Movatterモバイル変換

################################################################################################################################################################## Running command:######   /home/biocbuild/R/R/bin/R CMD check --install=check:atSNP.install-out.txt --library=/home/biocbuild/R/R/site-library --no-vignettes --timings atSNP_1.26.0.tar.gz###############################################################################################################################################################* using log directory ‘/home/biocbuild/bbs-3.22-bioc/meat/atSNP.Rcheck’* using R version 4.5.0 (2025-04-11)* using platform: aarch64-unknown-linux-gnu* R was compiled by    aarch64-unknown-linux-gnu-gcc (GCC) 14.2.0    GNU Fortran (GCC) 14.2.0* running under: openEuler 24.03 (LTS)* using session charset: UTF-8* using option ‘--no-vignettes’* checking for file ‘atSNP/DESCRIPTION’ ... OK* checking extension type ... Package* this is package ‘atSNP’ version ‘1.26.0’* 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 ‘atSNP’ can be installed ... OK* used C++ compiler: ‘aarch64-unknown-linux-gnu-g++ (GCC) 14.2.0’* 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 loading without being on the library search path ... OK* checking dependencies in R code ... NOTENamespaces in Imports field not imported from:  ‘graphics’ ‘testthat’  All declared Imports should be used.* checking S3 generic/method consistency ... OK* checking replacement functions ... OK* checking foreign function calls ... OK* checking R code for possible problems ... NOTEComputePValues: no visible binding for global variable ‘motif’ComputePValues: no visible binding for global variable ‘snpid’ComputePValues: no visible binding for global variable ‘snpbase’ComputePValues: no visible binding for global variable ‘pval_ref’ComputePValues: no visible binding for global variable ‘pval_snp’ComputePValues: no visible binding for global variable ‘pval_cond_ref’ComputePValues: no visible binding for global variable ‘pval_cond_snp’ComputePValues: no visible binding for global variable ‘pval_diff’ComputePValues: no visible binding for global variable ‘pval_rank’LoadSNPData: no visible global function definition for ‘is’LoadSNPData: no visible binding for global variable ‘IUPAC_CODE_MAP’MatchSubsequence: no visible binding for global variable ‘motif’MatchSubsequence: no visible binding for global variable ‘snpid’MatchSubsequence: no visible binding for global variable ‘snpbase’MatchSubsequence: no visible binding for global variable ‘len_seq’MatchSubsequence: no visible binding for global variable ‘ref_seq’MatchSubsequence : <anonymous>: no visible binding for global variable  ‘motif’checkMotifs: no visible global function definition for ‘is’checkSNPids: no visible global function definition for ‘is’dtMotifMatch: no visible binding for global variable ‘ref_seq’dtMotifMatch: no visible binding for global variable ‘len_seq’dtMotifMatch: no visible binding for global variable ‘snp_ref_start’dtMotifMatch: no visible binding for global variable ‘ref_start’dtMotifMatch: no visible binding for global variable ‘snp_start’dtMotifMatch: no visible binding for global variable ‘snp_ref_end’dtMotifMatch: no visible binding for global variable ‘ref_end’dtMotifMatch: no visible binding for global variable ‘snp_end’dtMotifMatch: no visible binding for global variable ‘snp_ref_length’dtMotifMatch: no visible binding for global variable  ‘ref_aug_match_seq_forward’dtMotifMatch: no visible binding for global variable  ‘ref_aug_match_seq_reverse’dtMotifMatch: no visible binding for global variable  ‘snp_aug_match_seq_forward’dtMotifMatch: no visible binding for global variable ‘snp_seq’dtMotifMatch: no visible binding for global variable  ‘snp_aug_match_seq_reverse’dtMotifMatch: no visible binding for global variable ‘ref_strand’dtMotifMatch: no visible binding for global variable ‘ref_location’dtMotifMatch: no visible binding for global variable ‘snp_strand’dtMotifMatch: no visible binding for global variable ‘snp_location’dtMotifMatch: no visible binding for global variable  ‘ref_extra_pwm_left’dtMotifMatch: no visible binding for global variable  ‘ref_extra_pwm_right’dtMotifMatch: no visible binding for global variable  ‘snp_extra_pwm_left’dtMotifMatch: no visible binding for global variable  ‘snp_extra_pwm_right’dtMotifMatch: no visible binding for global variable ‘snpid’match_subseq_par: no visible binding for global variable ‘snpid’match_subseq_par: no visible binding for global variable ‘motif’match_subseq_par: no visible binding for global variable ‘snpbase’match_subseq_par: no visible binding for global variable ‘ref_strand’match_subseq_par: no visible binding for global variable  ‘ref_match_seq’match_subseq_par: no visible binding for global variable ‘ref_seq’match_subseq_par: no visible binding for global variable ‘ref_start’match_subseq_par: no visible binding for global variable ‘ref_end’match_subseq_par: no visible binding for global variable ‘ref_seq_rev’match_subseq_par: no visible binding for global variable ‘len_seq’match_subseq_par: no visible binding for global variable ‘snp_strand’match_subseq_par: no visible binding for global variable  ‘snp_match_seq’match_subseq_par: no visible binding for global variable ‘snp_seq’match_subseq_par: no visible binding for global variable ‘snp_start’match_subseq_par: no visible binding for global variable ‘snp_end’match_subseq_par: no visible binding for global variable ‘snp_seq_rev’match_subseq_par: no visible binding for global variable  ‘snp_seq_ref_match’match_subseq_par: no visible binding for global variable  ‘ref_seq_snp_match’match_subseq_par: no visible binding for global variable ‘motif_len’match_subseq_par: no visible binding for global variable ‘log_lik_ref’match_subseq_par: no visible binding for global variable ‘log_lik_snp’match_subseq_par: no visible binding for global variable  ‘log_lik_ratio’match_subseq_par: no visible binding for global variable  ‘log_enhance_odds’match_subseq_par: no visible binding for global variable  ‘log_reduce_odds’match_subseq_par: no visible binding for global variable ‘IUPAC’motif_score_par: no visible binding for global variable ‘motif’motif_score_par: no visible binding for global variable ‘snpbase’plotMotifMatch: no visible global function definition for ‘is’results_motif_par: no visible binding for global variable ‘p.value’Undefined global functions or variables:  IUPAC IUPAC_CODE_MAP is len_seq log_enhance_odds log_lik_ratio  log_lik_ref log_lik_snp log_reduce_odds motif motif_len p.value  pval_cond_ref pval_cond_snp pval_diff pval_rank pval_ref pval_snp  ref_aug_match_seq_forward ref_aug_match_seq_reverse ref_end  ref_extra_pwm_left ref_extra_pwm_right ref_location ref_match_seq  ref_seq ref_seq_rev ref_seq_snp_match ref_start ref_strand  snp_aug_match_seq_forward snp_aug_match_seq_reverse snp_end  snp_extra_pwm_left snp_extra_pwm_right snp_location snp_match_seq  snp_ref_end snp_ref_length snp_ref_start snp_seq snp_seq_ref_match  snp_seq_rev snp_start snp_strand snpbase snpidConsider adding  importFrom("methods", "is")to your NAMESPACE file (and ensure that your DESCRIPTION Imports fieldcontains 'methods').* 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 ... NOTENote: information on .o files is not availableFile ‘/home/biocbuild/R/R-4.5.0/site-library/atSNP/libs/atSNP.so’:  Found ‘printf’, possibly from ‘printf’ (C)Compiled code should not call entry points which might terminate R norwrite to stdout/stderr instead of to the console, nor use Fortran I/Onor system RNGs nor [v]sprintf. The detected symbols are linked intothe code but might come from libraries and not actually be called.See ‘Writing portable packages’ in the ‘Writing R Extensions’ manual.* checking files in ‘vignettes’ ... OK* checking examples ... OKExamples with CPU (user + system) or elapsed time > 5s                 user system elapsedplotMotifMatch 11.092  1.359  15.886* checking for unstated dependencies in ‘tests’ ... OK* checking tests ...  Running ‘test.R’  Running ‘test_change.R’  Running ‘test_diff.R’  Running ‘test_is.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* DONEStatus: 3 NOTEsSee  ‘/home/biocbuild/bbs-3.22-bioc/meat/atSNP.Rcheck/00check.log’for details.

################################################################################################################################################################## Running command:######   /home/biocbuild/R/R/bin/R CMD INSTALL atSNP###############################################################################################################################################################* installing to library ‘/home/biocbuild/R/R-4.5.0/site-library’* installing *source* package ‘atSNP’ ...** this is package ‘atSNP’ version ‘1.26.0’** using staged installation** libsusing C++ compiler: ‘aarch64-unknown-linux-gnu-g++ (GCC) 14.2.0’/opt/ohpc/pub/compiler/gcc/14.2.0/bin/aarch64-unknown-linux-gnu-g++ -std=gnu++17 -I"/home/biocbuild/R/R-4.5.0/include" -DNDEBUG  -I'/home/biocbuild/R/R-4.5.0/site-library/Rcpp/include' -I/usr/local/include    -fPIC  -g -O2  -Wall -Werror=format-security  -c ImportanceSample.cpp -o ImportanceSample.o/opt/ohpc/pub/compiler/gcc/14.2.0/bin/aarch64-unknown-linux-gnu-g++ -std=gnu++17 -I"/home/biocbuild/R/R-4.5.0/include" -DNDEBUG  -I'/home/biocbuild/R/R-4.5.0/site-library/Rcpp/include' -I/usr/local/include    -fPIC  -g -O2  -Wall -Werror=format-security  -c ImportanceSampleChange.cpp -o ImportanceSampleChange.o/opt/ohpc/pub/compiler/gcc/14.2.0/bin/aarch64-unknown-linux-gnu-g++ -std=gnu++17 -I"/home/biocbuild/R/R-4.5.0/include" -DNDEBUG  -I'/home/biocbuild/R/R-4.5.0/site-library/Rcpp/include' -I/usr/local/include    -fPIC  -g -O2  -Wall -Werror=format-security  -c ImportanceSampleDiff.cpp -o ImportanceSampleDiff.o/opt/ohpc/pub/compiler/gcc/14.2.0/bin/aarch64-unknown-linux-gnu-g++ -std=gnu++17 -I"/home/biocbuild/R/R-4.5.0/include" -DNDEBUG  -I'/home/biocbuild/R/R-4.5.0/site-library/Rcpp/include' -I/usr/local/include    -fPIC  -g -O2  -Wall -Werror=format-security  -c MotifScore.cpp -o MotifScore.o/opt/ohpc/pub/compiler/gcc/14.2.0/bin/aarch64-unknown-linux-gnu-g++ -std=gnu++17 -shared -L/home/biocbuild/R/R-4.5.0/lib -L/usr/local/lib -o atSNP.so ImportanceSample.o ImportanceSampleChange.o ImportanceSampleDiff.o MotifScore.o -L/home/biocbuild/R/R-4.5.0/lib -lRinstalling to /home/biocbuild/R/R-4.5.0/site-library/00LOCK-atSNP/00new/atSNP/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 (atSNP)

