diff --git a/R/tree.R b/R/tree.R
index 5cdc20d1..ba37998f 100755
--- a/R/tree.R
+++ b/R/tree.R
@@ -31,200 +31,284 @@
 ## Functions ##
 ###############
 
-###########################
-## Approach 0 | FastTree2.0
-###########################
-## !! FastTree will only work if there are unique sequence names!!
-#' convertFA2Tree
-#'
-#' @param fa_path Path to the input FASTA alignment file (.fa). Default is the 
-#' path to "data/alns/pspa_snf7.fa".
-#' @param tre_path Path to the output file where the generated tree (.tre) will 
-#' be saved. Default is the path to "data/alns/pspa_snf7.tre".
-#' @param fasttree_path Path to the FastTree executable, which is used to 
-#' generate the phylogenetic tree. Default is "src/FastTree".
-#'
-#' @return No return value. The function generates a tree file (.tre) from the 
-#' input FASTA file.
-#' @export
-#'
-#' @examples
-#' \dontrun{
-#' convert_fa2tre(here("data/alns/pspa_snf7.fa"), 
-#'                 here("data/alns/pspa_snf7.tre"), 
-#'                 here("src/FastTree")
-#' }
-convertFA2Tree <- function(fa_path = here("data/alns/pspa_snf7.fa"),
-    tre_path = here("data/alns/pspa_snf7.tre"),
-    fasttree_path = here("src/FastTree")) {
-    # fa_path=here("data/alns/pspa_snf7.fa")
-    # tre_path=here("data/alns/pspa_snf7.tre")
-    # fasttree_path=here("src/FastTree")
-    print(fa_path)
-    system2(
-        command = fasttree_path,
-        args = paste(c(fa_path, ">", tre_path),
-            sep = "", collapse = " "
-        )
-    )
-    ################################
-    ## Compiling FastTree.c on a Mac
-    ################################
-    ## Other platforms? Check http://www.microbesonline.org/fasttree/
-    # system(paste("gcc -DNO_SSE -O3 -finline-functions -funroll-loops -Wall -o",
-    #              here("src/FastTree"),
-    #              here("src/FastTree.c"), "-lm", collapse=" "))
-}
-## Generate Trees for ALL fasta files in "data/alns"
-#' convertAlignment2Trees
-#'
-#' @description
-#' Generate Trees for ALL fasta files in "data/alns"
-#'
-#' @param aln_path Path to the directory containing all the alignment FASTA 
-#' files (.fa) for which trees will be generated. Default is "data/alns/".
-#' 
-#'
-#' @importFrom here here
-#' @importFrom purrr pmap
-#' @importFrom stringr str_replace_all
-#'
-#' @return No return value. The function generates tree files (.tre) for each 
-#' alignment file in the specified directory.
-#' @export
-#'
-#' @examples
-#' \dontrun{
-#' generate_trees(here("data/alns/"))
-#' }
-convertAlignment2Trees <- function(aln_path = here("data/alns/")) {
-    # finding all fasta alignment files
-    fa_filenames <- list.files(path = aln_path, pattern = "*.fa")
-    fa_paths <- paste0(aln_path, fa_filenames)
-    variable <- str_replace_all(basename(fa_filenames),
-        pattern = ".fa", replacement = ""
-    )
-
-    ## Using purrr::pmap to generate trees for each of the fa files!
-    fa2tre_args <- list(
-        fa_path = fa_paths,
-        tre_path = paste0(aln_path, variable, ".tre")
-    )
-    pmap(
-        .l = fa2tre_args, .f = convertFA2Tree,
-        fasttree_path = here("src/FastTree")
-    )
-}
-
 ##############################
-## REFS: 1-4
+## Approach 0-4
 ############
+#' Approach 0: !! FastTree will only work if there are unique sequence names!!
 #' createFA2Tree
-#'
+#' @author Klangina, Outreachy Applicant
 #' @author Janani Ravi, MolEcologist
-#' @keywords phylogenetic tree, alignment, fasta
+#' @keywords phylogenetic tree, alignment, fasta, FastTree
 #' @description
-#' Generating phylogenetic tree from alignment file '.fa'
+#' Generates a phylogenetic tree from an alignment file '.fa' using either FastTree or a detailed phylogenetic analysis approach.
 #'
 #' @param fa_file Character. Path to the alignment FASTA file (.fa) from which
-#'  the phylogenetic tree will be generated. Default is 'pspa_snf7.fa'.
-#' @param out_file Path to the output file where the generated tree (.tre) will 
+#'  the phylogenetic tree will be generated. Default is "data/alns/pspa_snf7.fa".
+#' @param out_file Character. Path to the output file where the generated tree (.tre) will 
 #' be saved. Default is "data/alns/pspa_snf7.tre".
+#' @param enable_fast Logical. If TRUE, uses FastTree approach; if FALSE, uses detailed phylogenetic analysis. Default is TRUE.
