Resolved conflicts involving homonymous functions

NAMESPACE

@@ -36,7 +36,6 @@ export(takeLine)
 export(testaaOnkoKunnollinenBapsData)
 export(testaaPop)
 export(writeMixtureInfo)
-import(utils)
 importFrom(Rsamtools,scanBam)
 importFrom(adegenet,.readExt)
 importFrom(adegenet,read.genepop)
@@ -45,6 +44,7 @@ importFrom(ape,read.FASTA)
 importFrom(matlab2r,blanks)
 importFrom(matlab2r,cell)
 importFrom(matlab2r,colon)
+importFrom(matlab2r,find)
 importFrom(matlab2r,inputdlg)
 importFrom(matlab2r,isempty)
 importFrom(matlab2r,isfield)

R/addToSummary.R

@@ -4,7 +4,7 @@ addToSummary <- function(logml, partitionSummary, worstIndex) {
   # annettua logml arvoa, niin lis<69>t<EFBFBD><74>n worstIndex:in kohtaan uusi logml ja
   # nykyist<73> partitiota vastaava nclusters:in arvo. Muutoin ei tehd<68> mit<69><74>n.
 
-  apu <- find(abs(partitionSummary[, 2] - logml) < 1e-5)
+  apu <- matlab2r::find(abs(partitionSummary[, 2] - logml) < 1e-5)
   if (isempty(apu)) {
     # Nyt l<>ydetty partitio ei ole viel<65> kirjattuna summaryyn.
     npops <- length(unique(PARTITION))

R/admix1.R

@@ -130,8 +130,8 @@ admix1 <- function(tietue) {
           osuusTaulu[q] <- 1
           arvot[q] <- computeIndLogml(omaFreqs, osuusTaulu)
         }
-        iso_arvo <- max(arvot)
+        iso_arvo <- base::max(arvot)
-        isoimman_indeksi <- match(max(arvot), arvot)
+        isoimman_indeksi <- match(base::max(arvot), arvot)
         osuusTaulu <- zeros(1, npops)
         osuusTaulu[isoimman_indeksi] <- 1
         PARTITION[ind] <- isoimman_indeksi
@@ -149,7 +149,7 @@ admix1 <- function(tietue) {
   }
 
   # Analyze further only individuals who have log-likelihood ratio larger than 3:
-  to_investigate <- t(find(likelihood > 3))
+  to_investigate <- t(matlab2r::find(likelihood > 3))
   cat("Possibly admixed individuals:\n")
   for (i in 1:length(to_investigate)) {
     cat(as.character(to_investigate[i]))
@@ -200,8 +200,8 @@ admix1 <- function(tietue) {
             osuusTaulu[q] <- 1
             arvot[q] <- computeIndLogml(omaFreqs, osuusTaulu)
           }
-          iso_arvo <- max(arvot)
+          iso_arvo <- base::max(arvot)
-          isoimman_indeksi <- match(max(arvot), arvot)
+          isoimman_indeksi <- match(base::max(arvot), arvot)
           osuusTaulu <- zeros(1, npops)
           osuusTaulu[isoimman_indeksi] <- 1
           PARTITION[ind] <- isoimman_indeksi
@@ -233,13 +233,13 @@ admix1 <- function(tietue) {
   missing_levels <- zeros(npops, 3) # the mean values for different levels.
   missing_level_partition <- zeros(ninds, 1) # level of each individual (one of the levels of its population).
   for (i in 1:npops) {
-    inds <- find(PARTITION == i)
+    inds <- matlab2r::find(PARTITION == i)
     # Proportions of non-missing data for the individuals:
     non_missing_data <- zeros(length(inds), 1)
     for (j in 1:length(inds)) {
       ind <- inds[j]
       non_missing_data[j] <- length(
-        find(data[(ind - 1) * rowsFromInd + 1:ind * rowsFromInd, ] > 0)
+        matlab2r::find(data[(ind - 1) * rowsFromInd + 1:ind * rowsFromInd, ] > 0)
       ) / (rowsFromInd * nloci)
     }
     if (all(non_missing_data > 0.9)) {
@@ -258,7 +258,7 @@ admix1 <- function(tietue) {
       n_levels <- length(unique(part))
       n_missing_levels[i] <- n_levels
       for (j in 1:n_levels) {
-        missing_levels[i, j] <- mean(non_missing_data[find(part == j)])
+        missing_levels[i, j] <- mean(non_missing_data[matlab2r::find(part == j)])
       }
     }
   }
@@ -269,7 +269,7 @@ admix1 <- function(tietue) {
   for (pop in t(admix_populaatiot)) {
     for (level in 1:n_missing_levels[pop]) {
       potential_inds_in_this_pop_and_level <-
-        find(
+        matlab2r::find(
           PARTITION == pop & missing_level_partition == level &
             likelihood > 3
         ) # Potential admix individuals here.
@@ -338,8 +338,8 @@ admix1 <- function(tietue) {
       # In case of a rounding error, the sum is made equal to unity by
       # fixing the largest value.
       if ((PARTITION[ind] > 0) & (sum(proportionsIt[ind, ]) != 1)) {
-        isoin <- max(proportionsIt[ind, ])
+        isoin <- base::max(proportionsIt[ind, ])
-        indeksi <- match(isoin, max(proportionsIt[ind, ]))
+        indeksi <- match(isoin, base::max(proportionsIt[ind, ]))
         erotus <- sum(proportionsIt[ind, ]) - 1
         proportionsIt[ind, indeksi] <- isoin - erotus
       }
@@ -352,7 +352,7 @@ admix1 <- function(tietue) {
     pop <- PARTITION[ind]
     if (pop == 0) { # Individual is outlier
       uskottavuus[ind] <- 1
-    } else if (isempty(find(to_investigate == ind))) {
+    } else if (isempty(matlab2r::find(to_investigate == ind))) {
       # Individual had log-likelihood ratio<3
       uskottavuus[ind] <- 1
     } else {