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"Copyright (C) 2025 The R Foundation for Statistical ComputingPlatform: aarch64-unknown-linux-gnuR 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(atSNP)> library(BiocParallel)> library(testthat)> > ## process the data> data(example)> > motif_scores <- ComputeMotifScore(motif_library, snpInfo, ncores = 1)> > motif_scores <- MatchSubsequence(motif_scores$snp.tbl, motif_scores$motif.scores, ncores = 1, motif.lib = motif_library)> > motif_scores[which(motif_scores$snpid == "rs7412" & motif_scores$motif == "SIX5_disc1"), ]   snpid      motif4 rs7412 SIX5_disc1                                                        ref_seq4 CTCCTCCGCGATGCCGATGACCTGCAGAAGCGCCTGGCAGTGTACCAGGCCGGGGCCCGCG                                                        snp_seq motif_len4 CTCCTCCGCGATGCCGATGACCTGCAGAAGTGCCTGGCAGTGTACCAGGCCGGGGCCCGCG        10  ref_start ref_end ref_strand snp_start snp_end snp_strand log_lik_ref4        29      38          -        22      31          +   -42.60672  log_lik_snp log_lik_ratio log_enhance_odds log_reduce_odds      IUPAC4    -38.4083     -4.198418           23.013       -2.917768 GARWTGTAGT  ref_match_seq snp_match_seq ref_seq_snp_match snp_seq_ref_match snpbase4    GCCAGGCGCT    CTGCAGAAGT        CTGCAGAAGC        GCCAGGCACT       T> > len_seq <- sapply(motif_scores$ref_seq, nchar)> snp_pos <- as.integer(len_seq / 2) + 1> > i <- which(motif_scores$snpid == "rs7412" & motif_scores$motif == "SIX5_disc1")> > test_that("Error: reference bases are not the same as the sequence matrix.", {+   expect_equal(sum(snpInfo$sequence_matrix[31, ] != snpInfo$ref_base), 0)+   expect_equal(sum(snpInfo$sequence_matrix[31, ] == snpInfo$snp_base), 0)+ })Test passed 😀> > test_that("Error: log_lik_ratio is not correct.", {+   expect_equal(motif_scores$log_lik_ref - motif_scores$log_lik_snp, motif_scores$log_lik_ratio)+ })Test passed 🎉> > test_that("Error: log likelihoods are not correct.", {+ +   log_lik <- sapply(seq(nrow(motif_scores)),+                         function(i) {+                           motif_mat <- motif_library[[motif_scores$motif[i]]]+                           colind<-which(snpInfo$snpids==motif_scores$snpid[i]) +                           bases <- snpInfo$sequence_matrix[motif_scores$ref_start[i]:motif_scores$ref_end[i], colind]+                           if(motif_scores$ref_strand[i] == "-")+                             bases <- 5 - rev(bases)+                           log(prod(+                                    motif_mat[cbind(seq(nrow(motif_mat)),+                                                    bases)]))+                         })+ +   expect_equal(log_lik, motif_scores$log_lik_ref)+ +   snp_mat <- snpInfo$sequence_matrix+   snp_mat[cbind(snp_pos, seq(ncol(snp_mat)))] <- snpInfo$snp_base+   log_lik <- sapply(seq(nrow(motif_scores)),+                     function(i) {+                       motif_mat <- motif_library[[motif_scores$motif[i]]]+                       colind<-which(snpInfo$snpids==motif_scores$snpid[i])+                       bases <- snp_mat[motif_scores$snp_start[i]:motif_scores$snp_end[i], colind]+                       if(motif_scores$snp_strand[i] == "-")+                         bases <- 5 - rev(bases)+                       log(prod(+                                motif_mat[cbind(seq(nrow(motif_mat)),+                                                bases)]))+                     })+ +   expect_equal(log_lik, motif_scores$log_lik_snp)+ })Test passed 😸> > test_that("Error: log_enhance_odds not correct.", {+   +   len_seq <- sapply(motif_scores$ref_seq, nchar)+   snp_pos <- as.integer(len_seq / 2) + 1+ +   ## log odds for reduction in binding affinity+   +   pos_in_pwm <- snp_pos - motif_scores$ref_start + 1+   neg_ids <- which(motif_scores$ref_strand == "-")+   pos_in_pwm[neg_ids] <- motif_scores$ref_end[neg_ids]- snp_pos[neg_ids] + 1+   snp_base <- sapply(substr(motif_scores$snp_seq, snp_pos, snp_pos), function(x) which(c("A", "C", "G", "T") == x))+   ref_base <- sapply(substr(motif_scores$ref_seq, snp_pos, snp_pos), function(x) which(c("A", "C", "G", "T") == x))+   snp_base[neg_ids] <- 5 - snp_base[neg_ids]+   ref_base[neg_ids] <- 5 - ref_base[neg_ids]+   my_log_reduce_odds <- sapply(seq(nrow(motif_scores)),+                                function(i)+                                log(motif_library[[motif_scores$motif[i]]][pos_in_pwm[i], ref_base[i]]) -+                                log(motif_library[[motif_scores$motif[i]]][pos_in_pwm[i], snp_base[i]])+                                )+ +   expect_equal(my_log_reduce_odds, motif_scores$log_reduce_odds)+ +   ## log odds in enhancing binding affinity+   +   pos_in_pwm <- snp_pos - motif_scores$snp_start + 1+   neg_ids <- which(motif_scores$snp_strand == "-")+   pos_in_pwm[neg_ids] <- motif_scores$snp_end[neg_ids]- snp_pos[neg_ids] + 1+   snp_base <- sapply(substr(motif_scores$snp_seq, snp_pos, snp_pos), function(x) which(c("A", "C", "G", "T") == x))+   ref_base <- sapply(substr(motif_scores$ref_seq, snp_pos, snp_pos), function(x) which(c("A", "C", "G", "T") == x))+   snp_base[neg_ids] <- 5 - snp_base[neg_ids]+   ref_base[neg_ids] <- 5 - ref_base[neg_ids]+   my_log_enhance_odds <- sapply(seq(nrow(motif_scores)),+                                 function(i)+                                 log(motif_library[[motif_scores$motif[i]]][pos_in_pwm[i], snp_base[i]]) -+                                 log(motif_library[[motif_scores$motif[i]]][pos_in_pwm[i], ref_base[i]]) +                                )+ +   expect_equal(my_log_enhance_odds, motif_scores$log_enhance_odds)+   + + })Test passed 🥳> > test_that("Error: the maximum log likelihood computation is