+#' @param fasttree_path Character. Path to the FastTree executable. Default is "src/FastTree".
+#' @param format Character. Format of the input file. Default is "fasta".
+#' @param seq_type Character. Type of sequences in the input file. Default is "AA" (amino acid).
+#' @param dist_matrix Character. Distance matrix to be used. Default is "similarity".
+#' @param subset_size Numeric. Number of sequences to subset for analysis. Default is 10.
+#' @param model_test Character. Model to use for model testing. Default is "all".
+#' @param dna_model Character. Model to use for DNA distance calculation. Default is "JC69".
+#' @param ml_model Character. Model to use for maximum likelihood estimation. Default is "JC".
+#' @param rearrangement Character. Type of tree rearrangement for ML optimization. Default is "stochastic".
+#' @param bootstrap_reps Numeric. Number of bootstrap replicates. Default is 100.
+#' @param bootstrap_p Numeric. P-value threshold for bootstrap support. Default is 50.
+#' @param plot_type Character. Type of plot for bootstrap tree. Default is "p".
+#' @param bootstrap_file Character. Path to save the bootstrap tree file. Default is "bootstrap_example.tre".
 #'
 #' @importFrom ape write.tree
 #' @importFrom phangorn bootstrap.pml dist.ml NJ modelTest phyDat plotBS pml pml.control pratchet optim.parsimony optim.pml read.phyDat upgma
 #' @importFrom seqinr dist.alignment read.alignment
 #' @importFrom stats logLik
 #'
-#' @return No return value. The function generates a phylogenetic tree file 
-#' (.tre) based on different approaches like Neighbor Joining, UPGMA, and 
-#' Maximum Likelihood.
+#' @return No return value. The function generates phylogenetic tree files 
+#' (.tre) based on either FastTree or detailed phylogenetic analysis approaches. 
+#' It also produces various plots and console outputs when using the detailed approach.
 #' @export
 #'
-#' @details The alignment file would need two columns: 1. accession +
-#' number and 2. alignment. The protein homolog accession to lineage mapping +
-#' file should have
+#' @details The function offers two main approaches for phylogenetic tree construction:
+#' 1. FastTree approach: A quick method using the FastTree program.
+#' 2. Detailed phylogenetic analysis: Includes model testing, distance matrix calculation,
+#'    UPGMA and NJ tree construction, parsimony searches, and maximum likelihood estimation
+#'    with bootstrapping.
+#' The alignment file should be in FASTA format with sequences properly aligned.
+#' When using FastTree, ensure that the FastTree executable is correctly installed and 
+#' the path is properly specified.
 #'
-#' @note Please refer to the source code if you have alternate +
-#' file formats and/or column names.
+#' @note This function requires the ape, phangorn, and seqinr packages to be installed.
+#' The detailed analysis approach performs computationally intensive operations and may 
+#' take a significant amount of time to run, especially for large datasets or with high 
+#' numbers of bootstrap replicates. FastTree approach is significantly faster but may 
+#' provide less detailed results.
 #'
 #' @examples
 #' \dontrun{
-#' generate_aln2tree("pspa_snf7.fa")
+#' # Using FastTree approach
+#' createFA2Tree("data/alns/my_alignment.fa", "data/trees/my_tree.tre", enable_fast = TRUE)
+#' 
+#' # Using detailed phylogenetic analysis approach
+#' createFA2Tree("data/alns/protein_align.fa", "data/trees/protein_tree.tre",
+#'               enable_fast = FALSE, subset_size = 20, bootstrap_reps = 500)
 #' }
-createFA2Tree <- function(fa_file = "data/alns/pspa_snf7.fa",
-    out_file = "data/alns/pspa_snf7.tre") {
+createFA2Tree <- function(
+    fa_file = "data/alns/pspa_snf7.fa",
+    out_file = "data/alns/pspa_snf7.tre",
+    enable_fast = TRUE,
+    fasttree_path = "src/FastTree",
+    format = "fasta",
+    seq_type = "AA",
+    dist_matrix = "similarity",
+    subset_size = 10,
+    model_test = "all",
+    dna_model = "JC69",
+    ml_model = "JC",
+    rearrangement = "stochastic",
+    bootstrap_reps = 100,
+    bootstrap_p = 50,
+    plot_type = "p",
+    bootstrap_file = "bootstrap_example.tre"
+) {
     ## SAMPLE ARGS
     # fa_file="data/alns/pspa_snf7.fa"
     # out_file="data/alns/pspa_snf7.tre"
+    # fasttree_path="src/FastTree"
 
