##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/R/R/bin/R CMD check --install=check:aroma.light.install-out.txt --library=/home/biocbuild/R/R/site-library --no-vignettes --timings aroma.light_3.40.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.22-bioc/meat/aroma.light.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 ‘aroma.light/DESCRIPTION’ ... OK
* this is package ‘aroma.light’ version ‘3.40.0’
* package encoding: latin1
* 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 ... NOTE
Found the following hidden files and directories:
  inst/rsp/.rspPlugins
These were most likely included in error. See section ‘Package
structure’ in the ‘Writing R Extensions’ manual.
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘aroma.light’ can be installed ... OK
* checking installed package size ... OK
* checking package 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 whether startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* 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 examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                    user system elapsed
normalizeCurveFit 10.604  0.016  10.637
normalizeAffine   10.404  0.024  10.446
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘backtransformAffine.matrix.R’
  Running ‘backtransformPrincipalCurve.matrix.R’
  Running ‘callNaiveGenotypes.R’
  Running ‘distanceBetweenLines.R’
  Running ‘findPeaksAndValleys.R’
  Running ‘fitPrincipalCurve.matrix.R’
  Running ‘fitXYCurve.matrix.R’
  Running ‘iwpca.matrix.R’
  Running ‘likelihood.smooth.spline.R’
  Running ‘medianPolish.matrix.R’
  Running ‘normalizeAffine.matrix.R’
  Running ‘normalizeAverage.list.R’
  Running ‘normalizeAverage.matrix.R’
  Running ‘normalizeCurveFit.matrix.R’
  Running ‘normalizeDifferencesToAverage.R’
  Running ‘normalizeFragmentLength-ex1.R’
  Running ‘normalizeFragmentLength-ex2.R’
  Running ‘normalizeQuantileRank.list.R’
  Running ‘normalizeQuantileRank.matrix.R’
  Running ‘normalizeQuantileSpline.matrix.R’
  Running ‘normalizeTumorBoost,flavors.R’
  Running ‘normalizeTumorBoost.R’
  Running ‘robustSmoothSpline.R’
  Running ‘rowAverages.matrix.R’
  Running ‘sampleCorrelations.matrix.R’
  Running ‘sampleTuples.R’
  Running ‘wpca.matrix.R’
  Running ‘wpca2.matrix.R’
 OK
* checking PDF version of manual ... OK
* DONE

Status: 1 NOTE
See
  ‘/home/biocbuild/bbs-3.22-bioc/meat/aroma.light.Rcheck/00check.log’
for details.

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/R/R/bin/R CMD INSTALL aroma.light
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/R/R-4.5.0/site-library’
* installing *source* package ‘aroma.light’ ...
** this is package ‘aroma.light’ version ‘3.40.0’
** using staged installation
** R
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (aroma.light)

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> X <- matrix(1:8, nrow=4, ncol=2)
> X[2,2] <- NA_integer_
> 
> print(X)
     [,1] [,2]
[1,]    1    5
[2,]    2   NA
[3,]    3    7
[4,]    4    8
> 
> # Returns a 4x2 matrix
> print(backtransformAffine(X, a=c(1,5)))
     [,1] [,2]
[1,]    0    0
[2,]    1   NA
[3,]    2    2
[4,]    3    3
> 
> # Returns a 4x2 matrix
> print(backtransformAffine(X, b=c(1,1/2)))
     [,1] [,2]
[1,]    1   10
[2,]    2   NA
[3,]    3   14
[4,]    4   16
> 
> # Returns a 4x2 matrix
> print(backtransformAffine(X, a=matrix(1:4,ncol=1)))
     [,1] [,2]
[1,]    0    4
[2,]    0   NA
[3,]    0    4
[4,]    0    4
> 
> # Returns a 4x2 matrix
> print(backtransformAffine(X, a=matrix(1:3,ncol=1)))
     [,1] [,2]
[1,]    0    4
[2,]    0   NA
[3,]    0    4
[4,]    3    7
> 
> # Returns a 4x2 matrix
> print(backtransformAffine(X, a=matrix(1:2,ncol=1), b=c(1,2)))
     [,1] [,2]
[1,]    0    2
[2,]    0   NA
[3,]    2    3
[4,]    2    3
> 
> # Returns a 4x1 matrix
> print(backtransformAffine(X, b=c(1,1/2), project=TRUE))
     [,1]
[1,]  2.8
[2,]  1.6
[3,]  5.2
[4,]  6.4
> 
> # If the columns of X are identical, and a identity
> # backtransformation is applied and projected, the
> # same matrix is returned.
> X <- matrix(1:4, nrow=4, ncol=3)
> Y <- backtransformAffine(X, b=c(1,1,1), project=TRUE)
> print(X)
     [,1] [,2] [,3]
[1,]    1    1    1
[2,]    2    2    2
[3,]    3    3    3
[4,]    4    4    4
> print(Y)
     [,1]
[1,]    1
[2,]    2
[3,]    3
[4,]    4
> stopifnot(sum(X[,1]-Y) <= .Machine$double.eps)
> 
> 
> # If the columns of X are identical, and a identity
> # backtransformation is applied and projected, the
> # same matrix is returned.
> X <- matrix(1:4, nrow=4, ncol=3)
> X[,2] <- X[,2]*2; X[,3] <- X[,3]*3
> print(X)
     [,1] [,2] [,3]
[1,]    1    2    3
[2,]    2    4    6
[3,]    3    6    9
[4,]    4    8   12
> Y <- backtransformAffine(X, b=c(1,2,3))
> print(Y)
     [,1] [,2] [,3]
[1,]    1    1    1
[2,]    2    2    2
[3,]    3    3    3
[4,]    4    4    4
> Y <- backtransformAffine(X, b=c(1,2,3), project=TRUE)
> print(Y)
     [,1]
[1,]    1
[2,]    2
[3,]    3
[4,]    4
> stopifnot(sum(X[,1]-Y) <= .Machine$double.eps)
> 
> proc.time()
   user  system elapsed 
  0.292   0.037   0.313 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> # Consider the case where K=4 measurements have been done
> # for the same underlying signals 'x'.  The different measurements
> # have different systematic variation
> #
> #   y_k = f(x_k) + eps_k; k = 1,...,K.
> #
> # In this example, we assume non-linear measurement functions
> #
> #   f(x) = a + b*x + x^c + eps(b*x)
> #
> # where 'a' is an offset, 'b' a scale factor, and 'c' an exponential.
> # We also assume heteroscedastic zero-mean noise with standard
> # deviation proportional to the rescaled underlying signal 'x'.
> #
> # Furthermore, we assume that measurements k=2 and k=3 undergo the
> # same transformation, which may illustrate that the come from
> # the same batch. However, when *fitting* the model below we
> # will assume they are independent.
> 
> # Transforms
> a <- c(2, 15, 15,   3)
> b <- c(2,  3,  3,   4)
> c <- c(1,  2,  2, 1/2)
> K <- length(a)
> 
> # The true signal
> N <- 1000
> x <- rexp(N)
> 
> # The noise
> bX <- outer(b,x)
> E <- apply(bX, MARGIN=2, FUN=function(x) rnorm(K, mean=0, sd=0.1*x))
> 
> # The transformed signals with noise
> Xc <- t(sapply(c, FUN=function(c) x^c))
> Y <- a + bX + Xc + E
> Y <- t(Y)
> 
> 
> 
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # Fit principal curve
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # Fit principal curve through Y = (y_1, y_2, ..., y_K)
> fit <- fitPrincipalCurve(Y)
> 
> # Flip direction of 'lambda'?
> rho <- cor(fit$lambda, Y[,1], use="complete.obs")
> flip <- (rho < 0)
> if (flip) {
+   fit$lambda <- max(fit$lambda, na.rm=TRUE)-fit$lambda
+ }
> 
> L <- ncol(fit$s)
> 
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # Backtransform data according to model fit
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # Backtransform toward the principal curve (the "common scale")
> YN1 <- backtransformPrincipalCurve(Y, fit=fit)
> stopifnot(ncol(YN1) == K)
> 
> 
> # Backtransform toward the first dimension
> YN2 <- backtransformPrincipalCurve(Y, fit=fit, targetDimension=1)
> stopifnot(ncol(YN2) == K)
> 
> 
> # Backtransform toward the last (fitted) dimension
> YN3 <- backtransformPrincipalCurve(Y, fit=fit, targetDimension=L)
> stopifnot(ncol(YN3) == K)
> 
> 
> # Backtransform toward the third dimension (dimension by dimension)
> # Note, this assumes that K == L.
> YN4 <- Y
> for (cc in 1:L) {
+   YN4[,cc] <- backtransformPrincipalCurve(Y, fit=fit,
+                                   targetDimension=1, dimensions=cc)
+ }
> stopifnot(identical(YN4, YN2))
> 
> 
> # Backtransform a subset toward the first dimension
> # Note, this assumes that K == L.
> YN5 <- backtransformPrincipalCurve(Y, fit=fit,
+                                targetDimension=1, dimensions=2:3)
> stopifnot(identical(YN5, YN2[,2:3]))
> stopifnot(ncol(YN5) == 2)
> 
> 
> # Extract signals from measurement #2 and backtransform according
> # its model fit.  Signals are standardized to target dimension 1.
> y6 <- Y[,2,drop=FALSE]
> yN6 <- backtransformPrincipalCurve(y6, fit=fit, dimensions=2,
+                                                targetDimension=1)
> stopifnot(identical(yN6, YN2[,2,drop=FALSE]))
> stopifnot(ncol(yN6) == 1)
> 
> 
> # Extract signals from measurement #2 and backtransform according
> # the the model fit of measurement #3 (because we believe these
> # two have undergone very similar transformations.
> # Signals are standardized to target dimension 1.
> y7 <- Y[,2,drop=FALSE]
> yN7 <- backtransformPrincipalCurve(y7, fit=fit, dimensions=3,
+                                                targetDimension=1)
> stopifnot(ncol(yN7) == 1)
> 
> rho <- cor(yN7, yN6)
> print(rho)
          [,1]
[1,] 0.9999727
> stopifnot(rho > 0.999)
> 
> proc.time()
   user  system elapsed 
  0.929   0.080   0.995 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> layout(matrix(1:3, ncol=1))
> par(mar=c(2,4,4,1)+0.1)
> 
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # A bimodal distribution
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> xAA <- rnorm(n=10000, mean=0, sd=0.1)
> xBB <- rnorm(n=10000, mean=1, sd=0.1)
> x <- c(xAA,xBB)
> fit <- findPeaksAndValleys(x)
> print(fit)
    type            x      density
1   peak -0.003313805 1.6946274351
2 valley  0.494158310 0.0004023596
3   peak  0.995810864 1.6906147624
> calls <- callNaiveGenotypes(x, cn=rep(1,length(x)), verbose=-20)
Calling genotypes from allele B fractions (BAFs)...
 Fitting naive genotype model...
  Fitting naive genotype model from normal allele B fractions (BAFs)...
   Flavor: density
   Censoring BAFs...
    Before:
          Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
    -0.3609527 -0.0000474  0.4856395  0.5003356  0.9999498  1.3952542 
    [1] 20000
    After:
          Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
          -Inf -0.0000474  0.4856395             0.9999498        Inf 
    [1] 16841
   Censoring BAFs...done
   Copy number level #1 (C=1) of 1...
    Identified extreme points in density of BAF:
        type          x     density
    1   peak 0.01107511 1.641569800
    2 valley 0.49824961 0.003770484
    3   peak 0.98199331 1.637468529
    Local minimas ("valleys") in BAF:
        type         x     density
    2 valley 0.4982496 0.003770484
   Copy number level #1 (C=1) of 1...done
  Fitting naive genotype model from normal allele B fractions (BAFs)...done
  [[1]]
  [[1]]$flavor
  [1] "density"
  