R/admixture_initialization.R

@@ -6,12 +6,12 @@
 admixture_initialization <- function(data_matrix, nclusters, Z) {
   size_data <- size(data_matrix)
   nloci <- size_data[2] - 1
-  n <- max(data_matrix[, ncol(data_matrix)])
+  n <- base::max(data_matrix[, ncol(data_matrix)])
   T <- cluster_own(Z, nclusters)
   initial_partition <- zeros(size_data[1], 1)
   for (i in 1:n) {
     kori <- T[i]
-    here <- find(data_matrix[, ncol(data_matrix)] == i)
+    here <- matlab2r::find(data_matrix[, ncol(data_matrix)] == i)
     for (j in 1:length(here)) {
       initial_partition[here[j], 1] <- kori
     }

R/arvoSeuraavaTi.R

@@ -2,7 +2,7 @@ arvoSeuraavaTila <- function(muutokset, logml) {
   # Suorittaa yksil<69>n seuraavan tilan arvonnan
 
   y <- logml + muutokset # siirron j<>lkeiset logml:t
-  y <- y - max(y)
+  y <- y - base::max(y)
   y <- exp(y)
   summa <- sum(y)
   y <- y / summa

R/computeDiffInCounts.R

@@ -6,7 +6,7 @@ computeDiffInCounts <- function(rows, max_noalle, nloci, data) {
   diffInCounts <- zeros(max_noalle, nloci)
   for (i in seq_len(nrow(data))) {
     row <- data[i, ]
-    notEmpty <- as.matrix(find(row >= 0))
+    notEmpty <- as.matrix(matlab2r::find(row >= 0))
 
     if (length(notEmpty) > 0) {
       diffInCounts[row(notEmpty) + (notEmpty - 1) * max_noalle] <-

R/computeLogml.R

@@ -1,10 +1,10 @@
 computeLogml <- function(counts, sumcounts, noalle, data, rowsFromInd) {
   nloci <- size(counts, 2)
   npops <- size(counts, 3)
-  adjnoalle <- zeros(max(noalle), nloci)
+  adjnoalle <- zeros(base::max(noalle), nloci)
   for (j in 1:nloci) {
     adjnoalle[1:noalle[j], j] <- noalle(j)
-    if ((noalle(j) < max(noalle))) {
+    if ((noalle(j) < base::max(noalle))) {
       adjnoalle[noalle[j] + 1:ncol(adjnoalle), j] <- 1
     }
   }