-    ###########################
-    ## Approach 1
-    ###########################
-    ## https://a-little-book-of-r-for-bioinformatics.readthedocs.io/en/latest/src/chapter5.html
-    prot_aln <- read.alignment(file = fa_file, format = "fasta")
-    prot_aln$seq
-    prot_dist <- dist.alignment(prot_aln, matrix = "similarity")
-    prot_nj <- NJ(prot_dist)
-    prot_nj_tree <- plot(prot_nj, main = "Neighbor Joining")
-    write.tree(phy = prot_nj_tree, file = tre_path)
+    if (enable_fast) {
+        # FastTree approach
+        print(fa_file)
+        system2(
+            command = fasttree_path,
+            args = paste(c(fa_file, ">", out_file),
+                sep = "", collapse = " "
+            )
+        )
+        ################################
+        ## Compiling FastTree.c on a Mac
+        ################################
+        ## Other platforms? Check http://www.microbesonline.org/fasttree/
+        # system(paste("gcc -DNO_SSE -O3 -finline-functions -funroll-loops -Wall -o",
+        #              here("src/FastTree"),
+        #              here("src/FastTree.c"), "-lm", collapse=" "))
+    } else {
+        # Detailed phylogenetic analysis approach
+        ###########################
+        ## Approach 1
+        ###########################
+        ## https://a-little-book-of-r-for-bioinformatics.readthedocs.io/en/latest/src/chapter5.html
+        prot_aln <- read.alignment(file = fa_file, format = format)
+        prot_aln$seq
+        prot_dist <- dist.alignment(prot_aln, matrix = dist_matrix)
+        prot_nj <- NJ(prot_dist)
+        prot_nj_tree <- plot(prot_nj, main = "Neighbor Joining")
+        write.tree(phy = prot_nj_tree, file = out_file)
 
-    ###########################
-    ## Approach 2
-    ###########################
-    ## Alignment file formats and conversion
-    # read in sequence data, convert to phyDat
-    prot_fa <- read.phyDat(fa_file, format = "fasta", type = "AA")
-    prot_phyDat <- phyDat(prot_fa, type = "AA", levels = NULL)
-    prot10 <- subset(prot_phyDat, 1:10)
-    prot10_phyDat <- phyDat(prot10, type = "AA", levels = NULL)
+        ###########################
+        ## Approach 2
+        ###########################
+        ## Alignment file formats and conversion
+        # read in sequence data, convert to phyDat
+        prot_fa <- read.phyDat(fa_file, format = format, type = seq_type)
+        prot_phyDat <- phyDat(prot_fa, type = seq_type, levels = NULL)
+        prot_subset <- subset(prot_phyDat, 1:subset_size)
+        prot_subset_phyDat <- phyDat(prot_subset, type = seq_type, levels = NULL)
 
-    ## Model Testing & Distance Matrices
-    ## Comparison of different nucleotide or amino acid substitution models
-    mt <- modelTest(prot10, model = "all")
-    print(mt)
+        ## Model Testing & Distance Matrices
+        ## Comparison of different nucleotide or amino acid substitution models
+        mt <- modelTest(prot_subset, model = model_test)
+        print(mt)
 
-    # estimate a distance matrix using a Jules-Cantor Model
-    dna_dist <- dist.ml(prot10, model = "JC69")
+        # estimate a distance matrix using a Jules-Cantor Model
+        dna_dist <- dist.ml(prot_subset, model = dna_model)
 