  [[1]]$cn
  [1] 1
  
  [[1]]$nbrOfGenotypeGroups
  [1] 2
  
  [[1]]$tau
  [1] 0.4982496
  
  [[1]]$n
  [1] 16841
  
  [[1]]$fit
      type          x     density
  1   peak 0.01107511 1.641569800
  2 valley 0.49824961 0.003770484
  3   peak 0.98199331 1.637468529
  
  [[1]]$fitValleys
      type         x     density
  2 valley 0.4982496 0.003770484
  
  
  attr(,"class")
  [1] "NaiveGenotypeModelFit" "list"                 
 Fitting naive genotype model...done
 Copy number level #1 (C=1) of 1...
  Model fit:
  $flavor
  [1] "density"
  
  $cn
  [1] 1
  
  $nbrOfGenotypeGroups
  [1] 2
  
  $tau
  [1] 0.4982496
  
  $n
  [1] 16841
  
  $fit
      type          x     density
  1   peak 0.01107511 1.641569800
  2 valley 0.49824961 0.003770484
  3   peak 0.98199331 1.637468529
  
  $fitValleys
      type         x     density
  2 valley 0.4982496 0.003770484
  
  Genotype threshholds [1]: 0.498249614350945
  TCN=1 => BAF in {0,1}.
  Call regions: A = (-Inf,0.498], B = (0.498,+Inf)
 Copy number level #1 (C=1) of 1...done
Calling genotypes from allele B fractions (BAFs)...done
> xc <- split(x, calls)
> print(table(calls))
calls
    0     1 
10000 10000 
> xx <- c(list(x),xc)
> plotDensity(xx, adjust=1.5, lwd=2, col=seq_along(xx), main="(AA,BB)")
> abline(v=fit$x)
> 
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # A trimodal distribution with missing values
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> xAB <- rnorm(n=10000, mean=1/2, sd=0.1)
> x <- c(xAA,xAB,xBB)
> x[sample(length(x), size=0.05*length(x))] <- NA_real_
> x[sample(length(x), size=0.01*length(x))] <- -Inf
> x[sample(length(x), size=0.01*length(x))] <- +Inf
> fit <- findPeaksAndValleys(x)
> print(fit)
    type            x   density
1   peak -0.006215467 1.1785340
2 valley  0.246444071 0.1871392
3   peak  0.503114078 1.1664826
4 valley  0.743742209 0.1903262
5   peak  0.996401747 1.1722004
> calls <- callNaiveGenotypes(x)
> xc <- split(x, calls)
> print(table(calls))
calls
   0  0.5    1 
9634 9276 9629 
> xx <- c(list(x),xc)
> plotDensity(xx, adjust=1.5, lwd=2, col=seq_along(xx), main="(AA,AB,BB)")
> abline(v=fit$x)
> 
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # A trimodal distribution with clear separation
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> xAA <- rnorm(n=10000, mean=0, sd=0.02)
> xAB <- rnorm(n=10000, mean=1/2, sd=0.02)
> xBB <- rnorm(n=10000, mean=1, sd=0.02)
> x <- c(xAA,xAB,xBB)
> fit <- findPeaksAndValleys(x)
> print(fit)
    type            x      density
1   peak -0.002233814 2.603843e+00
2 valley  0.246707916 3.280104e-05
3   peak  0.495649645 2.603723e+00
4 valley  0.747452774 3.146063e-05
5   peak  0.996394503 2.602069e+00
> calls <- callNaiveGenotypes(x)
> xc <- split(x, calls)
> print(table(calls))
calls
    0   0.5     1 
10000 10000 10000 
> xx <- c(list(x),xc)
> plotDensity(xx, adjust=1.5, lwd=2, col=seq_along(xx), main="(AA',AB',BB')")
> abline(v=fit$x)
> 
> proc.time()
   user  system elapsed 
  0.589   0.060   0.636 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> for (zzz in 0) {
+ 
+ # This example requires plot3d() in R.basic [http://www.braju.com/R/]
+ if (!require(pkgName <- "R.basic", character.only=TRUE)) break
+ 
+ layout(matrix(1:4, nrow=2, ncol=2, byrow=TRUE))
+ 
+ ############################################################
+ # Lines in two-dimensions
+ ############################################################
+ x <- list(a=c(1,0), b=c(1,2))
+ y <- list(a=c(0,2), b=c(1,1))
+ fit <- distanceBetweenLines(ax=x$a, bx=x$b, ay=y$a, by=y$b)
+ 
+ xlim <- ylim <- c(-1,8)
+ plot(NA, xlab="", ylab="", xlim=ylim, ylim=ylim)
+ 
+ # Highlight the offset coordinates for both lines
+ points(t(x$a), pch="+", col="red")
+ text(t(x$a), label=expression(a[x]), adj=c(-1,0.5))
+ points(t(y$a), pch="+", col="blue")
+ text(t(y$a), label=expression(a[y]), adj=c(-1,0.5))
+ 
+ v <- c(-1,1)*10
+ xv <- list(x=x$a[1]+x$b[1]*v, y=x$a[2]+x$b[2]*v)
+ yv <- list(x=y$a[1]+y$b[1]*v, y=y$a[2]+y$b[2]*v)
+ 
+ lines(xv, col="red")
+ lines(yv, col="blue")
+ 
+ points(t(fit$xs), cex=2.0, col="red")
+ text(t(fit$xs), label=expression(x(s)), adj=c(+2,0.5))
+ points(t(fit$yt), cex=1.5, col="blue")
+ text(t(fit$yt), label=expression(y(t)), adj=c(-1,0.5))
+ print(fit)
+ 
+ 
+ ############################################################
+ # Lines in three-dimensions
+ ############################################################
+ x <- list(a=c(0,0,0), b=c(1,1,1))  # The 'diagonal'
+ y <- list(a=c(2,1,2), b=c(2,1,3))  # A 'fitted' line
+ fit <- distanceBetweenLines(ax=x$a, bx=x$b, ay=y$a, by=y$b)
+ 
+ xlim <- ylim <- zlim <- c(-1,3)
+ dummy <- t(c(1,1,1))*100
+ 
+ # Coordinates for the lines in 3d
+ v <- seq(-10,10, by=1)
+ xv <- list(x=x$a[1]+x$b[1]*v, y=x$a[2]+x$b[2]*v, z=x$a[3]+x$b[3]*v)
+ yv <- list(x=y$a[1]+y$b[1]*v, y=y$a[2]+y$b[2]*v, z=y$a[3]+y$b[3]*v)
+ 
+ for (theta in seq(30,140,length.out=3)) {
+   plot3d(dummy, theta=theta, phi=30, xlab="", ylab="", zlab="",
+                              xlim=ylim, ylim=ylim, zlim=zlim)
+ 
+   # Highlight the offset coordinates for both lines
+   points3d(t(x$a), pch="+", col="red")
+   text3d(t(x$a), label=expression(a[x]), adj=c(-1,0.5))
+   points3d(t(y$a), pch="+", col="blue")
+   text3d(t(y$a), label=expression(a[y]), adj=c(-1,0.5))
+ 
+   # Draw the lines
+   lines3d(xv, col="red")
+   lines3d(yv, col="blue")
+ 
+   # Draw the two points that are closest to each other
+   points3d(t(fit$xs), cex=2.0, col="red")
+   text3d(t(fit$xs), label=expression(x(s)), adj=c(+2,0.5))
+   points3d(t(fit$yt), cex=1.5, col="blue")
+   text3d(t(fit$yt), label=expression(y(t)), adj=c(-1,0.5))
+ 
+   # Draw the distance between the two points
+   lines3d(rbind(fit$xs,fit$yt), col="purple", lwd=2)
+ }
+ 
+ print(fit)
+ 
+ } # for (zzz in 0)
Loading required package: R.basic
Warning message:
In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
  there is no package called 'R.basic'
> rm(zzz)
> 
> proc.time()
   user  system elapsed 
  0.427   0.044   0.469 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> layout(matrix(1:3, ncol=1))
> par(mar=c(2,4,4,1)+0.1)
> 
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # A unimodal distribution
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> x1 <- rnorm(n=10000, mean=0, sd=1)
> x <- x1
> fit <- findPeaksAndValleys(x)
> print(fit)
    type          x      density
1   peak -4.3669129 2.790655e-04
2 valley -4.0728135 6.317032e-05
3   peak  0.1483782 3.926542e-01
4 valley  3.2969721 1.629907e-03
5   peak  3.4007719 1.668992e-03
> plot(density(x), lwd=2, main="x1")
> abline(v=fit$x)
> 
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # A trimodal distribution
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> x2 <- rnorm(n=10000, mean=4, sd=1)
> x3 <- rnorm(n=10000, mean=8, sd=1)
> x <- c(x1,x2,x3)
> fit <- findPeaksAndValleys(x)
> print(fit)
    type           x    density
1   peak -0.02906962 0.12377416
2 valley  1.99792259 0.04285338
3   peak  3.95252222 0.12331781
4 valley  5.94331814 0.04256237
5   peak  8.00650664 0.12438379
> plot(density(x), lwd=2, main="c(x1,x2,x3)")
> abline(v=fit$x)
> 
> 
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # A trimodal distribution with clear separation
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> x1b <- rnorm(n=10000, mean=0, sd=0.1)
> x2b <- rnorm(n=10000, mean=4, sd=0.1)
> x3b <- rnorm(n=10000, mean=8, sd=0.1)
> x <- c(x1b,x2b,x3b)
> 
> # Illustrating explicit usage of density()
> d <- density(x)
> fit <- findPeaksAndValleys(d, tol=0)
> print(fit)
    type          x      density
1   peak -0.0121477 3.428308e-01
2 valley  1.9893921 1.140263e-06
3   peak  3.9694100 3.425351e-01
