diff --git a/documentation/boruvka_mst.md b/documentation/boruvka_mst.md
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@@ -0,0 +1,26 @@
+# Boruvka's Minimum Spanning Tree (MST)
+
+This document describes the Boruvka MST implementation located at `R/graph_algorithms/boruvka_mst.r`.
+
+## Description
+
+The implementation builds a Minimum Spanning Tree for an undirected weighted graph using Boruvka's method. The graph is represented by a list with `V` (number of vertices) and `edges` (a data.frame with columns `u`, `v`, `w`). Vertex indices are 1-based to match other algorithms in the repository.
+
+## Usage
+
+In an R session:
+
+source('graph_algorithms/boruvka_mst.r')
+
+From command line using Rscript:
+
+Rscript -e "source('R/graph_algorithms/boruvka_mst.r')"
+
+## Complexity
+
+- Time complexity: Depends on implementation details; this simple version iterates until components merge.
+- Space complexity: O(V + E)
+
+## Notes
+
+- This implementation prioritizes clarity and repository consistency. For large graphs, more optimized data structures and path compression in union-find should be used.
diff --git a/documentation/fast_fourier_transform.md b/documentation/fast_fourier_transform.md
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+++ b/documentation/fast_fourier_transform.md
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+# Fast Fourier Transform (FFT)
+
+This file documents the recursive Cooley-Tukey FFT implementation added to `R/mathematics/fast_fourier_transform.r`.
+
+## Description
+
+The `fft_recursive` function computes the discrete Fourier transform (DFT) of a numeric or complex vector using a divide-and-conquer Cooley-Tukey algorithm. If the input length is not a power of two, it is zero-padded to the next power of two.
+
+## Usage
+
+In an R session:
+
+source('mathematics/fast_fourier_transform.r')
+fft_recursive(c(0, 1, 2, 3))
+
+From the command line with Rscript:
+
+Rscript -e "source('R/mathematics/fast_fourier_transform.r'); print(fft_recursive(c(0,1,2,3)))"
+
+## Complexity
+
+Time complexity: O(n log n) for inputs with length a power of two; otherwise dominated by padding to next power of two.
+
+Space complexity: O(n) additional space for recursive calls.
+
+## Notes
+
+- The function returns a complex vector of the same length (after padding) as the input.
+- This implementation is primarily educational; production code should prefer the optimized `fft` function available in base R.
diff --git a/documentation/hamiltonian_path.md b/documentation/hamiltonian_path.md
new file mode 100644
index 00000000..9f5a7a37
--- /dev/null
+++ b/documentation/hamiltonian_path.md
@@ -0,0 +1,27 @@
+# Hamiltonian Path (Backtracking)
+
+This document describes the Hamiltonian Path backtracking implementation in `R/graph_algorithms/hamiltonian_path.r`.
+
+## Description
+
+The `hamiltonianPath` function searches for a Hamiltonian Path in an undirected graph represented by an adjacency matrix. It uses backtracking to attempt to build a path that visits every vertex exactly once.
+
+## Usage
+
+In an R session:
+
+source('graph_algorithms/hamiltonian_path.r')
+
+From command line using Rscript:
+
+Rscript -e "source('R/graph_algorithms/hamiltonian_path.r')"
+
+## Complexity
+
+- Time complexity: O(n!) in the worst case (backtracking over permutations).
+- Space complexity: O(n) for path storage and recursion.
+
+## Notes
+
+- The implementation assumes an undirected graph given as an adjacency matrix with 0/1 entries.
+- For production use on larger graphs, consider heuristics or approximation algorithms; the problem is NP-complete.
diff --git a/graph_algorithms/boruvka_mst.r b/graph_algorithms/boruvka_mst.r
new file mode 100644
index 00000000..1f087cd7
--- /dev/null
+++ b/graph_algorithms/boruvka_mst.r
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+# Boruvka's Minimum Spanning Tree (MST) — improved R translation
+#
+# Converted from an improved Python implementation: adds path compression in
+# union-find, returns whether a union happened, and guards against infinite
+# loops on disconnected graphs.
+
+create_graph <- function(V) {
+  list(V = as.integer(V), edges = data.frame(u = integer(), v = integer(), w = double(), stringsAsFactors = FALSE))
+}
+
+add_edge <- function(graph, u, v, w) {
+  # Append an edge. Vertices are 1-based indices for consistency.