-    ## Neighbor Joining, UPGMA, and Maximum Parsimony
-    # quick and dirty UPGMA and NJ trees
-    prot_UPGMA <- upgma(dna_dist)
-    prot_NJ <- NJ(dna_dist)
-    plot(prot_UPGMA, main = "UPGMA")
-    plot(prot_NJ, main = "Neighbor Joining")
+        ## Neighbor Joining, UPGMA, and Maximum Parsimony
+        # quick and dirty UPGMA and NJ trees
+        prot_UPGMA <- upgma(dna_dist)
+        prot_NJ <- NJ(dna_dist)
+        plot(prot_UPGMA, main = "UPGMA")
+        plot(prot_NJ, main = "Neighbor Joining")
 
-    # parsimony searches
-    prot_optim <- optim.parsimony(prot_NJ, prot)
-    prot_pratchet <- pratchet(prot10) # returning error
-    plot(prot_optim)
-    plot(prot_pratchet)
+        # parsimony searches
+        prot_optim <- optim.parsimony(prot_NJ, prot_phyDat)
+        prot_pratchet <- pratchet(prot_subset) # returning error
+        plot(prot_optim)
+        plot(prot_pratchet)
 
-    ## Maximum likelihood and Bootstrapping
-    # ml estimation w/ distance matrix
-    fit <- pml(prot_NJ, prot10)
-    print(fit)
-    fitJC <- optim.pml(fit, model = "JC", rearrangement = "stochastic")
-    logLik(fitJC)
-    bs <- bootstrap.pml(fitJC,
-        bs = 100, optNni = TRUE, multicore = TRUE,
-        control = pml.control(trace = 0)
-    )
-    plotBS(midpoint(fitJC$tree), bs, p = 50, type = "p")
+        ## Maximum likelihood and Bootstrapping
+        # ml estimation w/ distance matrix
+        fit <- pml(prot_NJ, prot_subset)
+        print(fit)
+        fitJC <- optim.pml(fit, model = ml_model, rearrangement = rearrangement)
+        logLik(fitJC)
+        bs <- bootstrap.pml(fitJC,
+            bs = bootstrap_reps, optNni = TRUE, multicore = TRUE,
+            control = pml.control(trace = 0)
+        )
+        plotBS(midpoint(fitJC$tree), bs, p = bootstrap_p, type = plot_type)
 
-    # subsetted alignment bs example
-    prot10_dm <- dist.ml(prot10)
-    prot10_NJ <- NJ(prot10_dm)
-    fit2 <- pml(prot10_NJ, data = prot10)
-    print(fit2)
-    fitJC2 <- optim.pml(fit2, model = "JC", rearrangement = "stochastic")
-    logLik(fitJC2)
-    bs_subset <- bootstrap.pml(fitJC2,
-        bs = 100, optNni = TRUE, multicore = TRUE,
-        control = pml.control(trace = 0)
-    )
-    bs2 <- plotBS(midpoint(fitJC2$tree), bs, p = 50, type = "p")
+        # subsetted alignment bs example
+        prot_subset_dm <- dist.ml(prot_subset)
+        prot_subset_NJ <- NJ(prot_subset_dm)
+        fit2 <- pml(prot_subset_NJ, data = prot_subset)
+        print(fit2)
+        fitJC2 <- optim.pml(fit2, model = ml_model, rearrangement = rearrangement)
+        logLik(fitJC2)
+        bs_subset <- bootstrap.pml(fitJC2,
+            bs = bootstrap_reps, optNni = TRUE, multicore = TRUE,
+            control = pml.control(trace = 0)
+        )
+        bs2 <- plotBS(midpoint(fitJC2$tree), bs, p = bootstrap_p, type = plot_type)
 