R/etsiParas.R

@@ -10,13 +10,13 @@ etsiParas <- function(osuus, osuusTaulu, omaFreqs, logml) {
   while (ready != 1) {
     muutokset <- laskeMuutokset4(osuus, osuusTaulu, omaFreqs, logml)
 
-    # Work around R's max() limitation on complex numbers
+    # Work around R's base::max() limitation on complex numbers
     if (any(sapply(muutokset, class) == "complex")) {
-      maxRe <- max(Re(as.vector(muutokset)))
+      maxRe <- base::max(Re(as.vector(muutokset)))
-      maxIm <- max(Im(as.vector(muutokset)))
+      maxIm <- base::max(Im(as.vector(muutokset)))
       maxMuutos <- complex(real = maxRe, imaginary = maxIm)
     } else {
-      maxMuutos <- max(as.vector(muutokset))
+      maxMuutos <- base::max(as.vector(muutokset))
     }
     indeksi <- which(muutokset == maxMuutos)
     if (Re(maxMuutos) > 0) {

R/findEmptyPop.R

@@ -6,7 +6,7 @@ findEmptyPop <- function(npops) {
     emptyPop <- -1
   } else {
     popDiff <- diff(c(0, pops, npops + 1))
-    emptyPop <- min(find(popDiff > 1))
+    emptyPop <- base::min(matlab2r::find(popDiff > 1))
   }
   return(list(emptyPop = emptyPop, pops = pops))
 }

R/getDistances.R

@@ -9,26 +9,26 @@ getDistances <- function(data_matrix, nclusters) {
 
   size_data <- size(data_matrix)
   nloci <- size_data[2] - 1
-  n <- max(data_matrix[, ncol(data_matrix)])
+  n <- base::max(data_matrix[, ncol(data_matrix)])
   distances <- zeros(choose(n, 2), 1)
   pointer <- 1
   for (i in 1:n - 1) {
     i_data <- data_matrix[
-      find(data_matrix[, ncol(data_matrix)] == i),
+      matlab2r::find(data_matrix[, ncol(data_matrix)] == i),
       1:nloci
     ]
     for (j in (i + 1):n) {
       d_ij <- 0
-      j_data <- data_matrix[find(data_matrix[, ncol()] == j), 1:nloci]
+      j_data <- data_matrix[matlab2r::find(data_matrix[, ncol()] == j), 1:nloci]
       vertailuja <- 0
       for (k in 1:size(i_data, 1)) {
         for (l in 1:size(j_data, 1)) {
-          here_i <- find(i_data[k, ] >= 0)
+          here_i <- matlab2r::find(i_data[k, ] >= 0)
-          here_j <- find(j_data[l, ] >= 0)
+          here_j <- matlab2r::find(j_data[l, ] >= 0)
           here_joint <- intersect(here_i, here_j)
           vertailuja <- vertailuja + length(here_joint)
           d_ij <- d_ij + length(
-            find(i_data[k, here_joint] != j_data[l, here_joint])
+            matlab2r::find(i_data[k, here_joint] != j_data[l, here_joint])
           )
         }
       }

R/globals.R

@@ -5,8 +5,6 @@ POP_LOGML <- array(1, dim = 100)
 LOGDIFF <- array(1, dim = c(100, 100))
 # If handling globas break, try other ideas from https://stackoverflow.com/a/65252740/1169233
 
-
-#' @import utils
 utils::globalVariables(
   c("PARTITION", "COUNTS", "SUMCOUNTS", "LOGDIFF", "POP_LOGML", "GAMMA_LN")
 )

R/handleData.R

@@ -24,9 +24,9 @@ handleData <- function(raw_data) {
   nloci <- size(raw_data, 2) - 1
 