4 valley  5.9709499 1.205258e-06
5   peak  7.9724897 3.420197e-01
> plot(d, lwd=2, main="c(x1b,x2b,x3b)")
> abline(v=fit$x)
> 
> proc.time()
   user  system elapsed 
  0.377   0.044   0.406 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> # Simulate data from the model y <- a + bx + x^c + eps(bx)
> J <- 1000
> x <- rexp(J)
> a <- c(2,15,3)
> b <- c(2,3,4)
> c <- c(1,2,1/2)
> bx <- outer(b,x)
> xc <- t(sapply(c, FUN=function(c) x^c))
> eps <- apply(bx, MARGIN=2, FUN=function(x) rnorm(length(b), mean=0, sd=0.1*x))
> y <- a + bx + xc + eps
> y <- t(y)
> 
> # Fit principal curve through (y_1, y_2, y_3)
> fit <- fitPrincipalCurve(y, verbose=TRUE)
Fitting principal curve...
 Data size: 1000x3
 Identifying missing values...
 Identifying missing values...done
 Data size after removing non-finite data points: 1000x3
 Calling principal_curve()...
Starting curve---distance^2: 1618343
Iteration 1---distance^2: 377.1033
Iteration 2---distance^2: 376.6076
Iteration 3---distance^2: 376.6252
  Converged: TRUE
  Number of iterations: 3
  Processing time/iteration: 0.2s (0.1s/iteration)
 Calling principal_curve()...done
Fitting principal curve...done
> 
> # Flip direction of 'lambda'?
> rho <- cor(fit$lambda, y[,1], use="complete.obs")
> flip <- (rho < 0)
> if (flip) {
+   fit$lambda <- max(fit$lambda, na.rm=TRUE)-fit$lambda
+ }
> 
> 
> # Backtransform (y_1, y_2, y_3) to be proportional to each other
> yN <- backtransformPrincipalCurve(y, fit=fit)
> 
> # Same backtransformation dimension by dimension
> yN2 <- y
> for (cc in 1:ncol(y)) {
+   yN2[,cc] <- backtransformPrincipalCurve(y, fit=fit, dimensions=cc)
+ }
> stopifnot(identical(yN2, yN))
> 
> 
> xlim <- c(0, 1.04*max(x))
> ylim <- range(c(y,yN), na.rm=TRUE)
> 
> 
> # Pairwise signals vs x before and after transform
> layout(matrix(1:4, nrow=2, byrow=TRUE))
> par(mar=c(4,4,3,2)+0.1)
> for (cc in 1:3) {
+   ylab <- substitute(y[c], env=list(c=cc))
+   plot(NA, xlim=xlim, ylim=ylim, xlab="x", ylab=ylab)
+   abline(h=a[cc], lty=3)
+   mtext(side=4, at=a[cc], sprintf("a=%g", a[cc]),
+         cex=0.8, las=2, line=0, adj=1.1, padj=-0.2)
+   points(x, y[,cc])
+   points(x, yN[,cc], col="tomato")
+   legend("topleft", col=c("black", "tomato"), pch=19,
+                     c("orignal", "transformed"), bty="n")
+ }
> title(main="Pairwise signals vs x before and after transform", outer=TRUE, line=-2)
> 
> 
> # Pairwise signals before and after transform
> layout(matrix(1:4, nrow=2, byrow=TRUE))
> par(mar=c(4,4,3,2)+0.1)
> for (rr in 3:2) {
+   ylab <- substitute(y[c], env=list(c=rr))
+   for (cc in 1:2) {
+     if (cc == rr) {
+       plot.new()
+       next
+     }
+     xlab <- substitute(y[c], env=list(c=cc))
+     plot(NA, xlim=ylim, ylim=ylim, xlab=xlab, ylab=ylab)
+     abline(a=0, b=1, lty=2)
+     points(y[,c(cc,rr)])
+     points(yN[,c(cc,rr)], col="tomato")
+     legend("topleft", col=c("black", "tomato"), pch=19,
+                       c("orignal", "transformed"), bty="n")
+   }
+ }
> title(main="Pairwise signals before and after transform", outer=TRUE, line=-2)
> 
> proc.time()
   user  system elapsed 
  1.126   0.063   1.199 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> # Simulate data from the model y <- a + bx + x^c + eps(bx)
> x <- rexp(1000)
> a <- c(2,15)
> b <- c(2,1)
> c <- c(1,2)
> bx <- outer(b,x)
> xc <- t(sapply(c, FUN=function(c) x^c))
> eps <- apply(bx, MARGIN=2, FUN=function(x) rnorm(length(x), mean=0, sd=0.1*x))
> Y <- a + bx + xc + eps
> Y <- t(Y)
> 
> lim <- c(0,70)
> plot(Y, xlim=lim, ylim=lim)
> 
> # Fit principal curve through a subset of (y_1, y_2)
> subset <- sample(nrow(Y), size=0.3*nrow(Y))
> fit <- fitXYCurve(Y[subset,], bandwidth=0.2)
> 
> lines(fit, col="red", lwd=2)
> 
> # Backtransform (y_1, y_2) keeping y_1 unchanged
> YN <- backtransformXYCurve(Y, fit=fit)
> points(YN, col="blue")
> abline(a=0, b=1, col="red", lwd=2)
> 
> proc.time()
   user  system elapsed 
  0.404   0.046   0.437 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> for (zzz in 0) {
+ 
+ # This example requires plot3d() in R.basic [http://www.braju.com/R/]
+ if (!require(pkgName <- "R.basic", character.only=TRUE)) break
+ 
+ # Simulate data from the model y <- a + bx + eps(bx)
+ x <- rexp(1000)
+ a <- c(2,15,3)
+ b <- c(2,3,4)
+ bx <- outer(b,x)
+ eps <- apply(bx, MARGIN=2, FUN=function(x) rnorm(length(x), mean=0, sd=0.1*x))
+ y <- a + bx + eps
+ y <- t(y)
+ 
+ # Add some outliers by permuting the dimensions for 1/10 of the observations
+ idx <- sample(1:nrow(y), size=1/10*nrow(y))
+ y[idx,] <- y[idx,c(2,3,1)]
+ 
+ # Plot the data with fitted lines at four different view points
+ opar <- par(mar=c(1,1,1,1)+0.1)
+ N <- 4
+ layout(matrix(1:N, nrow=2, byrow=TRUE))
+ theta <- seq(0,270,length.out=N)
+ phi <- rep(20, length.out=N)
+ xlim <- ylim <- zlim <- c(0,45)
+ persp <- list()
+ for (kk in seq_along(theta)) {
+   # Plot the data
+   persp[[kk]] <- plot3d(y, theta=theta[kk], phi=phi[kk], xlim=xlim, ylim=ylim, zlim=zlim)
+ }
+ 
+ # Weights on the observations
+ # Example a: Equal weights
+ w <- NULL
+ # Example b: More weight on the outliers (uncomment to test)
+ w <- rep(1, length(x)); w[idx] <- 0.8
+ 
+ # ...and show all iterations too with different colors.
+ maxIter <- c(seq(1,20,length.out=10),Inf)
+ col <- topo.colors(length(maxIter))
+ # Show the fitted value for every iteration
+ for (ii in seq_along(maxIter)) {
+   # Fit a line using IWPCA through data
+   fit <- iwpca(y, w=w, maxIter=maxIter[ii], swapDirections=TRUE)
+ 
+   ymid <- fit$xMean
+   d0 <- apply(y, MARGIN=2, FUN=min) - ymid
+   d1 <- apply(y, MARGIN=2, FUN=max) - ymid
+   b <- fit$vt[1,]
+   y0 <- -b * max(abs(d0))
+   y1 <-  b * max(abs(d1))
+   yline <- matrix(c(y0,y1), nrow=length(b), ncol=2)
+   yline <- yline + ymid
+ 
+   for (kk in seq_along(theta)) {
+     # Set pane to draw in
+     par(mfg=c((kk-1) %/% 2, (kk-1) %% 2) + 1)
+     # Set the viewpoint of the pane
+     options(persp.matrix=persp[[kk]])
+ 
+     # Get the first principal component
+     points3d(t(ymid), col=col[ii])
+     lines3d(t(yline), col=col[ii])
+ 
+     # Highlight the last one
+     if (ii == length(maxIter))
+       lines3d(t(yline), col="red", lwd=3)
+   }
+ }
+ 
+ par(opar)
+ 
+ } # for (zzz in 0)
Loading required package: R.basic
Warning message:
In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
  there is no package called 'R.basic'
> rm(zzz)
> 
> proc.time()
   user  system elapsed 
  0.404   0.039   0.428 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> # Define f(x)
> f <- expression(0.1*x^4 + 1*x^3 + 2*x^2 + x + 10*sin(2*x))
> 
> # Simulate data from this function in the range [a,b]
> a <- -2; b <- 5
> x <- seq(a, b, length.out=3000)
> y <- eval(f)
> 
> # Add some noise to the data
> y <- y + rnorm(length(y), 0, 10)
> 
> # Plot the function and its second derivative
> plot(x,y, type="l", lwd=4)
> 
> # Fit a cubic smoothing spline and plot it
> g <- smooth.spline(x,y, df=16)
> lines(g, col="yellow", lwd=2, lty=2)
> 
> # Calculating the (log) likelihood of the fitted spline
> l <- likelihood(g)
> 
> cat("Log likelihood with unique x values:\n")
Log likelihood with unique x values:
> print(l)
Likelihood of smoothing spline: -306239.3 
 Log base: 2.718282 
 Weighted residuals sum of square: 306239.4 
 Penalty: -0.1151156 
 Smoothing parameter lambda: 0.0009257147 
 Roughness score: 124.3532 
> 
> # Note that this is not the same as the log likelihood of the
> # data on the fitted spline iff the x values are non-unique
> x[1:5] <- x[1]  # Non-unique x values
> g <- smooth.spline(x,y, df=16)
> l <- likelihood(g)
> 
> cat("\nLog likelihood of the *spline* data set:\n")