not correct.", {+ +   snp_mat <- snpInfo$sequence_matrix+   snp_mat[cbind(snp_pos, seq(ncol(snp_mat)))] <- snpInfo$snp_base+ +   .findMaxLog <- function(seq_vec, pwm) {+     snp_pos <- as.integer(length(seq_vec) / 2) + 1+     start_pos <- snp_pos - nrow(pwm) + 1+     end_pos <- snp_pos+     rev_seq <- 5 - rev(seq_vec)+     +     maxLogProb <- -Inf+     for(i in start_pos : end_pos) {+       LogProb <- log(prod(pwm[cbind(seq(nrow(pwm)),+                                     seq_vec[i - 1 + seq(nrow(pwm))])]))+       if(LogProb > maxLogProb)+         maxLogProb <- LogProb+     }+     for(i in start_pos : end_pos) {+       LogProb <- log(prod(pwm[cbind(seq(nrow(pwm)),+                                     rev_seq[i - 1 + seq(nrow(pwm))])]))+       if(LogProb > maxLogProb)+         maxLogProb <- LogProb+     }+     return(maxLogProb)+   }+   +   ## find the maximum log likelihood on the reference sequence+   my_log_lik_ref <- sapply(seq(nrow(motif_scores)),+                            function(x) {+                          colind<-which(snpInfo$snpids==motif_scores$snpid[x])                           +                              seq_vec<- snpInfo$sequence_matrix[, colind]+                              pwm <- motif_library[[motif_scores$motif[x]]]+                              return(.findMaxLog(seq_vec, pwm))+                            })+ +   ## find the maximum log likelihood on the SNP sequence+ +   my_log_lik_snp <- sapply(seq(nrow(motif_scores)),+                            function(x) {+                         colind<-which(snpInfo$snpids==motif_scores$snpid[x]) #ADDED+                             seq_vec<- snp_mat[, colind]+                             pwm <- motif_library[[motif_scores$motif[x]]]+                             return(.findMaxLog(seq_vec, pwm))+                            })+   +   expect_equal(my_log_lik_ref, motif_scores$log_lik_ref)+   expect_equal(my_log_lik_snp, motif_scores$log_lik_snp)+   + })Test passed 🥇> > proc.time()   user  system elapsed  17.811   0.933  18.781

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"Copyright (C) 2025 The R Foundation for Statistical ComputingPlatform: aarch64-unknown-linux-gnuR 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(atSNP)> library(BiocParallel)> library(testthat)> data(example)> > trans_mat <- matrix(rep(snpInfo$prior, each = 4), nrow = 4)> test_pwm <- motif_library$SIX5_disc1> scores <- as.matrix(motif_scores$motif.scores[3:4, 4:5])> score_diff <- abs(scores[,2]-scores[,1])> > pval_a <- .Call("test_p_value", test_pwm, snpInfo$prior, snpInfo$transition, scores, 0.15, 100)> pval_ratio <- abs(log(pval_a[seq(nrow(scores)),1]) - log(pval_a[seq(nrow(scores)) + nrow(scores), 1]))> > test_score <- test_pwm> for(i in seq(nrow(test_score))) {+   for(j in seq(ncol(test_score))) {+     test_score[i, j] <- exp(mean(log(test_pwm[i, j] / test_pwm[i, -j])))+   }+ }> > adj_mat <- test_pwm + 0.25> motif_len <- nrow(test_pwm)> > ## these are functions for this test only> drawonesample <- function(theta) {+     prob_start <- rev(rowSums(test_score ^ theta) / rowSums(adj_mat))+     id <- sample(seq(motif_len), 1, prob = prob_start)+     sample <- sample(1:4, 2 * motif_len - 1, replace = TRUE, prob = snpInfo$prior)+     delta <- adj_mat+     delta[motif_len - id + 1, ] <- test_score[motif_len - id + 1, ] ^ theta+     sample[id - 1 + seq(motif_len)] <- apply(delta, 1, function(x) sample(seq(4), 1, prob = x))+     ## compute weight+     sc <- 0+     for(s in seq(motif_len)) {+       delta <- adj_mat+       delta[motif_len + 1 - s, ] <- test_score[motif_len + 1 - s, ] ^ theta+       sc <- sc + prod(delta[cbind(seq(motif_len), sample[s - 1 + seq(motif_len)])]) /+         prod(snpInfo$prior[sample[s - 1 + seq(motif_len)]])+     }+     sample <- c(sample, id, sc)+     return(sample)+ }> jointprob <- function(x) prod(test_pwm[cbind(seq(motif_len), x)])> maxjointprob <- function(x) {+   maxp <- -Inf+   p <- -Inf+   for(i in 1:motif_len) {+     p <- jointprob(x[i:(i+motif_len - 1)])+     if(p > maxp)+       maxp <- p+   }+   for(i in 1:motif_len) {+     p <- jointprob(5 - x[(i+motif_len - 1):i])+     if(p > maxp)+       maxp <- p+   }+   return(maxp)+ }> get_freq <- function(sample) {+   emp_freq <- matrix(0, nrow = 2 * motif_len - 1, ncol = 4)+   for(i in seq(2 * motif_len - 1)) {+     for(j in seq(4)) {+       emp_freq[i, j] <- sum(sample[i, ] == j - 1)+     }+   }+   emp_freq <- emp_freq / rowSums(emp_freq)+   return(emp_freq)+ }> > test_that("Error: quantile function computing are not equivalent.", {+   for(p in c(0.01, 0.1, 0.5, 0.9, 0.99) ) {+     delta <- .Call("test_find_percentile_change", score_diff, p, package = "atSNP")+     delta.r <- as.double(sort(abs(scores[,2]-scores[,1]))[ceiling((1 - p) * (nrow(scores)))])+     expect_equal(delta, delta.r)+   }+ })Test passed 🥳> > test_that("Error: the scores for samples are not equivalent.", {+   p <- 0.1+   delta <- .Call("test_find_percentile_change", score_diff, p, package = "atSNP")+   theta <- .Call("test_find_theta_change", test_score, adj_mat, delta, package = "atSNP")+   ## Use R code to generate a random sample+   for(i in seq(10)) {+     sample <- drawonesample(theta)+     sample_score <- .Call("test_compute_sample_score_change", test_pwm, test_score, adj_mat, sample[seq(2 * motif_len - 1)] - 1, snpInfo$prior, trans_mat, sample[2 * motif_len] - 1, theta, package = "atSNP")+     expect_equal(sample[2 * motif_len + 1], sample_score[1])+     sample1 <- sample2 <- sample3 <- sample+     sample1[motif_len] <- seq(4)[-sample[motif_len]][1]+     sample2[motif_len] <- seq(4)[-sample[motif_len]][2]+     sample3[motif_len] <- seq(4)[-sample[motif_len]][3]+     sample_score_r <- log(maxjointprob(sample[seq(2 * motif_len - 1)])) -+       log(c(maxjointprob(sample1[seq(2 * motif_len - 1)]),+             maxjointprob(sample2[seq(2 * motif_len - 1)]),+             maxjointprob(sample3[seq(2 * motif_len - 1)])))+     expect_equal(sample_score_r, sample_score[2:4])+   }+   +   ## Use C code to generate a random sample+   for(i in seq(10)) {+     sample <- .Call("test_importance_sample_change", test_score, snpInfo$prior, trans_mat, test_pwm, theta, package = "atSNP")+     start_pos <- sample[2 * motif_len] + 1+     adj_score <- 0+     for(s in seq_len(motif_len)) {+       adj_s <- sum(log(adj_mat[cbind(seq(motif_len), sample[s - 1 + seq(motif_len)] + 1)]) -+                    log(snpInfo$prior[sample[s - 1 + seq(motif_len)] + 1]))+       adj_s <- adj_s + theta * log(test_score[motif_len + 1 - s, sample[motif_len] + 1]) -+           log(adj_mat[motif_len + 1 - s, sample[motif_len] + 1])+       adj_score <- adj_score + exp(adj_s)+     }+     sample_score <- .Call("test_compute_sample_score_change", test_pwm, test_score, adj_mat, sample[seq(2 * motif_len - 1)], snpInfo$prior, trans_mat, sample[2 * motif_len], theta, package = "atSNP")+     expect_equal(adj_score, sample_score[1])+   }+ })Test passed 😸> > test_that("Error: compute the normalizing constant.", {+   ## parameters+   for(p in seq(9) / 10) {+     delta <- .Call("test_find_percentile_change", score_diff, p, package = "atSNP")+     theta <- .Call("test_find_theta_change", test_score, adj_mat, delta, package = "atSNP")+     const <- .Call("test_func_delta_change", test_score, adj_mat, theta, package = "atSNP")+     ## in R+     adj_sum <- rowSums(adj_mat)+     wei_sum <- rowSums(test_score ^ theta)+     const.r <- prod(adj_sum) * sum(wei_sum / adj_sum)+     expect_equal(const, const.r)+   }+ })Test passed 🎉> > test_that("Error: sample distributions are not expected.", {+   ## parameters+   p <- 0.1+   delta <- .Call("test_find_percentile_change", score_diff, p, package = "atSNP")+   theta <- .Call("test_find_theta_change", test_score, adj_mat, delta, package = "atSNP")+   prob_start <- rev(rowSums(test_score ^ theta) / rowSums(adj_mat))+   ## construct the delta matrix+   delta <- matrix(1, nrow = 4 * motif_len, ncol = 2 * motif_len - 1)+   for(pos in seq(motif_len)) {+     delta[seq(4) + 4 * (pos - 1), ] <- snpInfo$prior+     delta[seq(4) + 4 * (pos - 1), pos - 1 + seq(motif_len)] <- t(test_pwm)+     delta[seq(4) + 4 * (pos - 1), motif_len] <- test_score[motif_len + 1 - pos, ] ^ theta+     delta[seq(4) + 4 * (pos - 1), ] <- delta[seq(4) + 4 * (pos - 1),] / rep(colSums(delta[seq(4) + 4 * (pos - 1), ]), each = 4)+   }+   target_freq <- matrix(0, nrow = 4, ncol = 2 * motif_len - 1)+   for(pos in seq(motif_len)) {+     target_freq <- target_freq + delta[seq(4) + 4 * (pos - 1), ] * prob_start[pos]+   }+   target_freq <- t(target_freq)+   target_freq <- target_freq / rowSums(target_freq)+ +   results_i <- function(i) {+     ## generate 100 samples+     sample1 <- sapply(seq(100), function(x)+       .Call("test_importance_sample_change",+             adj_mat, snpInfo$prior, trans_mat, test_score, theta, package = "atSNP"))+     emp_freq1 <- get_freq(sample1)+     sample2 <- sapply(rep(theta, 100), drawonesample)+     emp_freq2 <- get_freq(sample2 - 1)+     ##    print(rbind(emp_freq1[10, ], emp_freq2[10, ], target_freq[10, ]))+     max(abs(emp_freq1 - target_freq)) > max(abs(emp_freq2 - target_freq))+   }+   +   if(Sys.info()[["sysname"]] == "Windows"){+     snow <- SnowParam(workers = 1, type = "SOCK")+     results<-bpmapply(results_i, seq(20), BPPARAM = snow,SIMPLIFY = FALSE)+   }else{+     results<-bpmapply(results_i, seq(20), BPPARAM = MulticoreParam(workers = 1),+                               SIMPLIFY = FALSE)+   }+ +   print(sum(unlist(results)))+   print(pbinom(sum(unlist(results)), size = 20, prob = 0.5))+ })[1] 8[1] 0.2517223── Skip: Error: sample distributions are not expected. ─────────────────────────Reason: empty test> > test_that("Error: the chosen pvalues should have the smaller variance.", {+   .structure_diff <- function(pval_mat) {+     id <- apply(pval_mat[, c(2, 4)], 1, which.min)+     return(cbind(pval_mat[, c(1, 3)][cbind(seq_along(id), id)],+                  pval_mat[, c(2, 4)][cbind(seq_along(id), id)]))+   }+   for(p in c(0.05, 0.1, 0.2, 0.5)) {+     p_values <- .Call("test_p_value_change", test_pwm, test_score, adj_mat, snpInfo$prior, snpInfo$transition, score_diff, pval_ratio, quantile(score_diff, 1 - p), 100, package = "atSNP")$score+     p_values_s <- .structure_diff(p_values)+     expect_equal(p_values_s[, 2], apply(p_values[, c(2, 4)], 1, min))+   }+ })Test passed 😸> > proc.time()   user  system elapsed  21.539   0.824  22.420