   dataApu <- data[, 1:nloci]
-  nollat <- find(dataApu == 0)
+  nollat <- matlab2r::find(dataApu == 0)
   if (!isempty(nollat)) {
-    isoinAlleeli <- max(max(dataApu))
+    isoinAlleeli <- base::max(max(dataApu))
     dataApu[nollat] <- isoinAlleeli + 1
     data[, 1:nloci] <- dataApu
   }
@@ -39,16 +39,16 @@ handleData <- function(raw_data) {
   for (i in 1:nloci) {
     alleelitLokuksessaI <- unique(data[, i])
     alleelitLokuksessa[[i]] <- sort(alleelitLokuksessaI[
-      find(
+      matlab2r::find(
         alleelitLokuksessaI >= 0
       )
     ])
     noalle[i] <- length(alleelitLokuksessa[[i]])
   }
-  alleleCodes <- zeros(max(noalle), nloci)
+  alleleCodes <- zeros(base::max(noalle), nloci)
   for (i in 1:nloci) {
     alleelitLokuksessaI <- alleelitLokuksessa[[i]]
-    puuttuvia <- max(noalle) - length(alleelitLokuksessaI)
+    puuttuvia <- base::max(noalle) - length(alleelitLokuksessaI)
     alleleCodes[, i] <- as.matrix(
       c(alleelitLokuksessaI, zeros(puuttuvia, 1))
     )
@@ -56,21 +56,21 @@ handleData <- function(raw_data) {
 
   for (loc in seq_len(nloci)) {
     for (all in seq_len(noalle[loc])) {
-      data[find(data[, loc] == alleleCodes[all, loc]), loc] <- all
+      data[matlab2r::find(data[, loc] == alleleCodes[all, loc]), loc] <- all
     }
   }
 
-  nind <- max(data[, ncol(data)])
+  nind <- base::max(data[, ncol(data)])
   nrows <- size(data, 1)
   ncols <- size(data, 2)
   rowsFromInd <- zeros(nind, 1)
   for (i in 1:nind) {
-    rowsFromInd[i] <- length(find(data[, ncol(data)] == i))
+    rowsFromInd[i] <- length(matlab2r::find(data[, ncol(data)] == i))
   }
-  maxRowsFromInd <- max(rowsFromInd)
+  maxRowsFromInd <- base::max(rowsFromInd)
   a <- -999
   emptyRow <- repmat(a, c(1, ncols))
-  lessThanMax <- find(rowsFromInd < maxRowsFromInd)
+  lessThanMax <- matlab2r::find(rowsFromInd < maxRowsFromInd)
   missingRows <- maxRowsFromInd * nind - nrows
   data <- rbind(data, zeros(missingRows, ncols))
   pointer <- 1
@@ -81,12 +81,12 @@ handleData <- function(raw_data) {
   newData <- data
   rowsFromInd <- maxRowsFromInd
 
-  adjprior <- zeros(max(noalle), nloci)
+  adjprior <- zeros(base::max(noalle), nloci)
   priorTerm <- 0
   for (j in 1:nloci) {
     adjprior[, j] <- as.matrix(c(
       repmat(1 / noalle[j], c(noalle[j], 1)),
-      ones(max(noalle) - noalle[j], 1)
+      ones(base::max(noalle) - noalle[j], 1)
     ))
     priorTerm <- priorTerm + noalle[j] * lgamma(1 / noalle[j])
   }