Log likelihood of the *spline* data set:
> print(l)
Likelihood of smoothing spline: -305620.9 
 Log base: 2.718282 
 Weighted residuals sum of square: 305621 
 Penalty: -0.1151927 
 Smoothing parameter lambda: 0.0009261969 
 Roughness score: 124.3717 
> 
> # In cases with non unique x values one has to proceed as
> # below if one want to get the log likelihood for the original
> # data.
> l <- likelihood(g, x=x, y=y)
> cat("\nLog likelihood of the *original* data set:\n")

Log likelihood of the *original* data set:
> print(l)
Likelihood of smoothing spline: -306238.2 
 Log base: 2.718282 
 Weighted residuals sum of square: 306238.3 
 Penalty: -0.115193 
 Smoothing parameter lambda: 0.0009261969 
 Roughness score: 124.3721 
> 
> 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
  0.450   0.042   0.477 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> # Deaths from sport parachuting;  from ABC of EDA, p.224:
> deaths <- matrix(c(14,15,14, 7,4,7, 8,2,10, 15,9,10, 0,2,0), ncol=3, byrow=TRUE)
> rownames(deaths) <- c("1-24", "25-74", "75-199", "200++", "NA")
> colnames(deaths) <- 1973:1975
> 
> print(deaths)
       1973 1974 1975
1-24     14   15   14
25-74     7    4    7
75-199    8    2   10
200++    15    9   10
NA        0    2    0
> 
> mp <- medianPolish(deaths)
> mp1 <- medpolish(deaths, trace=FALSE)
> print(mp)

Median Polish Results (Dataset: "deaths")

Overall: 8

Row Effects:
  1-24  25-74 75-199  200++     NA 
     6     -1      0      2     -8 

Column Effects:
1973 1974 1975 
   0   -1    0 

Residuals:
       1973 1974 1975
1-24      0    2    0
25-74     0   -2    0
75-199    0   -5    2
200++     5    0    0
NA        0    3    0