+  graph$edges <- rbind(graph$edges, data.frame(u = as.integer(u), v = as.integer(v), w = as.numeric(w), stringsAsFactors = FALSE))
+  graph
+}
+
+boruvka_mst <- function(graph) {
+  V <- as.integer(graph$V)
+  edges <- graph$edges
+
+  # Union-Find arrays
+  parent <- seq_len(V)
+  rank <- rep(0L, V)
+
+  find_set <- function(i) {
+    # Iterative find with path compression
+    root <- i
+    while (parent[root] != root) {
+      root <- parent[root]
+    }
+    # path compression
+    j <- i
+    while (parent[j] != root) {
+      nextj <- parent[j]
+      parent[j] <<- root
+      j <- nextj
+    }
+    root
+  }
+
+  union_set <- function(x, y) {
+    xroot <- find_set(x)
+    yroot <- find_set(y)
+    if (xroot == yroot) return(FALSE)
+    if (rank[xroot] < rank[yroot]) {
+      parent[xroot] <<- yroot
+    } else if (rank[xroot] > rank[yroot]) {
+      parent[yroot] <<- xroot
+    } else {
+      parent[yroot] <<- xroot
+      rank[xroot] <<- rank[xroot] + 1L
+    }
+    TRUE
+  }
+
+  num_trees <- V
+  mst_weight <- 0
+  mst_edges <- data.frame(u = integer(), v = integer(), w = double(), stringsAsFactors = FALSE)
+
+  # Edge case: empty graph
+  if (nrow(edges) == 0) {
+    cat("No edges in graph.\n")
+    return(invisible(list(edges = mst_edges, total_weight = 0)))
+  }
+
+  while (num_trees > 1) {
+    cheapest <- rep(NA_integer_, V)
+
+    # For every edge, check components and record cheapest edge for each component
+    for (i in seq_len(nrow(edges))) {
+      u <- edges$u[i]
+      v <- edges$v[i]
+      w <- edges$w[i]
+      set_u <- find_set(u)
+      set_v <- find_set(v)
+
+      if (set_u == set_v) next
+
+      if (is.na(cheapest[set_u]) || edges$w[cheapest[set_u]] > w) {
+        cheapest[set_u] <- i
+      }
+      if (is.na(cheapest[set_v]) || edges$w[cheapest[set_v]] > w) {
+        cheapest[set_v] <- i
+      }
+    }
+
+    any_added <- FALSE
+
+    # Add the cheapest edges to MST
+    for (node in seq_len(V)) {
+      idx <- cheapest[node]
+      if (is.na(idx)) next
+
+      u <- edges$u[idx]
+      v <- edges$v[idx]
+      w <- edges$w[idx]
+      set_u <- find_set(u)
+      set_v <- find_set(v)
+
+      if (set_u != set_v) {
+        if (union_set(set_u, set_v)) {
+          mst_weight <- mst_weight + w
+          mst_edges <- rbind(mst_edges, data.frame(u = u, v = v, w = w, stringsAsFactors = FALSE))
+          num_trees <- num_trees - 1L
+          any_added <- TRUE
+        }
+      }
+    }
+
+    # If no edges were added in this pass, the graph is disconnected
+    if (!any_added) {
+      cat("Graph appears disconnected; stopping. No spanning tree exists that connects all vertices.\n")
+      break
+    }
+  }
+
+  cat("Edges in MST:\n")
+  if (nrow(mst_edges) > 0) {
+    for (i in seq_len(nrow(mst_edges))) {
+      cat(mst_edges$u[i], "--", mst_edges$v[i], "==", mst_edges$w[i], "\n")
+    }
+  } else {
+    cat("(none)\n")
+  }
+  cat("Total weight of MST:", mst_weight, "\n")
+
+  invisible(list(edges = mst_edges, total_weight = mst_weight))
+}
+
+# Example usage and test
+cat("=== Boruvka's MST Algorithm (improved) ===\n")
+g <- create_graph(4)
+g <- add_edge(g, 1, 2, 10)
+g <- add_edge(g, 1, 3, 6)
+g <- add_edge(g, 1, 4, 5)
+g <- add_edge(g, 2, 4, 15)
+g <- add_edge(g, 3, 4, 4)
+
+cat("Graph edges:\n")
+print(g$edges)
+cat("\nComputing MST...\n")
+boruvka_mst(g)
diff --git a/graph_algorithms/hamiltonian_path.r b/graph_algorithms/hamiltonian_path.r
new file mode 100644
index 00000000..2d3da356
--- /dev/null
+++ b/graph_algorithms/hamiltonian_path.r
@@ -0,0 +1,83 @@
+# Hamiltonian Path (Backtracking)
+#
+# This implementation searches for a Hamiltonian Path in an undirected graph
+# represented by an adjacency matrix. It uses backtracking to try all possible
+# vertex sequences. The implementation follows the style used in other
+# algorithms in the `R/graph_algorithms` folder.