R/indMix.R

@@ -48,7 +48,7 @@ indMix <- function(c, npops, dispText = TRUE) {
       return()
     } else {
       npopsTaulu <- as.numeric(npopstext)
-      ykkoset <- find(npopsTaulu == 1)
+      ykkoset <- matlab2r::find(npopsTaulu == 1)
       npopsTaulu[ykkoset] <- NA # Mik<69>li ykk<6B>si<73> annettu yl<79>rajaksi, ne poistetaan (if ones are given as an upper limit, they are deleted)
       if (isempty(npopsTaulu)) {
         logml <- 1
@@ -233,13 +233,13 @@ indMix <- function(c, npops, dispText = TRUE) {
           maxMuutos <- 0
           ninds <- size(rows, 1)
           for (pop in 1:npops) {
-            inds2 <- find(PARTITION == pop)
+            inds2 <- matlab2r::find(PARTITION == pop)
             ninds2 <- length(inds2)
             if (ninds2 > 2) {
               dist2 <- laskeOsaDist(inds2, dist, ninds)
               Z2 <- linkage(t(dist2))
               if (round == 3) {
-                npops2 <- max(min(20, floor(ninds2 / 5)), 2)
+                npops2 <- base::max(base::min(20, floor(ninds2 / 5)), 2)
               } else if (round == 4) {
                 npops2 <- 2 # Moneenko osaan jaetaan
               }
@@ -253,7 +253,7 @@ indMix <- function(c, npops, dispText = TRUE) {
                 maxMuutos <- isoin
                 muuttuvaPop2 <- indeksi %% npops2
                 if (muuttuvaPop2 == 0) muuttuvaPop2 <- npops2
-                muuttuvat <- inds2[find(T2 == muuttuvaPop2)]
+                muuttuvat <- inds2[matlab2r::find(T2 == muuttuvaPop2)]
                 i2 <- ceiling(indeksi / npops2)
               }
             }
@@ -310,7 +310,7 @@ indMix <- function(c, npops, dispText = TRUE) {
             j <- j + 1
             pop <- pops[j]
             totalMuutos <- 0
-            inds <- find(PARTITION == pop)
+            inds <- matlab2r::find(PARTITION == pop)
             if (round == 5) {
               aputaulu <- c(inds, rand(length(inds), 1))
               aputaulu <- sortrows(aputaulu, 2)
@@ -398,7 +398,7 @@ indMix <- function(c, npops, dispText = TRUE) {
           while (j < npops) {
             j <- j + 1
             pop <- pops[j]
-            inds2 <- find(PARTITION == pop)
+            inds2 <- matlab2r::find(PARTITION == pop)
             ninds2 <- length(inds2)
             if (ninds2 > 5) {
               partition <- PARTITION
@@ -410,7 +410,7 @@ indMix <- function(c, npops, dispText = TRUE) {
               dist2 <- laskeOsaDist(inds2, dist, ninds)
               Z2 <- linkage(t(dist2))
               T2 <- cluster_own(Z2, 2)
-              muuttuvat <- inds2[find(T2 == 1)]
+              muuttuvat <- inds2[matlab2r::find(T2 == 1)]
 
               muutokset <- laskeMuutokset3(
                 T2, inds2, rows, data, adjprior, priorTerm, pop

R/initialCounts.R

@@ -3,18 +3,18 @@ initialCounts <- function(partition, data, npops, rows, noalle, adjprior) {
   ninds <- size(rows, 1)
 
   koot <- rows[, 1] - rows[, 2] + 1
-  maxSize <- max(koot)
+  maxSize <- base::max(koot)
 
-  counts <- zeros(max(noalle), nloci, npops)
+  counts <- zeros(base::max(noalle), nloci, npops)
   sumcounts <- zeros(npops, nloci)
   for (i in 1:npops) {
     for (j in 1:nloci) {
-      havainnotLokuksessa <- find(partition == i & data[, j] >= 0)
+      havainnotLokuksessa <- matlab2r::find(partition == i & data[, j] >= 0)
       sumcounts[i, j] <- length(havainnotLokuksessa)
       for (k in 1:noalle[j]) {
         alleleCode <- k
         N_ijk <- length(
-          find(data[havainnotLokuksessa, j] == alleleCode)
+          matlab2r::find(data[havainnotLokuksessa, j] == alleleCode)
         )
         counts[k, j, i] <- N_ijk
       }

R/initialPopCounts.R

@@ -1,16 +1,16 @@
 initialPopCounts <- function(data, npops, rows, noalle, adjprior) {
   nloci <- size(data, 2)
-  counts <- zeros(max(noalle), nloci, npops)
+  counts <- zeros(base::max(noalle), nloci, npops)
   sumcounts <- zeros(npops, nloci)
 
   for (i in 1:npops) {
     for (j in 1:nloci) {
       i_rivit <- rows(i, 1):rows(i, 2)
-      havainnotLokuksessa <- find(data[i_rivit, j] >= 0)
+      havainnotLokuksessa <- matlab2r::find(data[i_rivit, j] >= 0)
       sumcounts[i, j] <- length(havainnotLokuksessa)
       for (k in 1:noalle[j]) {
         alleleCode <- k
-        N_ijk <- length(find(data[i_rivit, j] == alleleCode))
+        N_ijk <- length(matlab2r::find(data[i_rivit, j] == alleleCode))
         counts[k, j, i] <- N_ijk
       }
     }