> 
> ff <- c("overall", "row", "col", "residuals")
> stopifnot(all.equal(mp[ff], mp1[ff]))
> 
> # Validate decomposition:
> stopifnot(all.equal(deaths, mp$overall+outer(mp$row,mp$col,"+")+mp$resid))
> 
> proc.time()
   user  system elapsed 
  0.297   0.032   0.315 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> pathname <- system.file("data-ex", "PMT-RGData.dat", package="aroma.light")
> rg <- read.table(pathname, header=TRUE, sep="\t")
> nbrOfScans <- max(rg$slide)
> 
> rg <- as.list(rg)
> for (field in c("R", "G"))
+   rg[[field]] <- matrix(as.double(rg[[field]]), ncol=nbrOfScans)
> rg$slide <- rg$spot <- NULL
> rg <- as.matrix(as.data.frame(rg))
> colnames(rg) <- rep(c("R", "G"), each=nbrOfScans)
> 
> rgC <- rg
> 
> layout(matrix(c(1,2,0,3,4,0,5,6,7), ncol=3, byrow=TRUE))
> 
> for (channel in c("R", "G")) {
+   sidx <- which(colnames(rg) == channel)
+   channelColor <- switch(channel, R="red", G="green")
+ 
+   # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+   # The raw data
+   # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+   plotMvsAPairs(rg, channel=channel)
+   title(main=paste("Observed", channel))
+   box(col=channelColor)
+ 
+   # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+   # The calibrated data
+   # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+   rgC[,sidx] <- calibrateMultiscan(rg[,sidx], average=NULL)
+ 
+   plotMvsAPairs(rgC, channel=channel)
+   title(main=paste("Calibrated", channel))
+   box(col=channelColor)
+ } # for (channel ...)
There were 50 or more warnings (use warnings() to see the first 50)
> 
> 
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # The average calibrated data
> #
> # Note how the red signals are weaker than the green. The reason
> # for this can be that the scale factor in the green channel is
> # greater than in the red channel, but it can also be that there
> # is a remaining relative difference in bias between the green
> # and the red channel, a bias that precedes the scanning.
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> rgCA <- matrix(NA_real_, nrow=nrow(rg), ncol=2)
> colnames(rgCA) <- c("R", "G")
> for (channel in c("R", "G")) {
+   sidx <- which(colnames(rg) == channel)
+   rgCA[,channel] <- calibrateMultiscan(rg[,sidx])
+ }
> 
> plotMvsA(rgCA)
> title(main="Average calibrated")
> 
> 
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # The affine normalized average calibrated data
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # Create a matrix where the columns represent the channels
> # to be normalized.
> rgCAN <- rgCA
> # Affine normalization of channels
> rgCAN <- normalizeAffine(rgCAN)
> 
> plotMvsA(rgCAN)
> title(main="Affine normalized A.C.")
> 
> 
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # It is always ok to rescale the affine normalized data if its
> # done on (R,G); not on (A,M)! However, this is only needed for
> # esthetic purposes.
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> rgCAN <- rgCAN * 2^5
> plotMvsA(rgCAN)
> title(main="Rescaled normalized")
> 
> 
> 
> proc.time()
   user  system elapsed 
  2.391   0.131   2.570 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> # Simulate ten samples of different lengths
> N <- 10000
> X <- list()
> for (kk in 1:8) {
+   rfcn <- list(rnorm, rgamma)[[sample(2, size=1)]]
+   size <- runif(1, min=0.3, max=1)
+   a <- rgamma(1, shape=20, rate=10)
+   b <- rgamma(1, shape=10, rate=10)
+   values <- rfcn(size*N, a, b)
+ 
+   # "Censor" values
+   values[values < 0 | values > 8] <- NA_real_
+ 
+   X[[kk]] <- values
+ }
> 
> # Add 20% missing values
> X <- lapply(X, FUN=function(x) {
+   x[sample(length(x), size=0.20*length(x))] <- NA_real_
+   x
+ })
> 
> # Normalize quantiles
> Xn <- normalizeAverage(X, na.rm=TRUE, targetAvg=median(unlist(X), na.rm=TRUE))
> 
> # Plot the data
> layout(matrix(1:2, ncol=1))
> xlim <- range(X, Xn, na.rm=TRUE)
> plotDensity(X, lwd=2, xlim=xlim, main="The original distributions")
> plotDensity(Xn, lwd=2, xlim=xlim, main="The normalized distributions")
> 
> proc.time()
   user  system elapsed 
  0.409   0.065   0.459 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> # Simulate three samples with on average 20% missing values
> N <- 10000
> X <- cbind(rnorm(N, mean=3, sd=1),
+            rnorm(N, mean=4, sd=2),
+            rgamma(N, shape=2, rate=1))
> X[sample(3*N, size=0.20*3*N)] <- NA_real_
> 
> # Normalize quantiles
> Xn <- normalizeAverage(X, na.rm=TRUE, targetAvg=median(X, na.rm=TRUE))
> 
> # Plot the data
> layout(matrix(1:2, ncol=1))
> xlim <- range(X, Xn, na.rm=TRUE)
> plotDensity(X, lwd=2, xlim=xlim, main="The three original distributions")
> plotDensity(Xn, lwd=2, xlim=xlim, main="The three normalized distributions")
> 
> proc.time()
   user  system elapsed 
  0.356   0.062   0.411 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> pathname <- system.file("data-ex", "PMT-RGData.dat", package="aroma.light")
> rg <- read.table(pathname, header=TRUE, sep="\t")
> nbrOfScans <- max(rg$slide)
> 
> rg <- as.list(rg)
> for (field in c("R", "G"))
+   rg[[field]] <- matrix(as.double(rg[[field]]), ncol=nbrOfScans)
> rg$slide <- rg$spot <- NULL
> rg <- as.matrix(as.data.frame(rg))
> colnames(rg) <- rep(c("R", "G"), each=nbrOfScans)
> 
> layout(matrix(c(1,2,0,3,4,0,5,6,7), ncol=3, byrow=TRUE))
> 
> rgC <- rg
> for (channel in c("R", "G")) {
+   sidx <- which(colnames(rg) == channel)
+   channelColor <- switch(channel, R="red", G="green")
+ 
+   # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+   # The raw data
+   # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+   plotMvsAPairs(rg[,sidx])
+   title(main=paste("Observed", channel))
+   box(col=channelColor)
+ 
+   # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+   # The calibrated data
+   # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+   rgC[,sidx] <- calibrateMultiscan(rg[,sidx], average=NULL)
+ 
+   plotMvsAPairs(rgC[,sidx])
+   title(main=paste("Calibrated", channel))
+   box(col=channelColor)
+ } # for (channel ...)
> 
> 
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # The average calibrated data
> #
> # Note how the red signals are weaker than the green. The reason
> # for this can be that the scale factor in the green channel is
> # greater than in the red channel, but it can also be that there
> # is a remaining relative difference in bias between the green
> # and the red channel, a bias that precedes the scanning.
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> rgCA <- rg
> for (channel in c("R", "G")) {
+   sidx <- which(colnames(rg) == channel)
+   rgCA[,sidx] <- calibrateMultiscan(rg[,sidx])
+ }
> 
> rgCAavg <- matrix(NA_real_, nrow=nrow(rgCA), ncol=2)
> colnames(rgCAavg) <- c("R", "G")
> for (channel in c("R", "G")) {
+   sidx <- which(colnames(rg) == channel)
+   rgCAavg[,channel] <- apply(rgCA[,sidx], MARGIN=1, FUN=median, na.rm=TRUE)
+ }
> 
> # Add some "fake" outliers
> outliers <- 1:600
> rgCAavg[outliers,"G"] <- 50000
> 
> plotMvsA(rgCAavg)
> title(main="Average calibrated (AC)")
> 
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # Normalize data
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # Weight-down outliers when normalizing
> weights <- rep(1, nrow(rgCAavg))
> weights[outliers] <- 0.001
> 
> # Affine normalization of channels
> rgCANa <- normalizeAffine(rgCAavg, weights=weights)
> # It is always ok to rescale the affine normalized data if its
> # done on (R,G); not on (A,M)! However, this is only needed for
> # esthetic purposes.
> rgCANa <- rgCANa *2^1.4
> plotMvsA(rgCANa)
> title(main="Normalized AC")
> 
> # Curve-fit (lowess) normalization
> rgCANlw <- normalizeLowess(rgCAavg, weights=weights)
Warning message:
In normalizeCurveFit.matrix(X, method = "lowess", ...) :
  Weights were rounded to {0,1} since 'lowess' normalization supports only zero-one weights.
> plotMvsA(rgCANlw, col="orange", add=TRUE)
> 
> # Curve-fit (loess) normalization
> rgCANl <- normalizeLoess(rgCAavg, weights=weights)
> plotMvsA(rgCANl, col="red", add=TRUE)
> 
> # Curve-fit (robust spline) normalization
> rgCANrs <- normalizeRobustSpline(rgCAavg, weights=weights)
> plotMvsA(rgCANrs, col="blue", add=TRUE)
> 
> legend(x=0,y=16, legend=c("affine", "lowess", "loess", "r. spline"), pch=19,
+        col=c("black", "orange", "red", "blue"), ncol=2, x.intersp=0.3, bty="n")
> 
> 
> plotMvsMPairs(cbind(rgCANa, rgCANlw), col="orange", xlab=expression(M[affine]))
> title(main="Normalized AC")
> plotMvsMPairs(cbind(rgCANa, rgCANl), col="red", add=TRUE)
> plotMvsMPairs(cbind(rgCANa, rgCANrs), col="blue", add=TRUE)
> abline(a=0, b=1, lty=2)
> legend(x=-6,y=6, legend=c("lowess", "loess", "r. spline"), pch=19,
+        col=c("orange", "red", "blue"), ncol=2, x.intersp=0.3, bty="n")
> 
> 
> proc.time()
   user  system elapsed 
 10.923   0.061  10.993 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> # Simulate three shifted tracks of different lengths with same profiles
> ns <- c(A=2, B=1, C=0.25)*1000
> xx <- lapply(ns, FUN=function(n) { seq(from=1, to=max(ns), length.out=n) })
> zz <- mapply(seq_along(ns), ns, FUN=function(z,n) rep(z,n))
> 
> yy <- list(
+   A = rnorm(ns["A"], mean=0, sd=0.5),
+   B = rnorm(ns["B"], mean=5, sd=0.4),
+   C = rnorm(ns["C"], mean=-5, sd=1.1)
+ )
> yy <- lapply(yy, FUN=function(y) {
+   n <- length(y)
+   y[1:(n/2)] <- y[1:(n/2)] + 2
+   y[1:(n/4)] <- y[1:(n/4)] - 4
+   y
+ })
> 
> # Shift all tracks toward the first track
> yyN <- normalizeDifferencesToAverage(yy, baseline=1)
> 
> # The baseline channel is not changed
> stopifnot(identical(yy[[1]], yyN[[1]]))
> 
> # Get the estimated parameters
> fit <- attr(yyN, "fit")
> 
> # Plot the tracks
> layout(matrix(1:2, ncol=1))
> x <- unlist(xx)
> col <- unlist(zz)
> y <- unlist(yy)
> yN <- unlist(yyN)
> plot(x, y, col=col, ylim=c(-10,10))
> plot(x, yN, col=col, ylim=c(-10,10))
> 
> proc.time()
   user  system elapsed 
  0.426   0.047   0.459 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # Example 1: Single-enzyme fragment-length normalization of 6 arrays
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # Number samples
> I <- 9
> 
> # Number of loci
> J <- 1000
> 
> # Fragment lengths
> fl <- seq(from=100, to=1000, length.out=J)
> 
> # Simulate data points with unknown fragment lengths
> hasUnknownFL <- seq(from=1, to=J, by=50)
> fl[hasUnknownFL] <- NA_real_
> 
> # Simulate data
> y <- matrix(0, nrow=J, ncol=I)
> maxY <- 12
> for (kk in 1:I) {
+   k <- runif(n=1, min=3, max=5)
+   mu <- function(fl) {
+     mu <- rep(maxY, length(fl))
+     ok <- !is.na(fl)
+     mu[ok] <- mu[ok] - fl[ok]^{1/k}
+     mu
+   }
+   eps <- rnorm(J, mean=0, sd=1)
+   y[,kk] <- mu(fl) + eps
+ }
> 
> # Normalize data (to a zero baseline)
> yN <- apply(y, MARGIN=2, FUN=function(y) {
+   normalizeFragmentLength(y, fragmentLengths=fl, onMissing="median")
+ })
> 
> # The correction factors