-    ## Exporting Trees
-    write.tree(bs2, file = "bootstrap_example.tre")
+        ## Exporting Trees
+        write.tree(bs2, file = bootstrap_file)
+    }
 }
+#' convertAlignment2Trees
+#' @author Klangina, Outreachy Applicant
+#' @description
+#' Generate Trees for ALL fasta files in the specified directory. This function processes
+#' multiple alignment files and creates phylogenetic trees for each, using either FastTree
+#' or a detailed phylogenetic analysis approach.
+#'
+#' @param aln_path Character. Path to the directory containing all the alignment FASTA 
+#' files (.fa) for which trees will be generated. Default is here("data/alns/").
+#' @param enable_fast Logical. If TRUE, uses FastTree approach; if FALSE, uses detailed 
+#' phylogenetic analysis. Default is TRUE.
+#' @param fasttree_path Character. Path to the FastTree executable. Default is here("src/FastTree").
+#' @param format Character. Format of the input files. Default is "fasta".
+#' @param seq_type Character. Type of sequences in the input files. Default is "AA" (amino acid).
+#' @param dist_matrix Character. Distance matrix to be used. Default is "similarity".
+#' @param subset_size Numeric. Number of sequences to subset for analysis. Default is 10.
+#' @param model_test Character. Model to use for model testing. Default is "all".
+#' @param dna_model Character. Model to use for DNA distance calculation. Default is "JC69".
+#' @param ml_model Character. Model to use for maximum likelihood estimation. Default is "JC".
+#' @param rearrangement Character. Type of tree rearrangement for ML optimization. Default is "stochastic".
+#' @param bootstrap_reps Numeric. Number of bootstrap replicates. Default is 100.
+#' @param bootstrap_p Numeric. P-value threshold for bootstrap support. Default is 50.
+#' @param plot_type Character. Type of plot for bootstrap tree. Default is "p".
+#' @param bootstrap_file_suffix Character. Suffix for bootstrap tree files. Default is "_bootstrap.tre".
+#'
+#' @importFrom here here
+#' @importFrom purrr pmap
+#' @importFrom stringr str_replace_all
+#'
+#' @return No return value. The function generates tree files (.tre) and bootstrap files 
+#' for each alignment file in the specified directory.
+#' @export
+#'
+#' @details This function processes all .fa files in the specified directory. For each file, 
+#' it generates a phylogenetic tree using either FastTree (if enable_fast is TRUE) or a 
+#' detailed phylogenetic analysis approach (if enable_fast is FALSE). The detailed approach 
+#' includes model testing, distance matrix calculation, UPGMA and NJ tree construction, 
+#' parsimony searches, and maximum likelihood estimation with bootstrapping.
+#'
+#' @note This function requires the ape, phangorn, and seqinr packages to be installed.
+#' The detailed analysis approach performs computationally intensive operations and may 
+#' take a significant amount of time to run, especially for large datasets or with high 
+#' numbers of bootstrap replicates. FastTree approach is significantly faster but may 
+#' provide less detailed results.
+#'
+#' @examples
+#' \dontrun{
+#' # Using FastTree approach for all alignments in the default directory
+#' convertAlignment2Trees()
+#' 
+#' # Using detailed phylogenetic analysis for alignments in a specific directory
+#' convertAlignment2Trees(aln_path = "path/to/alignments/", 
+#'                        enable_fast = FALSE, 
+#'                        subset_size = 20, 
+#'                        bootstrap_reps = 500)
+#' }
+convertAlignment2Trees <- function(
+    aln_path = here("data/alns/"),
+    enable_fast = TRUE,
+    fasttree_path = here("src/FastTree"),
+    format = "fasta",
+    seq_type = "AA",
+    dist_matrix = "similarity",
+    subset_size = 10,
+    model_test = "all",
+    dna_model = "JC69",
+    ml_model = "JC",
+    rearrangement = "stochastic",
+    bootstrap_reps = 100,
+    bootstrap_p = 50,
+    plot_type = "p",
+    bootstrap_file_suffix = "_bootstrap.tre"
+) {
+    # finding all fasta alignment files
+    fa_filenames <- list.files(path = aln_path, pattern = "*.fa")
+    fa_paths <- paste0(aln_path, fa_filenames)
+    variable <- str_replace_all(basename(fa_filenames),
+        pattern = ".fa", replacement = ""
+    )
+
+    ## Using purrr::pmap to generate trees for each of the fa files!
+    fa2tre_args <- list(
+        fa_file = fa_paths,
+        out_file = paste0(aln_path, variable, ".tre"),
+        enable_fast = enable_fast,
+        fasttree_path = fasttree_path,
+        format = format,
+        seq_type = seq_type,
+        dist_matrix = dist_matrix,
+        subset_size = subset_size,
+        model_test = model_test,
+        dna_model = dna_model,
+        ml_model = ml_model,
+        rearrangement = rearrangement,
+        bootstrap_reps = bootstrap_reps,
+        bootstrap_p = bootstrap_p,
+        plot_type = plot_type,
+        bootstrap_file = paste0(aln_path, variable, bootstrap_file_suffix)
+    )
+    
+    pmap(
+        .l = fa2tre_args, 
+        .f = createFA2Tree
+    )
+}
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