R/laskeMuutokset12345.R

@@ -68,7 +68,7 @@ laskeMuutokset <- function(ind, globalRows, data, adjprior, priorTerm) {
   COUNTS[, , i1] <- COUNTS[, , i1] + diffInCounts
   SUMCOUNTS[i1, ] <- SUMCOUNTS[i1, ] + diffInSumCounts
 
-  i2 <- find(muutokset == -Inf) # Etsit<69><74>n populaatiot jotka muuttuneet viime kerran j<>lkeen. (Searching for populations that have changed since the last time)
+  i2 <- matlab2r::find(muutokset == -Inf) # Etsit<69><74>n populaatiot jotka muuttuneet viime kerran j<>lkeen. (Searching for populations that have changed since the last time)
   i2 <- setdiff(i2, i1)
   i2_logml <- POP_LOGML[i2]
 
@@ -95,7 +95,7 @@ laskeMuutokset2 <- function(i1, globalRows, data, adjprior, priorTerm) {
 
   i1_logml <- POP_LOGML[i1]
 
-  inds <- find(PARTITION == i1)
+  inds <- matlab2r::find(PARTITION == i1)
   ninds <- length(inds)
 
   if (ninds == 0) {
@@ -138,7 +138,7 @@ laskeMuutokset2 <- function(i1, globalRows, data, adjprior, priorTerm) {
 laskeMuutokset3 <- function(T2, inds2, globalRows, data, adjprior, priorTerm, i1) {
   # Palauttaa length(unique(T2))*npops taulun, jossa (i,j):s alkio
   # kertoo, mik<69> olisi muutos logml:ss<73>, jos populaation i1 osapopulaatio
-  # inds2(find(T2==i)) siirret<65><74>n koriin j.
+  # inds2(matlab2r::find(T2==i)) siirret<65><74>n koriin j.
 
   npops <- size(COUNTS, 3)
   npops2 <- length(unique(T2))
@@ -146,7 +146,7 @@ laskeMuutokset3 <- function(T2, inds2, globalRows, data, adjprior, priorTerm, i1
 
   i1_logml <- POP_LOGML[i1]
   for (pop2 in 1:npops2) {
-    inds <- inds2[find(T2 == pop2)]
+    inds <- inds2[matlab2r::find(T2 == pop2)]
     ninds <- length(inds)
     if (ninds > 0) {
       rows <- list()

R/learn_partition_modified.R

@@ -11,9 +11,9 @@ learn_partition_modified <- function(ordered) {
   part <- learn_simple_partition(ordered, 0.05)
   nclust <- length(unique(part))
   if (nclust == 3) {
-    mini_1 <- min(ordered(which(part == 1)))
+    mini_1 <- base::ordered(which(part == 1))
-    mini_2 <- min(ordered(which(part == 2)))
+    mini_2 <- base::min(ordered(which(part == 2)))
-    mini_3 <- min(ordered(which(part == 3)))
+    mini_3 <- base::min(ordered(which(part == 3)))
     if (mini_1 > 0.9 & mini_2 > 0.9) {
       part[part == 2] <- 1
       part[part == 3] <- 2

R/newGetDistances.R

@@ -1,11 +1,11 @@
 newGetDistances <- function(data, rowsFromInd) {
-  ninds <- max(data[, ncol(data)])
+  ninds <- base::max(data[, ncol(data)])
   nloci <- size(data, 2) - 1
   riviLkm <- choose(ninds, 2)
 
-  empties <- find(data < 0)
+  empties <- matlab2r::find(data < 0)
   data[empties] <- 0
-  data <- apply(data, 2, as.numeric) # max(noalle) oltava <256
+  data <- apply(data, 2, as.numeric) # base::max(noalle) oltava <256
 
   pariTaulu <- zeros(riviLkm, 2)
   aPointer <- 1
@@ -51,10 +51,10 @@ newGetDistances <- function(data, rowsFromInd) {
   }
 
   rm(x, y, vertailutNyt)
-  nollat <- find(vertailuja == 0)
+  nollat <- matlab2r::find(vertailuja == 0)
   dist <- zeros(length(vertailuja), 1)
   dist[nollat] <- 1
-  muut <- find(vertailuja > 0)
+  muut <- matlab2r::find(vertailuja > 0)
   dist[muut] <- summa[muut] / vertailuja[muut]
   rm(summa, vertailuja)
   Z <- linkage(t(dist))