> rho <- y-yN
> print(summary(rho))
       V1              V2              V3              V4       
 Min.   :7.398   Min.   :4.687   Min.   :7.953   Min.   :7.808  
 1st Qu.:7.547   1st Qu.:5.241   1st Qu.:8.049   1st Qu.:7.977  
 Median :7.813   Median :5.925   Median :8.258   Median :8.253  
 Mean   :8.006   Mean   :6.080   Mean   :8.396   Mean   :8.342  
 3rd Qu.:8.431   3rd Qu.:6.876   3rd Qu.:8.702   3rd Qu.:8.680  
 Max.   :9.181   Max.   :7.973   Max.   :9.299   Max.   :9.186  
       V5              V6              V7              V8       
 Min.   :2.999   Min.   :4.665   Min.   :3.336   Min.   :7.920  
 1st Qu.:3.889   1st Qu.:5.104   1st Qu.:4.200   1st Qu.:8.072  
 Median :4.760   Median :5.688   Median :5.015   Median :8.323  
 Mean   :4.935   Mean   :5.913   Mean   :5.166   Mean   :8.407  
 3rd Qu.:5.930   3rd Qu.:6.648   3rd Qu.:6.086   3rd Qu.:8.717  
 Max.   :7.447   Max.   :7.935   Max.   :7.504   Max.   :9.172  
       V9       
 Min.   :4.761  
 1st Qu.:5.277  
 Median :5.878  
 Mean   :6.039  
 3rd Qu.:6.760  
 Max.   :7.842  
> # The correction for units with unknown fragment lengths
> # equals the median correction factor of all other units
> print(summary(rho[hasUnknownFL,]))
       V1              V2              V3              V4              V5      
 Min.   :7.813   Min.   :5.925   Min.   :8.258   Min.   :8.253   Min.   :4.76  
 1st Qu.:7.813   1st Qu.:5.925   1st Qu.:8.258   1st Qu.:8.253   1st Qu.:4.76  
 Median :7.813   Median :5.925   Median :8.258   Median :8.253   Median :4.76  
 Mean   :7.813   Mean   :5.925   Mean   :8.258   Mean   :8.253   Mean   :4.76  
 3rd Qu.:7.813   3rd Qu.:5.925   3rd Qu.:8.258   3rd Qu.:8.253   3rd Qu.:4.76  
 Max.   :7.813   Max.   :5.925   Max.   :8.258   Max.   :8.253   Max.   :4.76  
       V6              V7              V8              V9       
 Min.   :5.688   Min.   :5.015   Min.   :8.323   Min.   :5.878  
 1st Qu.:5.688   1st Qu.:5.015   1st Qu.:8.323   1st Qu.:5.878  
 Median :5.688   Median :5.015   Median :8.323   Median :5.878  
 Mean   :5.688   Mean   :5.015   Mean   :8.323   Mean   :5.878  
 3rd Qu.:5.688   3rd Qu.:5.015   3rd Qu.:8.323   3rd Qu.:5.878  
 Max.   :5.688   Max.   :5.015   Max.   :8.323   Max.   :5.878  
> 
> # Plot raw data
> layout(matrix(1:9, ncol=3))
> xlim <- c(0,max(fl, na.rm=TRUE))
> ylim <- c(0,max(y, na.rm=TRUE))
> xlab <- "Fragment length"
> ylab <- expression(log2(theta))
> for (kk in 1:I) {
+   plot(fl, y[,kk], xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab)
+   ok <- (is.finite(fl) & is.finite(y[,kk]))
+   lines(lowess(fl[ok], y[ok,kk]), col="red", lwd=2)
+ }
> 
> # Plot normalized data
> layout(matrix(1:9, ncol=3))
> ylim <- c(-1,1)*max(y, na.rm=TRUE)/2
> for (kk in 1:I) {
+   plot(fl, yN[,kk], xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab)
+   ok <- (is.finite(fl) & is.finite(y[,kk]))
+   lines(lowess(fl[ok], yN[ok,kk]), col="blue", lwd=2)
+ }
> 
> proc.time()
   user  system elapsed 
  1.005   0.060   1.053 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # Example 2: Two-enzyme fragment-length normalization of 6 arrays
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> set.seed(0xbeef)
> 
> # Number samples
> I <- 5
> 
> # Number of loci
> J <- 3000
> 
> # Fragment lengths (two enzymes)
> fl <- matrix(0, nrow=J, ncol=2)
> fl[,1] <- seq(from=100, to=1000, length.out=J)
> fl[,2] <- seq(from=1000, to=100, length.out=J)
> 
> # Let 1/2 of the units be on both enzymes
> fl[seq(from=1, to=J, by=4),1] <- NA_real_
> fl[seq(from=2, to=J, by=4),2] <- NA_real_
> 
> # Let some have unknown fragment lengths
> hasUnknownFL <- seq(from=1, to=J, by=15)
> fl[hasUnknownFL,] <- NA_real_
> 
> # Sty/Nsp mixing proportions:
> rho <- rep(1, I)
> rho[1] <- 1/3;  # Less Sty in 1st sample
> rho[3] <- 3/2;  # More Sty in 3rd sample
> 
> 
> # Simulate data
> z <- array(0, dim=c(J,2,I))
> maxLog2Theta <- 12
> for (ii in 1:I) {
+   # Common effect for both enzymes
+   mu <- function(fl) {
+     k <- runif(n=1, min=3, max=5)
+     mu <- rep(maxLog2Theta, length(fl))
+     ok <- is.finite(fl)
+     mu[ok] <- mu[ok] - fl[ok]^{1/k}
+     mu
+   }
+ 
+   # Calculate the effect for each data point
+   for (ee in 1:2) {
+     z[,ee,ii] <- mu(fl[,ee])
+   }
+ 
+   # Update the Sty/Nsp mixing proportions
+   ee <- 2
+   z[,ee,ii] <- rho[ii]*z[,ee,ii]
+ 
+   # Add random errors
+   for (ee in 1:2) {
+     eps <- rnorm(J, mean=0, sd=1/sqrt(2))
+     z[,ee,ii] <- z[,ee,ii] + eps
+   }
+ }
> 
> 
> hasFl <- is.finite(fl)
> 
> unitSets <- list(
+   nsp  = which( hasFl[,1] & !hasFl[,2]),
+   sty  = which(!hasFl[,1] &  hasFl[,2]),
+   both = which( hasFl[,1] &  hasFl[,2]),
+   none = which(!hasFl[,1] & !hasFl[,2])
+ )
> 
> # The observed data is a mix of two enzymes
> theta <- matrix(NA_real_, nrow=J, ncol=I)
> 
> # Single-enzyme units
> for (ee in 1:2) {
+   uu <- unitSets[[ee]]
+   theta[uu,] <- 2^z[uu,ee,]
+ }
> 
> # Both-enzyme units (sum on intensity scale)
> uu <- unitSets$both
> theta[uu,] <- (2^z[uu,1,]+2^z[uu,2,])/2
> 
> # Missing units (sample from the others)
> uu <- unitSets$none
> theta[uu,] <- apply(theta, MARGIN=2, sample, size=length(uu))
> 
> # Calculate target array
> thetaT <- rowMeans(theta, na.rm=TRUE)
> targetFcns <- list()
> for (ee in 1:2) {
+   uu <- unitSets[[ee]]
+   fit <- lowess(fl[uu,ee], log2(thetaT[uu]))
+   class(fit) <- "lowess"
+   targetFcns[[ee]] <- function(fl, ...) {
+     predict(fit, newdata=fl)
+   }
+ }
> 
> 
> # Fit model only to a subset of the data
> subsetToFit <- setdiff(1:J, seq(from=1, to=J, by=10))
> 
> # Normalize data (to a target baseline)
> thetaN <- matrix(NA_real_, nrow=J, ncol=I)
> fits <- vector("list", I)
> for (ii in 1:I) {
+   lthetaNi <- normalizeFragmentLength(log2(theta[,ii]), targetFcns=targetFcns,
+                      fragmentLengths=fl, onMissing="median",
+                      subsetToFit=subsetToFit, .returnFit=TRUE)
+   fits[[ii]] <- attr(lthetaNi, "modelFit")
+   thetaN[,ii] <- 2^lthetaNi
+ }
> 
> 
> # Plot raw data
> xlim <- c(0, max(fl, na.rm=TRUE))
> ylim <- c(0, max(log2(theta), na.rm=TRUE))
> Mlim <- c(-1,1)*4
> xlab <- "Fragment length"
> ylab <- expression(log2(theta))
> Mlab <- expression(M==log[2](theta/theta[R]))
> 
> layout(matrix(1:(3*I), ncol=I, byrow=TRUE))
> for (ii in 1:I) {
+   plot(NA, xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab, main="raw")
+ 
+   # Single-enzyme units
+   for (ee in 1:2) {
+     # The raw data
+     uu <- unitSets[[ee]]
+     points(fl[uu,ee], log2(theta[uu,ii]), col=ee+1)
+   }
+ 
+   # Both-enzyme units (use fragment-length for enzyme #1)
+   uu <- unitSets$both
+   points(fl[uu,1], log2(theta[uu,ii]), col=3+1)
+ 
+   for (ee in 1:2) {
+     # The true effects
+     uu <- unitSets[[ee]]
+     lines(lowess(fl[uu,ee], log2(theta[uu,ii])), col="black", lwd=4, lty=3)
+ 
+     # The estimated effects
+     fit <- fits[[ii]][[ee]]$fit
+     lines(fit, col="orange", lwd=3)
+ 
+     muT <- targetFcns[[ee]](fl[uu,ee])
+     lines(fl[uu,ee], muT, col="cyan", lwd=1)
+   }
+ }
> 
> # Calculate log-ratios
> thetaR <- rowMeans(thetaN, na.rm=TRUE)
> M <- log2(thetaN/thetaR)
> 
> # Plot normalized data
> for (ii in 1:I) {
+   plot(NA, xlim=xlim, ylim=Mlim, xlab=xlab, ylab=Mlab, main="normalized")
+   # Single-enzyme units
+   for (ee in 1:2) {
+     # The normalized data
+     uu <- unitSets[[ee]]
+     points(fl[uu,ee], M[uu,ii], col=ee+1)
+   }
+   # Both-enzyme units (use fragment-length for enzyme #1)
+   uu <- unitSets$both
+   points(fl[uu,1], M[uu,ii], col=3+1)
+ }
> 
> ylim <- c(0,1.5)
> for (ii in 1:I) {
+   data <- list()
+   for (ee in 1:2) {
+     # The normalized data
+     uu <- unitSets[[ee]]
+     data[[ee]] <- M[uu,ii]
+   }
+   uu <- unitSets$both
+   if (length(uu) > 0)
+     data[[3]] <- M[uu,ii]
+ 
+   uu <- unitSets$none
+   if (length(uu) > 0)
+     data[[4]] <- M[uu,ii]
+ 
+   cols <- seq_along(data)+1
+   plotDensity(data, col=cols, xlim=Mlim, xlab=Mlab, main="normalized")
+ 
+   abline(v=0, lty=2)
+ }
> 
> 
> proc.time()
   user  system elapsed 
  0.921   0.074   0.983 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> # Simulate ten samples of different lengths
> N <- 10000
> X <- list()
> for (kk in 1:8) {
+   rfcn <- list(rnorm, rgamma)[[sample(2, size=1)]]
+   size <- runif(1, min=0.3, max=1)
+   a <- rgamma(1, shape=20, rate=10)
+   b <- rgamma(1, shape=10, rate=10)
+   values <- rfcn(size*N, a, b)
+ 
+   # "Censor" values
+   values[values < 0 | values > 8] <- NA_real_
+ 
+   X[[kk]] <- values
+ }
> 
> # Add 20% missing values
> X <- lapply(X, FUN=function(x) {
+   x[sample(length(x), size=0.20*length(x))] <- NA_real_
+   x
+ })
> 
> # Normalize quantiles
> Xn <- normalizeQuantile(X)
> 
> # Plot the data
> layout(matrix(1:2, ncol=1))
> xlim <- range(X, na.rm=TRUE)
> plotDensity(X, lwd=2, xlim=xlim, main="The original distributions")
> plotDensity(Xn, lwd=2, xlim=xlim, main="The normalized distributions")
> 
> proc.time()
   user  system elapsed 
  0.431   0.069   0.486 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> # Simulate three samples with on average 20% missing values
> N <- 10000
> X <- cbind(rnorm(N, mean=3, sd=1),
+            rnorm(N, mean=4, sd=2),
+            rgamma(N, shape=2, rate=1))
> X[sample(3*N, size=0.20*3*N)] <- NA_real_
> 
> # Normalize quantiles
> Xn <- normalizeQuantile(X)
> 
> # Plot the data
> layout(matrix(1:2, ncol=1))
> xlim <- range(X, Xn, na.rm=TRUE)
> plotDensity(X, lwd=2, xlim=xlim, main="The three original distributions")
> plotDensity(Xn, lwd=2, xlim=xlim, main="The three normalized distributions")
> 
> proc.time()
   user  system elapsed 
  0.376   0.049   0.413 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> # Simulate three samples with on average 20% missing values
> N <- 10000
> X <- cbind(rnorm(N, mean=3, sd=1),
+            rnorm(N, mean=4, sd=2),
+            rgamma(N, shape=2, rate=1))
> X[sample(3*N, size=0.20*3*N)] <- NA_real_
> 
> # Plot the data
> layout(matrix(c(1,0,2:5), ncol=2, byrow=TRUE))
> xlim <- range(X, na.rm=TRUE)
> plotDensity(X, lwd=2, xlim=xlim, main="The three original distributions")
> 
> Xn <- normalizeQuantile(X)
> plotDensity(Xn, lwd=2, xlim=xlim, main="The three normalized distributions")
> plotXYCurve(X, Xn, xlim=xlim, main="The three normalized distributions")
> 
> Xn2 <- normalizeQuantileSpline(X, xTarget=Xn[,1], spar=0.99)
> plotDensity(Xn2, lwd=2, xlim=xlim, main="The three normalized distributions")
> plotXYCurve(X, Xn2, xlim=xlim, main="The three normalized distributions")
> 
> proc.time()
   user  system elapsed 
  1.421   0.081   1.491 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> library("R.utils")
Loading required package: R.oo
Loading required package: R.methodsS3
R.methodsS3 v1.8.2 (2022-06-13 22:00:14 UTC) successfully loaded. See ?R.methodsS3 for help.
R.oo v1.27.1 (2025-05-02 21:00:05 UTC) successfully loaded. See ?R.oo for help.