+#
+# Time Complexity: O(n!) in the worst case (backtracking over permutations)
+# Space Complexity: O(n) for the path and recursion stack
+#
+# Input: adjacency matrix `graph` (n x n)
+# Output: prints a Hamiltonian path if found and returns TRUE, otherwise prints
+#         a message and returns FALSE
+
+# Function to check if vertex v can be added to path at position pos
+isSafe <- function(v, graph, path, pos) {
+  # Check adjacency between current vertex and previous vertex
+  if (graph[path[pos - 1], v] == 0)
+    return(FALSE)
+
+  # Check if vertex is already in path
+  if (v %in% path)
+    return(FALSE)
+
+  return(TRUE)
+}
+
+# Recursive function to find Hamiltonian path
+hamiltonianUtil <- function(graph, path, pos) {
+  n <- nrow(graph)
+
+  # Base case: if all vertices are included in the path
+  if (pos > n)
+    return(TRUE)
+
+  for (v in 1:n) {
+    if (isSafe(v, graph, path, pos)) {
+      path[pos] <- v
+
+      if (hamiltonianUtil(graph, path, pos + 1))
+        return(TRUE)
+
+      # Backtrack
+      path[pos] <- -1
+    }
+  }
+  return(FALSE)
+}
+
+# Main function to find Hamiltonian path
+hamiltonianPath <- function(graph) {
+  n <- nrow(graph)
+
+  for (start in 1:n) {
+    path <- rep(-1, n)
+    path[1] <- start
+
+    if (hamiltonianUtil(graph, path, 2)) {
+      cat("Hamiltonian Path found:\n")
+      print(path)
+      return(TRUE)
+    }
+  }
+
+  cat("No Hamiltonian Path found.\n")
+  return(FALSE)
+}
+
+# Example usage and test
+cat("=== Hamiltonian Path (Backtracking) ===\n")
+
+graph <- matrix(c(
+  0, 1, 1, 0,
+  1, 0, 1, 1,
+  1, 1, 0, 1,
+  0, 1, 1, 0
+), nrow = 4, byrow = TRUE)
+
+cat("Adjacency matrix:\n")
+print(graph)
+
+cat("\nSearching for Hamiltonian Path...\n")
+hamiltonianPath(graph)
diff --git a/mathematics/fast_fourier_transform.r b/mathematics/fast_fourier_transform.r
new file mode 100644
index 00000000..0ce03f96
--- /dev/null
+++ b/mathematics/fast_fourier_transform.r
@@ -0,0 +1,53 @@
+# Fast Fourier Transform (Cooley-Tukey recursive implementation)
+#
+# This implementation accepts a numeric or complex vector and returns
+# its discrete Fourier transform as a complex vector. If the input length
+# is not a power of two, the vector is zero-padded to the next power of two.
+#
+# Usage:
+#   source('mathematics/fast_fourier_transform.r')
+#   x <- c(0,1,2,3)
+#   fft_result <- fft_recursive(x)
+#   print(fft_result)
+
+next_power_of_two <- function(n) {
+  if (n <= 0) return(1)
+  p <- 1
+  while (p < n) p <- p * 2
+  p
+}
+
+fft_recursive <- function(x) {
+  # Ensure input is complex
+  x <- as.complex(x)
+  N <- length(x)
+
+  # Pad to next power of two if necessary
+  M <- next_power_of_two(N)
+  if (M != N) {
+    x <- c(x, rep(0+0i, M - N))
+    N <- M
+  }
+
+  if (N == 1) return(x)
+
+  even <- fft_recursive(x[seq(1, N, by = 2)])
+  odd  <- fft_recursive(x[seq(2, N, by = 2)])
+
+  factor <- exp(-2i * pi * (0:(N/2 - 1)) / N)
+  T <- factor * odd
+
+  c(even + T, even - T)
+}
+
+# Example usage when run directly with Rscript
+if (identical(Sys.getenv("R_SCRIPT_NAME"), "") && interactive()) {
+  # Running in interactive R session - show sample
+  x <- c(0, 1, 2, 3)
+  cat("Input:\n")
+  print(x)
+  cat("FFT result:\n")
+  print(fft_recursive(x))
+}
+
+# When running via Rscript, users can call: Rscript -e "source('R/mathematics/fast_fourier_transform.r'); print(fft_recursive(c(0,1,2,3)))"