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"Copyright (C) 2025 The R Foundation for Statistical ComputingPlatform: aarch64-unknown-linux-gnuR 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(atSNP)> library(BiocParallel)> library(testthat)> data(example)> > trans_mat <- matrix(rep(snpInfo$prior, each = 4), nrow = 4)> test_pwm <- motif_library$SIX5_disc1> scores <- as.matrix(motif_scores$motif.scores[3:4, 4:5])> score_diff <- abs(scores[,2]-scores[,1])> > test_score <- test_pwm> for(i in seq(nrow(test_score))) {+   for(j in seq(ncol(test_score))) {+     test_score[i, j] <- exp(mean(log(test_pwm[i, j] / test_pwm[i, -j])))+   }+ }> > adj_mat <- test_pwm + rowMeans(test_pwm)> motif_len <- nrow(test_pwm)> > ## these are functions for this test only> drawonesample <- function(theta) {+     prob_start <- sapply(seq(motif_len),+                          function(j)+                              sum(snpInfo$prior * test_score[motif_len + 1 - j, ] ^ theta *+                                      adj_mat[motif_len + 1 - j, ]) /+                                          sum(snpInfo$prior * adj_mat[motif_len + 1 - j, ])+                          )+     id <- sample(seq(motif_len), 1, prob = prob_start)+     sample <- sample(1:4, 2 * motif_len - 1, replace = TRUE, prob = snpInfo$prior)+     delta <- adj_mat+     delta[motif_len + 1 - id, ] <- delta[motif_len + 1 - id, ] * test_score[motif_len + 1 - id, ] ^ theta+     sample[id - 1 + seq(motif_len)] <- apply(delta, 1, function(x)+         sample(seq(4), 1, prob = x * snpInfo$prior))+     sc <- 0+     for(s in seq(motif_len)) {+       delta <- adj_mat+       delta[motif_len + 1 - s, ] <- delta[motif_len + 1 - s, ] * test_score[motif_len + 1 - s, ] ^ theta+       sc <- sc + prod(delta[cbind(seq(motif_len), sample[s - 1 + seq(motif_len)])])+     }+     sample <- c(sample, id, sc)+     return(sample)+ }> jointprob <- function(x) prod(test_pwm[cbind(seq(motif_len), x)])> maxjointprob <- function(x) {+   maxp <- -Inf+   p <- -Inf+   for(i in 1:motif_len) {+     p <- jointprob(x[i:(i+motif_len - 1)])+     if(p > maxp)+       maxp <- p+   }+   for(i in 1:motif_len) {+     p <- jointprob(5 - x[(i+motif_len - 1):i])+     if(p > maxp)+       maxp <- p+   }+   return(maxp)+ }> get_freq <- function(sample) {+   emp_freq <- matrix(0, nrow = 2 * motif_len - 1, ncol = 4)+   for(i in seq(2 * motif_len - 1)) {+     for(j in seq(4)) {+       emp_freq[i, j] <- sum(sample[i, ] == j - 1)+     }+   }+   emp_freq <- emp_freq / rowSums(emp_freq)+   return(emp_freq)+ }> > test_that("Error: quantile function computing are not equivalent.", {+   for(p in c(0.01, 0.1, 0.5, 0.9, 0.99)) {+     delta <- .Call("test_find_percentile_diff", score_diff, p, package = "atSNP")+     delta.r <- as.double(sort(abs(scores[,2]-scores[,1]))[ceiling((1 - p) * (nrow(scores)))])+     expect_equal(delta, delta.r)+   }+ })Test passed 🎉> > test_that("Error: the scores for samples are not equivalent.", {+   p <- 0.1+   delta <- .Call("test_find_percentile_diff", score_diff, p, package = "atSNP")+   theta <- .Call("test_find_theta_diff", test_score, adj_mat, snpInfo$prior, snpInfo$transition, delta, package = "atSNP")+   ## Use R code to generate a random sample+   for(i in seq(10)) {+     sample <- drawonesample(theta)+     sample_score <- .Call("test_compute_sample_score_diff", test_pwm, test_score, adj_mat, sample[seq(2 * motif_len - 1)] - 1, sample[2 * motif_len] - 1, theta, package = "atSNP")+     expect_equal(sample[2 * motif_len + 1], sample_score[1])+     sample1 <- sample2 <- sample3 <- sample+     sample1[motif_len] <- seq(4)[-sample[motif_len]][1]+     sample2[motif_len] <- seq(4)[-sample[motif_len]][2]+     sample3[motif_len] <- seq(4)[-sample[motif_len]][3]+     sample_score_r <- log(maxjointprob(sample[seq(2 * motif_len - 1)])) -+       log(c(maxjointprob(sample1[seq(2 * motif_len - 1)]),+             maxjointprob(sample2[seq(2 * motif_len - 1)]),+             maxjointprob(sample3[seq(2 * motif_len - 1)])))+     expect_equal(sample_score_r, sample_score[-1])+   }+   +   ## Use C code to generate a random sample+   delta <- matrix(1, nrow = 4 * motif_len, ncol = 2 * motif_len - 1)+   for(pos in seq(motif_len)) {+       for(j in (pos + motif_len - 1) : 1) {+           if(j < pos + motif_len - 1) {+               delta[4 * (pos - 1) + seq(4), j] <- sum(snpInfo$prior * delta[4 * (pos - 1) + seq(4), j + 1])+           }+           if(j >= pos) {+               delta[4 * (pos - 1) + seq(4), j] <- delta[4 * (pos - 1) + seq(4), j] * adj_mat[j - pos + 1, ]+           }+           if(j == motif_len) {+               delta[4 * (pos - 1) + seq(4), j] <- delta[4 * (pos - 1) + seq(4), j] * test_score[j - pos + 1, ] ^ theta+           }+       }+   }+   for(i in seq(10)) {+     sample <- .Call("test_importance_sample_diff", delta, snpInfo$prior, trans_mat, test_pwm, theta, package = "atSNP")+     start_pos <- sample[2 * motif_len] + 1+     adj_score <- 0+     for(s in seq_len(motif_len)) {+       adj_s <- sum(log(adj_mat[cbind(seq(motif_len), sample[s - 1 + seq(motif_len)] + 1)]))+       adj_s <- adj_s + theta * log(test_score[motif_len + 1 - s, sample[motif_len] + 1])+       adj_score <- adj_score + exp(adj_s)+     }+     sample_score <- .Call("test_compute_sample_score_diff", test_pwm, test_score, adj_mat, sample[seq(2 * motif_len - 1)], sample[2 * motif_len], theta, package = "atSNP")+     expect_equal(adj_score, sample_score[1])+   }+ })Test passed 🌈> > test_that("Error: compute the normalizing constant.", {+ +   ## parameters+   p <- 0.1+   delta <- .Call("test_find_percentile_diff", score_diff, p, package = "atSNP")+   theta <- .Call("test_find_theta_diff", test_score, adj_mat, snpInfo$prior, snpInfo$transition, delta, package = "atSNP")+   +   ##+   const <- .Call("test_func_delta_diff", test_score, adj_mat, snpInfo$prior, trans_mat, theta, package = "atSNP")+ +    prob_start <- sapply(seq(motif_len),+                          function(j)+                              sum(snpInfo$prior * test_score[motif_len + 1 - j, ] ^ theta *+                                      adj_mat[motif_len + 1 - j, ]) /+                                          sum(snpInfo$prior * adj_mat[motif_len + 1 - j, ])+                          )+   +   const.r <- prod(colSums(snpInfo$prior * t(adj_mat))) * sum(prob_start)+   expect_equal(const, const.r)+ })Test passed 🌈> > test_that("Error: sample distributions are not expected.", {+   +   ## parameters+   p <- 0.1+   delta <- .Call("test_find_percentile_diff", score_diff, p, package = "atSNP")+   theta <- .Call("test_find_theta_diff", test_score, adj_mat, snpInfo$prior, snpInfo$transition, delta, package = "atSNP")+ +   ## construct the delta matrix+   delta <- matrix(1, nrow = 4 * motif_len, ncol = 2 * motif_len - 1)+    for(pos in seq(motif_len)) {+         for(j in (pos + motif_len - 1) : 1) {+             if(j < pos + motif_len - 1) {+                 delta[4 * (pos - 1) + seq(4), j] <- sum(snpInfo$prior * delta[4 * (pos - 1) + seq(4), j + 1])+             }+             if(j >= pos) {+                 delta[4 * (pos - 1) + seq(4), j] <- delta[4 * (pos - 1) + seq(4), j] * adj_mat[j - pos + 1, ]+             }+             if(j == motif_len) {+                 delta[4 * (pos - 1) + seq(4), j] <- delta[4 * (pos - 1) + seq(4), j] * test_score[j - pos + 1, ] ^ theta+             }+         }+     }+ +   target_freq <- matrix(0, nrow = 4, ncol = 2 * motif_len - 1)+   +   mat <- snpInfo$prior * matrix(delta[, 1], nrow = 4)+   wei <- colSums(mat)+   for(j in seq(2 * motif_len - 1)) {+       for(pos in seq(motif_len)) {+           tmp <- delta[seq(4) + 4 * (pos - 1), j] * snpInfo$prior+           target_freq[, j] <- target_freq[, j] +  tmp / sum(tmp) * wei[pos]+       }+   }+   target_freq <- t(target_freq)+   target_freq <- target_freq / rowSums(target_freq)+ +   results_i <- function(i) {+     ## generate 100 samples+     sample1 <- sapply(seq(100), function(x)+       .Call("test_importance_sample_diff",+             delta, snpInfo$prior, trans_mat, test_score, theta, package = "atSNP"))+     emp_freq1 <- get_freq(sample1)+     +     sample2 <- sapply(rep(theta, 100), drawonesample)+     emp_freq2 <- get_freq(sample2 - 1)+     +     ##    print(rbind(emp_freq1[10, ], emp_freq2[10, ], target_freq[10, ]))+     max(abs(emp_freq1 - target_freq)) > max(abs(emp_freq2 - target_freq))+   }+  +   if(Sys.info()[["sysname"]] == "Windows"){+     snow <- SnowParam(workers = 1, type = "SOCK")+     results<-bpmapply(results_i, seq(20), BPPARAM = snow,SIMPLIFY = FALSE)+   }else{+     results<-bpmapply(results_i, seq(20), BPPARAM = MulticoreParam(workers = 1),+                       SIMPLIFY = FALSE)+   }+   +   print(sum(unlist(results)))+ +   print(pbinom(sum(unlist(results)), size = 20, prob = 0.5))+   + })[1] 13[1] 0.9423409── Skip: Error: sample distributions are not expected. ─────────────────────────Reason: empty test> > test_that("Error: the chosen pvalues should have the smaller variance.", {+ +   .structure_diff <- function(pval_mat) {+     id <- apply(pval_mat[, c(2, 4)], 1, which.min)+     return(cbind(pval_mat[, c(1, 3)][cbind(seq_along(id), id)],+                  pval_mat[, c(2, 4)][cbind(seq_along(id), id)]))+   }+   +   for(p in c(0.05, 0.1, 0.2, 0.5)) {+     p_values <- .Call("test_p_value_diff", test_pwm, test_score, adj_mat, snpInfo$prior, snpInfo$transition, score_diff, quantile(score_diff, 1 - p), 100, package = "atSNP")+     p_values_s <- .structure_diff(p_values)+     expect_equal(p_values_s[, 2], apply(p_values[, c(2, 4)], 1, min))+   }+ })Test passed 😀> > proc.time()   user  system elapsed  21.626   0.945  22.622

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"Copyright (C) 2025 The R Foundation for Statistical ComputingPlatform: aarch64-unknown-linux-gnuR 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(atSNP)> library(BiocParallel)> library(testthat)> data(example)> > trans_mat <- matrix(rep(snpInfo$prior, each = 4), nrow = 4)> test_pwm <- motif_library$SIX5_disc1> scores <- as.matrix(motif_scores$motif.scores[3:4, 4:5])> > motif_len <- nrow(test_pwm)> > ## these are functions for this test only> drawonesample <- function(theta) {+   delta <- snpInfo$prior * t(test_pwm ^ theta)+   delta <- delta / rep(colSums(delta), each = 4)+   sample <- sample(1:4, 2 * motif_len - 1, replace = TRUE, prob = snpInfo$prior)+   id <- sample(seq(motif_len), 1)+   sample[id : (id + motif_len - 1)] <- apply(delta, 2, function(x) sample(1:4, 1, prob = x))+   sc <- s_cond <- 0+   for(s in seq(motif_len)) {+     sc <- sc + prod(test_pwm[cbind(seq(motif_len),+                                   sample[s : (s + motif_len - 1)])]) ^ theta+   }+   s_cond <- prod(test_pwm[cbind(seq(motif_len),+                                 sample[id : (id + motif_len - 1)])]) ^ theta+   sample <- c(sample, id, sc, s_cond)+   return(sample)+ }> jointprob <- function(x) prod(test_pwm[cbind(seq(motif_len), x)])> maxjointprob <- function(x) {+   maxp <- -Inf+   p <- -Inf+   for(i in 1:motif_len) {+     p <- jointprob(x[i:(i+motif_len - 1)])+     if(p > maxp)+       maxp <- p+   }+   for(i in 1:motif_len) {+     p <- jointprob(5 - x[(i+motif_len - 1):i])+     if(p > maxp)+       maxp <- p+   }+   return(maxp)+ }> get_freq <- function(sample) {+   ids <- cbind(+                rep(sample[motif_len * 2, ], each = motif_len) + seq(motif_len),+                rep(seq(100), each = motif_len))+   sample_motif <- matrix(sample[ids], nrow = motif_len) + 1+   emp_freq <- matrix(0, nrow = motif_len, ncol = 4)+   for(i in seq(motif_len)) {+     for(j in seq(4)) {+       emp_freq[i, j] <- sum(sample_motif[i, ] == j)+     }+   }+   emp_freq <- emp_freq / rowSums(emp_freq)+   return(emp_freq)+ }> > test_that("Error: quantile function computing are not equivalent.", {+   for(p in c(0.01, 0.1, 0.5, 0.9, 0.99)) {+     delta <- .Call("test_find_percentile", c(scores), p, package = "atSNP")+     delta.r <- -sort(-c(scores))[as.integer(p * length(scores)) + 1]+     expect_equal(delta, delta.r)+   }+ })Test passed 🥇> > test_that("Error: the scores for samples are not equivalent.", {+   p <- 0.01+   delta <- .Call("test_find_percentile", scores, p, package = "atSNP")+   theta <- .Call("test_find_theta", test_pwm, snpInfo$prior, snpInfo$transition, delta, package = "atSNP")+   ## Use R code to generate a random sample+   for(i in seq(10)) {+     sample <- drawonesample(theta)+     sample_score <- .Call("test_compute_sample_score", test_pwm, sample[seq(2 * motif_len - 1)] - 1, sample[motif_len * 2] - 1, theta, package = "atSNP")+     expect_equal(sample[2 * motif_len + 1], sample_score[2])+     expect_equal(sample[2 * motif_len + 2], sample_score[3])+   }+   ## Use C code to generate a random sample+   for(i in seq(10)) {+     delta <- t(test_pwm ^ theta)+     delta <- cbind(matrix(+                           sum(snpInfo$prior * delta[, 1]),+                           nrow = 4, ncol = motif_len - 1), delta)+     sample <- .Call("test_importance_sample", delta, snpInfo$prior, trans_mat, test_pwm, theta, package = "atSNP")+     start_pos <- sample[motif_len * 2]+     adj_score <- 0+     for(s in seq(motif_len) - 1) {+       adj_score <- adj_score + prod(test_pwm[cbind(seq(motif_len),+                                                    sample[s + seq(motif_len)] + 1)]) ^ theta+     }+     adj_score_cond <- prod(test_pwm[cbind(seq(motif_len), sample[start_pos + seq(motif_len)] + 1)]) ^ theta+     sample_score <- .Call("test_compute_sample_score", test_pwm, sample[seq(2 * motif_len - 1)], sample[motif_len * 2], theta, package = "atSNP")+     expect_equal(adj_score, sample_score[2])+     expect_equal(adj_score_cond, sample_score[3])+   }+ })Test passed 😀> > test_that("Error: compute the normalizing constant.", {+   ## parameters+   p <- 0.01+   delta <- .Call("test_find_percentile", scores, p, package = "atSNP")+   theta <- .Call("test_find_theta", test_pwm, snpInfo$prior, snpInfo$transition, delta, package = "atSNP")+   ##+   const <- .Call("test_func_delta", test_pwm, snpInfo$prior, trans_mat, theta, package = "atSNP")+   const.r <- prod(colSums(snpInfo$prior * t(test_pwm) ^ theta)) * motif_len+   expect_equal(abs(const - const.r) / const < 1e-5, TRUE)+ })Test passed 🎊> > test_that("Error: sample distributions are not expected.", {+   ## parameters+   p <- 0.1+   delta <- .Call("test_find_percentile", scores, p, package = "atSNP")+   theta <- .Call("test_find_theta", test_pwm, snpInfo$prior, trans_mat, delta, package = "atSNP")+   delta <- t(test_pwm ^ theta)+   delta <- cbind(matrix(+                         sum(snpInfo$prior * delta[, 1]),+                         nrow = 4, ncol = motif_len - 1), delta)+ +   results_i <- function(i) {+     ## generate 100 samples+     sample <- sapply(seq(100), function(x)+                      .Call("test_importance_sample",+                            delta, snpInfo$prior, trans_mat, test_pwm, theta, package = "atSNP"))+     emp_freq1 <- get_freq(sample)+     target_freq <- test_pwm ^ theta * snpInfo$prior+     target_freq <- target_freq / rowSums(target_freq)+     ## generate samples in R+     sample <- sapply(rep(theta, 100), drawonesample)+     emp_freq2 <- get_freq(sample[seq(2 * motif_len), ] - 1)+     max(abs(emp_freq1 - target_freq)) > max(abs(emp_freq2 - target_freq))+   }+ +   if(Sys.info()[["sysname"]] == "Windows"){+     snow <- SnowParam(workers = 1, type = "SOCK")+     results<-bpmapply(results_i, seq(20), BPPARAM = snow,SIMPLIFY = FALSE)+   }else{+     results<-bpmapply(results_i, seq(20), BPPARAM = MulticoreParam(workers = 1),+                       SIMPLIFY = FALSE)+   }+   +   print(sum(unlist(results)))+   print(pbinom(sum(unlist(results)), size = 20, prob = 0.5))+ })[1] 8[1] 0.2517223── Skip: Error: sample distributions are not expected. ─────────────────────────Reason: empty test> > test_that("Error: the chosen pvalues should have the smaller variance.", {+   .structure <- function(pval_mat) {+     id1 <- apply(pval_mat[, c(2, 4)], 1, which.min)+     return(cbind(+                  pval_mat[, c(1, 3)][cbind(seq_along(id1), id1)],+                  pval_mat[, c(2, 4)][cbind(seq_along(id1), id1)])+            )+   }+   for(p in c(0.01, 0.05, 0.1)) {+     theta <- .Call("test_find_theta", test_pwm, snpInfo$prior, trans_mat, quantile(c(scores), 1 - p), package = "atSNP")+     p_values <- .Call("test_p_value", test_pwm, snpInfo$prior, snpInfo$transition, c(scores), theta, 100, package = "atSNP")+     p_values_s <- .structure(p_values)+     expect_equal(p_values_s[, 2], apply(p_values[, c(2, 4)], 1, min))+   }+ })Test passed 🎉> > proc.time()   user  system elapsed  19.192   0.910  20.146