R/poistaTyhjatPopulaatiot.R

@@ -1,13 +1,13 @@
 poistaTyhjatPopulaatiot <- function(npops) {
   # % Poistaa tyhjentyneet populaatiot COUNTS:ista ja
   # % SUMCOUNTS:ista. P<>ivitt<74><74> npops:in ja PARTITION:in.
-  notEmpty <- find(any(SUMCOUNTS, 2))
+  notEmpty <- matlab2r::find(any(SUMCOUNTS, 2))
   COUNTS <- COUNTS[, , notEmpty]
   SUMCOUNTS <- SUMCOUNTS[notEmpty, ]
   LOGDIFF <- LOGDIFF[, notEmpty]
 
   for (n in 1:length(notEmpty)) {
-    apu <- find(PARTITION == notEmpty(n))
+    apu <- matlab2r::find(PARTITION == notEmpty(n))
     PARTITION[apu] <- n
   }
   npops <- length(notEmpty)

R/rBAPS-package.R

@@ -5,6 +5,6 @@
 #' @note Found a bug? Want to suggest a feature? Contribute to the scientific
 #' and open source communities by opening an issue on our home page.
 #' Check the "BugReports" field on the package description for the URL.
-#' @importFrom matlab2r blanks cell colon inputdlg isempty isfield isspace max min ones rand repmat reshape size sortrows squeeze strcmp times zeros
+#' @importFrom matlab2r blanks cell colon find inputdlg isempty isfield isspace max min ones rand repmat reshape size sortrows squeeze strcmp times zeros
 #' @importFrom stats runif
 NULL

R/rand_disc.R

@@ -1,7 +1,7 @@
 rand_disc <- function(CDF) {
   # %returns an index of a value from a discrete distribution using inversion method
   slump <- rand
-  har <- find(CDF > slump)
+  har <- matlab2r::find(CDF > slump)
   svar <- har(1)
   return(svar)
 }

R/simuloiAlleeli.R

@@ -26,6 +26,6 @@ simuloiAlleeli <- function(allfreqs, pop, loc) {
   cumsumma <- cumsum(freqs)
   arvo <- runif(1)
   isommat <- which(cumsumma > arvo)
-  all <- min(isommat)
+  all <- base::min(isommat)
   return(all)
 }

R/testaaOnkoKunnollinenBapsData.R

@@ -12,7 +12,7 @@ testaaOnkoKunnollinenBapsData <- function(data) {
     return(ninds)
   }
   lastCol <- data[, ncol(data)]
-  ninds <- max(lastCol)
+  ninds <- base::max(lastCol)
   if (any(1:ninds != unique(lastCol))) {
     ninds <- 0
     return(ninds)

R/updateGlobalVariables.R

@@ -14,7 +14,7 @@ updateGlobalVariables <- function(ind, i2, diffInCounts, adjprior, priorTerm) {
   )
 
   LOGDIFF[, c(i1, i2)] <- -Inf
-  inx <- c(find(PARTITION == i1), find(PARTITION == i2))
+  inx <- c(matlab2r::find(PARTITION == i1), matlab2r::find(PARTITION == i2))
   LOGDIFF[inx, ] <- -Inf
 }
 
@@ -22,7 +22,7 @@ updateGlobalVariables2 <- function(i1, i2, diffInCounts, adjprior, priorTerm) {
   # % Suorittaa globaalien muuttujien muutokset, kun kaikki
   # % korissa i1 olevat yksil<69>t siirret<65><74>n koriin i2.
 
-  inds <- find(PARTITION == i1)
+  inds <- matlab2r::find(PARTITION == i1)
   PARTITION[inds] <- i2
 
   COUNTS[, , i1] <- COUNTS[, , i1] - diffInCounts
@@ -34,7 +34,7 @@ updateGlobalVariables2 <- function(i1, i2, diffInCounts, adjprior, priorTerm) {
   POP_LOGML[i2] <- computePopulationLogml(i2, adjprior, priorTerm)
 
   LOGDIFF[, c(i1, i2)] <- -Inf
-  inx <- c(find(PARTITION == i1), find(PARTITION == i2))
+  inx <- c(matlab2r::find(PARTITION == i1), matlab2r::find(PARTITION == i2))
   LOGDIFF[inx, ] <- -Inf
 }
 