Attaching package: 'R.oo'

The following object is masked from 'package:R.methodsS3':

    throw

The following objects are masked from 'package:methods':

    getClasses, getMethods

The following objects are masked from 'package:base':

    attach, detach, load, save

R.utils v2.13.0 (2025-02-24 21:20:02 UTC) successfully loaded. See ?R.utils for help.

Attaching package: 'R.utils'

The following object is masked from 'package:utils':

    timestamp

The following objects are masked from 'package:base':

    cat, commandArgs, getOption, isOpen, nullfile, parse, use, warnings

> 
> # Load data
> pathname <- system.file("data-ex/TumorBoost,fracB,exampleData.Rbin", package="aroma.light")
> data <- loadObject(pathname)
> 
> # Drop loci with missing values
> data <- na.omit(data)
> 
> attachLocally(data)
> pos <- position/1e6
> 
> # Call naive genotypes
> muN <- callNaiveGenotypes(betaN)
> 
> # Genotype classes
> isAA <- (muN == 0)
> isAB <- (muN == 1/2)
> isBB <- (muN == 1)
> 
> # Sanity checks
> stopifnot(all(muN[isAA] == 0))
> stopifnot(all(muN[isAB] == 1/2))
> stopifnot(all(muN[isBB] == 1))
> 
> # TumorBoost normalization with different flavors
> betaTNs <- list()
> for (flavor in c("v1", "v2", "v3", "v4")) {
+   betaTN <- normalizeTumorBoost(betaT=betaT, betaN=betaN, preserveScale=FALSE, flavor=flavor)
+ 
+   # Assert that no non-finite values are introduced
+   stopifnot(all(is.finite(betaTN)))
+ 
+   # Assert that nothing is flipped
+   stopifnot(all(betaTN[isAA] < 1/2))
+   stopifnot(all(betaTN[isBB] > 1/2))
+ 
+   betaTNs[[flavor]] <- betaTN
+ }
> 
> # Plot
> layout(matrix(1:4, ncol=1))
> par(mar=c(2.5,4,0.5,1)+0.1)
> ylim <- c(-0.05, 1.05)
> col <- rep("#999999", length(muN))
> col[muN == 1/2] <- "#000000"
> for (flavor in names(betaTNs)) {
+   betaTN <- betaTNs[[flavor]]
+   ylab <- sprintf("betaTN[%s]", flavor)
+   plot(pos, betaTN, col=col, ylim=ylim, ylab=ylab)
+ }
> 
> proc.time()
   user  system elapsed 
  0.633   0.045   0.664 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> library("R.utils")
Loading required package: R.oo
Loading required package: R.methodsS3
R.methodsS3 v1.8.2 (2022-06-13 22:00:14 UTC) successfully loaded. See ?R.methodsS3 for help.
R.oo v1.27.1 (2025-05-02 21:00:05 UTC) successfully loaded. See ?R.oo for help.

Attaching package: 'R.oo'

The following object is masked from 'package:R.methodsS3':

    throw

The following objects are masked from 'package:methods':

    getClasses, getMethods

The following objects are masked from 'package:base':

    attach, detach, load, save

R.utils v2.13.0 (2025-02-24 21:20:02 UTC) successfully loaded. See ?R.utils for help.

