* replaced stackr::ref_alt_allelesto stackr::change_alleles.

* replaced `stackr::read_long_tidy_wide` to `stackr::tidy_wide`

R/fst_NEI87.R

@@ -43,7 +43,7 @@
 #' @param data A file in the working directory or object in the global environment 
 #' in wide or long (tidy) formats. To import, the function uses internally
 #' \href{https://github.com/thierrygosselin/stackr}{stackr} 
-#' \code{\link[stackr]{read_long_tidy_wide}}. See details for more info.
+#' \code{\link[stackr]{tidy_wide}}. See details for more info.
 #' 
 #' \emph{How to get a tidy data frame ?}
 #' \href{https://github.com/thierrygosselin/stackr}{stackr} 
@@ -129,7 +129,7 @@
 #' @details \strong{Input data:}
 #'  
 #' To discriminate the long from the wide format, 
-#' the function \pkg{stackr} \code{\link[stackr]{read_long_tidy_wide}} searches 
+#' the function \pkg{stackr} \code{\link[stackr]{tidy_wide}} searches 
 #' for \code{MARKERS or LOCUS} in column names (TRUE = long format).
 #' The data frame is tab delimitted.
 
@@ -158,7 +158,7 @@
 #' @rdname fst_NEI87
 #' @import parallel
 #' @importFrom dplyr select distinct n_distinct group_by ungroup rename arrange tally filter if_else mutate summarise left_join inner_join right_join anti_join semi_join full_join summarise_each_ funs summarise_if mutate_if count bind_rows bind_cols ntile desc n
-#' @importFrom stackr read_long_tidy_wide change_pop_names
+#' @importFrom stackr tidy_wide change_pop_names
 #' @importFrom tidyr spread gather unite separate complete nesting
 #' @importFrom stringi stri_replace_all_regex stri_sub stri_join
 #' @importFrom purrr map
@@ -256,7 +256,7 @@ fst_NEI87 <- function(
 
   # Import data ---------------------------------------------------------------
   if (verbose) message("Importing data")
-  input <- stackr::read_long_tidy_wide(data = data)
+  input <- stackr::tidy_wide(data = data)
 
   # Change individuals names containing special character
   input$INDIVIDUALS <- stringi::stri_replace_all_fixed(

R/fst_WC84.R

@@ -41,7 +41,7 @@
 #' @param data A file in the working directory or object in the global environment 
 #' in wide or long (tidy) formats. To import, the function uses internally
 #' \href{https://github.com/thierrygosselin/stackr}{stackr} 
-#' \code{\link[stackr]{read_long_tidy_wide}}. See details for more info.
+#' \code{\link[stackr]{tidy_wide}}. See details for more info.
 #' 
 #' \emph{How to get a tidy data frame ?}
 #' \href{https://github.com/thierrygosselin/stackr}{stackr} 
@@ -137,7 +137,7 @@
 #' @details \strong{Input data:}
 #'  
 #' To discriminate the long from the wide format, 
-#' the function \pkg{stackr} \code{\link[stackr]{read_long_tidy_wide}} searches 
+#' the function \pkg{stackr} \code{\link[stackr]{tidy_wide}} searches 
 #' for \code{MARKERS or LOCUS} in column names (TRUE = long format).
 #' The data frame is tab delimitted.
 
@@ -166,7 +166,7 @@
 #' @rdname fst_WC84
 #' @import parallel
 #' @import ggplot2
-#' @importFrom stackr read_long_tidy_wide discard_monomorphic_markers keep_common_markers change_pop_names detect_biallelic_markers
+#' @importFrom stackr tidy_wide discard_monomorphic_markers keep_common_markers change_pop_names detect_biallelic_markers
 #' @importFrom tidyr separate gather spread unite
 #' @importFrom purrr map flatten
 #' @importFrom dplyr mutate summarise group_by ungroup select rename full_join left_join anti_join right_join semi_join filter n_distinct distinct arrange sample_n bind_rows bind_cols ntile desc n
@@ -291,7 +291,7 @@ fst_WC84 <- function(
 
   # Import data ---------------------------------------------------------------
   if (verbose) message("Importing data")
-  input <- stackr::read_long_tidy_wide(data = data, import.metadata = TRUE)
+  input <- stackr::tidy_wide(data = data, import.metadata = TRUE)
 
   # For long tidy format, switch LOCUS to MARKERS column name, if found MARKERS not found
   if (tibble::has_name(input, "LOCUS") && !tibble::has_name(input, "MARKERS")) {

README.Rmd

@@ -239,6 +239,10 @@ Danecek P, Auton A, Abecasis G et al. (2011) The variant call format and VCFtool
 
 Foll M, Gaggiotti O (2008) A Genome-Scan Method to Identify Selected Loci Appropriate for Both Dominant and Codominant Markers: A Bayesian Perspective. Genetics, 180, 977–993.
 
+Goudet J.hierfstat, a package for r to compute and test hierarchical F-statistics.
+Molecular Ecology Notes.2005; 5: 184-186. doi:10.1111/j.1471-8286.2004.00828.x
+
+
 Ishwaran H. and Kogalur U.B. (2015). Random Forests for Survival, Regression and Classification (RF-SRC), R package version 1.6.1.
 
 Jombart T (2008) adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics, 24, 1403–1405.

README.md

@@ -274,6 +274,8 @@ Danecek P, Auton A, Abecasis G et al. (2011) The variant call format and VCFtool
 
 Foll M, Gaggiotti O (2008) A Genome-Scan Method to Identify Selected Loci Appropriate for Both Dominant and Codominant Markers: A Bayesian Perspective. Genetics, 180, 977–993.
 
+Goudet J.hierfstat, a package for r to compute and test hierarchical F-statistics. Molecular Ecology Notes.2005; 5: 184-186. <doi:10.1111/j.1471-8286.2004.00828.x>
+
 Ishwaran H. and Kogalur U.B. (2015). Random Forests for Survival, Regression and Classification (RF-SRC), R package version 1.6.1.
 
 Jombart T (2008) adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics, 24, 1403–1405.