@@ -56,6 +56,6 @@ updateGlobalVariables3 <- function(muuttuvat, diffInCounts, adjprior, priorTerm,
   )
 
   LOGDIFF[, c(i1, i2)] <- -Inf
-  inx <- c(find(PARTITION == i1), find(PARTITION == i2))
+  inx <- c(matlab2r::find(PARTITION == i1), matlab2r::find(PARTITION == i2))
   LOGDIFF[inx, ] <- -Inf
 }

R/writeMixtureInfo.R

@@ -64,7 +64,7 @@ writeMixtureInfo <- function(logml, rowsFromInd, data, adjprior, priorTerm, outP
     append(fid, c("Best Partition: ", "\n"))
   }
   for (m in 1:cluster_count) {
-    indsInM <- find(PARTITION == m)
+    indsInM <- matlab2r::find(PARTITION == m)
     length_of_beginning <- 11 + floor(log10(m))
     cluster_size <- length(indsInM)
 
@@ -139,8 +139,8 @@ writeMixtureInfo <- function(logml, rowsFromInd, data, adjprior, priorTerm, outP
         nimi <- as.character(popnames[i])
         nameSizes[i] <- length(nimi)
       }
-      maxSize <- max(nameSizes)
+      maxSize <- base::max(nameSizes)
-      maxSize <- max(maxSize, 5)
+      maxSize <- base::max(maxSize, 5)
       erotus <- maxSize - 5
       alku <- blanks(erotus)
       ekarivi <- c(alku, "  ind", blanks(6 + erotus))
@@ -193,8 +193,8 @@ writeMixtureInfo <- function(logml, rowsFromInd, data, adjprior, priorTerm, outP
     nloci <- size(COUNTS, 2)
     d <- zeros(maxnoalle, nloci, npops)
     prior <- adjprior
-    prior[find(prior == 1)] <- 0
+    prior[matlab2r::find(prior == 1)] <- 0
-    nollia <- find(all(prior == 0)) # Loci in which only one allele was detected.
+    nollia <- matlab2r::find(all(prior == 0)) # Loci in which only one allele was detected.
     prior[1, nollia] <- 1
     for (pop1 in 1:npops) {
       d[, , pop1] <- (squeeze(COUNTS[, , pop1]) + prior) /
@@ -261,7 +261,7 @@ writeMixtureInfo <- function(logml, rowsFromInd, data, adjprior, priorTerm, outP
 
   partitionSummary <- sortrows(partitionSummary, 2)
   partitionSummary <- partitionSummary[size(partitionSummary, 1):1, ]
-  partitionSummary <- partitionSummary[find(partitionSummary[, 2] > -1e49), ]
+  partitionSummary <- partitionSummary[matlab2r::find(partitionSummary[, 2] > -1e49), ]
   if (size(partitionSummary, 1) > 10) {
     vikaPartitio <- 10
   } else {
@@ -298,12 +298,12 @@ writeMixtureInfo <- function(logml, rowsFromInd, data, adjprior, priorTerm, outP
     len <- length(npopsTaulu)
     probs <- zeros(len, 1)
     partitionSummary[, 2] <- partitionSummary[, 2] -
-      max(partitionSummary[, 2])
+      base::max(partitionSummary[, 2])
     sumtn <- sum(exp(partitionSummary[, 2]))
     for (i in 1:len) {
       npopstn <- sum(
         exp(
-          partitionSummary[find(
+          partitionSummary[matlab2r::find(
             partitionSummary[, 1] == npopsTaulu[i]
           ), 2]
         )

man/linkage.Rd

@@ -23,5 +23,9 @@ output format of PDIST.
 Z = linkage(X) returns a matrix Z that encodes a tree containing hierarchical clusters of the rows of the input data matrix X.
 }
 \note{
-This is also a base Matlab function. The reason why the source code is also present here is unclear.
+This is also a base MATLAB function. The reason why the BAPS
+source code also contains a LINKAGE function is unclear. One could speculate
+that BAPS should use this function instead of the base one, so this is why
+this function is part of this package (instead of a MATLAB-replicating
+package such as matlab2r)
 }