, 0.1, .4), y = c(1, .75, 1)))
> n4 <- owin(poly = list(x = c(1, 1, .6), y = c(.7, .9, .9)))
> n5 <- owin(poly = list(x = c(.95, .95, .7), y = c(.1, .4, .1)))
> nLarge <- owin(poly = list(x = c(1.1, 1.1, .7), y = c(.1, .4, .1)))
> nList <- lapply(seq_len(nDesignFactors), function(x) {
+     Li <- list("w1" = n1, "w2" = n2, "w3" = n3, "w4" = n4, "w5" = n5)
+     names(Li) <- paste0(names(Li), "_", x)
+     Li
+ })
> nList2 <- lapply(seq_len(nDesignFactors), function(x) {
+     Li <- list("w1" = n1, "w2" = n2, "w3" = n3, "w4" = n4, "w5" = nLarge)
+     names(Li) <- paste0(names(Li), "_", x)
+     Li
+ })
> names(nList) <- names(nList2) <- rownames(hypFrame) # Matching names is necessary
> hypFrame2 <- addNuclei(hypFrame2, nList, verbose = FALSE)
> # Register the parallel backend
> nCores <- 2
> if(.Platform$OS.type == "unix"){
+   #On unix-based systems, use MulticoreParam
+   register(MulticoreParam(nCores))
+ } else {
+   #On windows, use makeCluster
+   library(doParallel)
+   Clus = makeCluster(nCores)
+   registerDoParallel(Clus)
+   register(DoparParam(), default = TRUE)
+ }
> #register(SerialParam()) # Switch on when mapping test coverage
> pis <- c("nn", "nnPair", "edge", "centroid", "nnCell", "nnPairCell")
> piEstsBG <- estPis(hypFrame2, pis = pis, null = "background", verbose = FALSE)
> piEstsCSR <- estPis(hypFrame2, pis = pis, null = "CSR", verbose = FALSE)
> piEstsBG2 <- estPis(hypFrame2[, c("ppp", "image", "tabObs")],
+     pis = "nn", null = "background",
+     verbose = FALSE
+ )
> # Add weight functions
> objBG <- addWeightFunction(piEstsBG, designVars = "condition")
> objCSR <- addWeightFunction(piEstsCSR, designVars = "condition")
> # Fit Yang models too
> data(Yang)
> hypYang <- buildHyperFrame(Yang[Yang$section %in% paste0("section", seq_len(3)),], 
+                            coordVars = c("x", "y"), imageVars = c("day", "root", "section"
+ )) #Subset for speed
Found 18 unique images
> yangPims <- estPis(hypYang, features = getFeatures(hypYang)[12:21], 
+                    pis = c("nn", "nnPair"), verbose = FALSE, nPointsAll = 2e3)
> yangPims <- addWeightFunction(yangPims, lowestLevelVar = "section")
> data(Eng)
> hypEng <- buildHyperFrame(Eng, coordVars = c("x", "y"), imageVars = c("fov", "experiment"))
Found 17 unique images
There were 13 warnings (use warnings() to see them)
> hypEng <- addCell(hypEng, EngRois, verbose = FALSE)
> test_check("smoppix")

rd10xV> dir <- system.file(
rd10xV+   file.path("extdata", "10xVisium"), 
rd10xV+   package = "SpatialExperiment")

rd10xV> sample_ids <- c("section1", "section2")

rd10xV> samples <- file.path(dir, sample_ids, "outs")

rd10xV> list.files(samples[1])
[1] "raw_feature_bc_matrix" "spatial"              

rd10xV> list.files(file.path(samples[1], "spatial"))
[1] "scalefactors_json.json"    "tissue_lowres_image.png"  
[3] "tissue_positions_list.csv"

rd10xV> file.path(samples[1], "raw_feature_bc_matrix")
[1] "/Library/Frameworks/R.framework/Versions/4.6/Resources/library/SpatialExperiment/extdata/10xVisium/section1/outs/raw_feature_bc_matrix"

rd10xV> (spe <- read10xVisium(samples, sample_ids, 
rd10xV+   type = "sparse", data = "raw", 
rd10xV+   images = "lowres", load = FALSE))
class: SpatialExperiment 
dim: 50 99 
metadata(0):
assays(1): counts
rownames(50): ENSMUSG00000051951 ENSMUSG00000089699 ...
  ENSMUSG00000005886 ENSMUSG00000101476
rowData names(1): symbol
colnames(99): AAACAACGAATAGTTC-1 AAACAAGTATCTCCCA-1 ...
  AAAGTCGACCCTCAGT-1 AAAGTGCCATCAATTA-1
colData names(4): in_tissue array_row array_col sample_id
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):
spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
imgData names(4): sample_id image_id data scaleFactor

rd10xV> # base directory 'outs/' from Space Ranger can also be omitted
rd10xV> samples2 <- file.path(dir, sample_ids)

rd10xV> (spe2 <- read10xVisium(samples2, sample_ids, 
rd10xV+   type = "sparse", data = "raw", 
rd10xV+   images = "lowres", load = FALSE))
class: SpatialExperiment 
dim: 50 99 
metadata(0):
assays(1): counts
rownames(50): ENSMUSG00000051951 ENSMUSG00000089699 ...
  ENSMUSG00000005886 ENSMUSG00000101476
rowData names(1): symbol
colnames(99): AAACAACGAATAGTTC-1 AAACAAGTATCTCCCA-1 ...
  AAAGTCGACCCTCAGT-1 AAAGTGCCATCAATTA-1
colData names(4): in_tissue array_row array_col sample_id
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):
spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
imgData names(4): sample_id image_id data scaleFactor

rd10xV> # tabulate number of spots mapped to tissue
rd10xV> cd <- colData(spe)

rd10xV> table(
rd10xV+   in_tissue = cd$in_tissue, 
rd10xV+   sample_id = cd$sample_id)
         sample_id
in_tissue section1 section2
    FALSE       28       27
    TRUE        22       22

rd10xV> # view available images
rd10xV> imgData(spe)
DataFrame with 2 rows and 4 columns
    sample_id    image_id   data scaleFactor
  <character> <character> <list>   <numeric>
1    section1      lowres   ####   0.0510334
2    section2      lowres   ####   0.0510334
[ FAIL 0 | WARN 2 | SKIP 0 | PASS 184 ]

[ FAIL 0 | WARN 2 | SKIP 0 | PASS 184 ]
> 
> proc.time()
   user  system elapsed 
 41.803  19.024  46.982