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### Running command:
###
###   /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_1.1.7.tar.gz
###
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* using log directory ‘/Users/biocbuild/bbs-3.22-bioc/meat/smoppix.Rcheck’
* using R version 4.5.1 Patched (2025-09-10 r88807)
* using platform: x86_64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 14.2.0
* running under: macOS Monterey 12.7.6
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘smoppix/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘smoppix’ version ‘1.1.7’
* 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 14.0.0 (clang-1400.0.29.202)’
* used SDK: ‘MacOSX11.3.1.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 ... NOTE
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
plotTopResults     10.664  6.061   8.961
estPis             11.467  3.411   4.980
writeToXlsx         9.253  5.203   8.029
fitLMMs             7.851  4.716   6.270
evalWeightFunction  5.753  5.460   6.408
plotWf              4.674  2.901   5.020
buildDataFrame      3.925  1.672   4.069
plotExplore         5.203  0.233   5.472
estGradients        3.357  1.429  11.870
* 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: 3 NOTEs
See
  ‘/Users/biocbuild/bbs-3.22-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/4.5-x86_64/Resources/library’
* installing *source* package ‘smoppix’ ...
** this is package ‘smoppix’ version ‘1.1.7’
** using staged installation
** libs
using C++ compiler: ‘Apple clang version 14.0.0 (clang-1400.0.29.202)’
using SDK: ‘MacOSX11.3.1.sdk’
clang++ -arch x86_64 -std=gnu++17 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG  -I'/Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library/Rcpp/include' -I/opt/R/x86_64/include    -fPIC  -falign-functions=64 -Wall -g -O2   -c RcppExports.cpp -o RcppExports.o
clang++ -arch x86_64 -std=gnu++17 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG  -I'/Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library/Rcpp/include' -I/opt/R/x86_64/include    -fPIC  -falign-functions=64 -Wall -g -O2   -c crossdistFast.cpp -o crossdistFast.o
clang++ -arch x86_64 -std=gnu++17 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG  -I'/Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library/Rcpp/include' -I/opt/R/x86_64/include    -fPIC  -falign-functions=64 -Wall -g -O2   -c findRanksDist.cpp -o findRanksDist.o
clang++ -arch x86_64 -std=gnu++17 -dynamiclib -Wl,-headerpad_max_install_names -undefined dynamic_lookup -L/Library/Frameworks/R.framework/Resources/lib -L/opt/R/x86_64/lib -o smoppix.so RcppExports.o crossdistFast.o findRanksDist.o -F/Library/Frameworks/R.framework/.. -framework R
installing to /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library/00LOCK-smoppix/00new/smoppix/libs
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
Loading required namespace: smoppix
*** 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 4.5.1 Patched (2025-09-10 r88807) -- "Great Square Root"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

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 3.1-4
Loading required package: spatstat.geom
spatstat.geom 3.6-0
spatstat.random 3.4-2
> 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/4.5-x86_64/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 182 ]

[ FAIL 0 | WARN 2 | SKIP 0 | PASS 182 ]
> 
> proc.time()
   user  system elapsed 
137.866  55.559 142.753