Attaching package: 'R.utils'

The following object is masked from 'package:utils':

    timestamp

The following objects are masked from 'package:base':

    cat, commandArgs, getOption, isOpen, nullfile, parse, use, warnings

> 
> # Load data
> pathname <- system.file("data-ex/TumorBoost,fracB,exampleData.Rbin", package="aroma.light")
> data <- loadObject(pathname)
> attachLocally(data)
> pos <- position/1e6
> muN <- genotypeN
> 
> layout(matrix(1:4, ncol=1))
> par(mar=c(2.5,4,0.5,1)+0.1)
> ylim <- c(-0.05, 1.05)
> col <- rep("#999999", length(muN))
> col[muN == 1/2] <- "#000000"
> 
> # Allele B fractions for the normal sample
> plot(pos, betaN, col=col, ylim=ylim)
> 
> # Allele B fractions for the tumor sample
> plot(pos, betaT, col=col, ylim=ylim)
> 
> # TumorBoost w/ naive genotype calls
> betaTN <- normalizeTumorBoost(betaT=betaT, betaN=betaN, preserveScale=FALSE)
> plot(pos, betaTN, col=col, ylim=ylim)
> 
> # TumorBoost w/ external multi-sample genotype calls
> betaTNx <- normalizeTumorBoost(betaT=betaT, betaN=betaN, muN=muN, preserveScale=FALSE)
> plot(pos, betaTNx, col=col, ylim=ylim)
> 
> proc.time()
   user  system elapsed 
  0.524   0.072   0.584 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> data(cars)
> attach(cars)
> plot(speed, dist, main = "data(cars)  &  robust smoothing splines")
> 
> # Fit a smoothing spline using L_2 norm
> cars.spl <- smooth.spline(speed, dist)
> lines(cars.spl, col = "blue")
> 
> # Fit a smoothing spline using L_1 norm
> cars.rspl <- robustSmoothSpline(speed, dist)
> lines(cars.rspl, col = "red")
> 
> # Fit a smoothing spline using L_2 norm with 10 degrees of freedom
> lines(smooth.spline(speed, dist, df=10), lty=2, col = "blue")
> 
> # Fit a smoothing spline using L_1 norm with 10 degrees of freedom
> lines(robustSmoothSpline(speed, dist, df=10), lty=2, col = "red")
> 
> # Fit a smoothing spline using Tukey's biweight norm
> cars.rspl <- robustSmoothSpline(speed, dist, method = "symmetric")
> lines(cars.rspl, col = "purple")
> 
> legend(5,120, c(
+       paste("smooth.spline [C.V.] => df =",round(cars.spl$df,1)),
+       paste("robustSmoothSpline L1 [C.V.] => df =",round(cars.rspl$df,1)),
+       paste("robustSmoothSpline symmetric [C.V.] => df =",round(cars.rspl$df,1)),
+       "standard with s( * , df = 10)", "robust with s( * , df = 10)"
+     ),
+     col = c("blue","red","purple","blue","red"), lty = c(1,1,1,2,2),
+     bg='bisque')
> 
> proc.time()
   user  system elapsed 
  0.427   0.040   0.454 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> X <- matrix(1:30, nrow=5L, ncol=6L)
> mu <- rowMeans(X)
> sd <- apply(X, MARGIN=1L, FUN=sd)
> 
> y <- rowAverages(X)
> stopifnot(all(y == mu))
> stopifnot(all(attr(y,"deviance") == sd))
> stopifnot(all(attr(y,"df") == ncol(X)))
> 
> proc.time()
   user  system elapsed 
  0.292   0.023   0.301 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> # Simulate 20000 genes with 10 observations each
> X <- matrix(rnorm(n=20000), ncol=10)
> 
> # Calculate the correlation for 5000 random gene pairs
> cor <- sampleCorrelations(X, npairs=5000)
> print(summary(cor))
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
-0.915628 -0.241670  0.002259  0.003743  0.242345  0.903145 
> 
> 
> proc.time()
   user  system elapsed 
  0.640   0.041   0.670 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> pairs <- sampleTuples(1:10, size=5, length=2)
> print(pairs)
     [,1] [,2]
[1,]    3   10
[2,]    7    3
[3,]    7    2
[4,]    9    7
[5,]    9    2
> 
> triples <- sampleTuples(1:10, size=5, length=3)
> print(triples)
     [,1] [,2] [,3]
[1,]    1    9    4
[2,]    3    9    1
[3,]    2    4   10
[4,]    5    3    6
[5,]    5    2    4
> 
> # Allow tuples with repeated elements
> quadruples <- sampleTuples(1:3, size=5, length=4, replace=TRUE)
> print(quadruples)
     [,1] [,2] [,3] [,4]
[1,]    2    3    1    1
[2,]    3    1    1    1
[3,]    3    1    2    1
[4,]    2    1    2    1
[5,]    1    3    2    3
> 
> proc.time()
   user  system elapsed 
  0.281   0.037   0.304 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> for (zzz in 0) {
+ 
+ # This example requires plot3d() in R.basic [http://www.braju.com/R/]
+ if (!require(pkgName <- "R.basic", character.only=TRUE)) break
+ 
+ # -------------------------------------------------------------
+ # A first example
+ # -------------------------------------------------------------
+ # Simulate data from the model y <- a + bx + eps(bx)
+ x <- rexp(1000)
+ a <- c(2,15,3)
+ b <- c(2,3,15)
+ bx <- outer(b,x)
+ eps <- apply(bx, MARGIN=2, FUN=function(x) rnorm(length(x), mean=0, sd=0.1*x))
+ y <- a + bx + eps
+ y <- t(y)
+ 
+ # Add some outliers by permuting the dimensions for 1/3 of the observations
+ idx <- sample(1:nrow(y), size=1/3*nrow(y))
+ y[idx,] <- y[idx,c(2,3,1)]
+ 
+ # Down-weight the outliers W times to demonstrate how weights are used
+ W <- 10
+ 
+ # Plot the data with fitted lines at four different view points
+ N <- 4
+ theta <- seq(0,180,length.out=N)
+ phi <- rep(30, length.out=N)
+ 
+ # Use a different color for each set of weights
+ col <- topo.colors(W)
+ 
+ opar <- par(mar=c(1,1,1,1)+0.1)
+ layout(matrix(1:N, nrow=2, byrow=TRUE))
+ for (kk in seq(theta)) {
+   # Plot the data
+   plot3d(y, theta=theta[kk], phi=phi[kk])
+ 
+   # First, same weights for all observations
+   w <- rep(1, length=nrow(y))
+ 
+   for (ww in 1:W) {
+     # Fit a line using IWPCA through data
+     fit <- wpca(y, w=w, swapDirections=TRUE)
+ 
+     # Get the first principal component
+     ymid <- fit$xMean
+     d0 <- apply(y, MARGIN=2, FUN=min) - ymid
+     d1 <- apply(y, MARGIN=2, FUN=max) - ymid
+     b <- fit$vt[1,]
+     y0 <- -b * max(abs(d0))
+     y1 <-  b * max(abs(d1))
+     yline <- matrix(c(y0,y1), nrow=length(b), ncol=2)
+     yline <- yline + ymid
+ 
+     points3d(t(ymid), col=col)
+     lines3d(t(yline), col=col)
+ 
+     # Down-weight outliers only, because here we know which they are.
+     w[idx] <- w[idx]/2
+   }
+ 
+   # Highlight the last one
+   lines3d(t(yline), col="red", lwd=3)
+ }
+ 
+ par(opar)
+ 
+ } # for (zzz in 0)
Loading required package: R.basic
Warning message:
In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
  there is no package called 'R.basic'
> rm(zzz)
> 
> proc.time()
   user  system elapsed 
  0.378   0.031   0.395 

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

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.

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> library("aroma.light")
aroma.light v3.40.0 (2026-01-12) successfully loaded. See ?aroma.light for help.
> 
> # -------------------------------------------------------------
> # A second example
> # -------------------------------------------------------------
> # Data
> x <- c(1,2,3,4,5)
> y <- c(2,4,3,3,6)
> 
> opar <- par(bty="L")
> opalette <- palette(c("blue", "red", "black"))
> xlim <- ylim <- c(0,6)
> 
> # Plot the data and the center mass
> plot(x,y, pch=16, cex=1.5, xlim=xlim, ylim=ylim)
> points(mean(x), mean(y), cex=2, lwd=2, col="blue")
> 
> 
> # Linear regression y ~ x
> fit <- lm(y ~ x)
> abline(fit, lty=1, col=1)
> 
> # Linear regression y ~ x through without intercept
> fit <- lm(y ~ x - 1)
> abline(fit, lty=2, col=1)
> 
> 
> # Linear regression x ~ y
> fit <- lm(x ~ y)
> c <- coefficients(fit)
> b <- 1/c[2]
> a <- -b*c[1]
> abline(a=a, b=b, lty=1, col=2)
> 
> # Linear regression x ~ y through without intercept
> fit <- lm(x ~ y - 1)
> b <- 1/coefficients(fit)
> abline(a=0, b=b, lty=2, col=2)
> 
> 
> # Orthogonal linear "regression"
> fit <- wpca(cbind(x,y))
> 
> b <- fit$vt[1,2]/fit$vt[1,1]
> a <- fit$xMean[2]-b*fit$xMean[1]
> abline(a=a, b=b, lwd=2, col=3)
> 
> # Orthogonal linear "regression" without intercept
> fit <- wpca(cbind(x,y), center=FALSE)
> b <- fit$vt[1,2]/fit$vt[1,1]
> a <- fit$xMean[2]-b*fit$xMean[1]
> abline(a=a, b=b, lty=2, lwd=2, col=3)
> 
> legend(xlim[1],ylim[2], legend=c("lm(y~x)", "lm(y~x-1)", "lm(x~y)",
+           "lm(x~y-1)", "pca", "pca w/o intercept"), lty=rep(1:2,3),
+                      lwd=rep(c(1,1,2),each=2), col=rep(1:3,each=2))
> 
> palette(opalette)
> par(opar)
> 
> proc.time()
   user  system elapsed 
  0.331   0.046   0.363