diff --git a/src/linalg/qrp.rs b/src/linalg/qrp.rs
index c71ac578..c7d2f27b 100644
--- a/src/linalg/qrp.rs
+++ b/src/linalg/qrp.rs
@@ -11,10 +11,7 @@ use crate::storage::{Storage, StorageMut};
 
 use crate::geometry::Reflection;
 use crate::linalg::householder;
-
-//=============================================================================
 use crate::linalg::PermutationSequence;
-//=============================================================================
 
 /// The QRP decomposition of a general matrix.
 #[cfg_attr(feature = "serde-serialize", derive(Serialize, Deserialize))]
@@ -65,7 +62,9 @@ where DefaultAllocator: Allocator<N, R, C> + Allocator<N, R> + Allocator<N, DimM
         let (nrows, ncols) = matrix.data.shape();
         let min_nrows_ncols = nrows.min(ncols);
         let mut p = PermutationSequence::identity_generic(min_nrows_ncols);
+
         let mut diag = unsafe { MatrixMN::new_uninitialized_generic(min_nrows_ncols, U1) };
+        println!("diag: {:?}", &diag);
 
         if min_nrows_ncols.value() == 0 {
             return QRP {
@@ -76,12 +75,18 @@ where DefaultAllocator: Allocator<N, R, C> + Allocator<N, R> + Allocator<N, DimM
         }
 
         for ite in 0..min_nrows_ncols.value() {
+            let mut col_norm = Vec::new();
+            for column in matrix.column_iter() {
+                col_norm.push(column.norm_squared());
+            }
+            
             let piv = matrix.slice_range(ite.., ite..).icamax_full();
             let col_piv = piv.1 + ite;
             matrix.swap_columns(ite, col_piv);
             p.append_permutation(ite, col_piv);
 
             householder::clear_column_unchecked(&mut matrix, &mut diag[ite], ite, 0, None);
+            println!("matrix: {:?}", &matrix.data);
         }
 
         QRP {
@@ -139,7 +144,7 @@ where DefaultAllocator: Allocator<N, R, C> + Allocator<N, R> + Allocator<N, DimM
 
         res
     }
-    /// The column permutations of this decomposition.
+    /// Retrieves the column permutation of this decomposition.
     #[inline]
     pub fn p(&self) -> &PermutationSequence<DimMinimum<R, C>> {
         &self.p
@@ -151,13 +156,14 @@ where DefaultAllocator: Allocator<N, R, C> + Allocator<N, R> + Allocator<N, DimM
     ) -> (
         MatrixMN<N, R, DimMinimum<R, C>>,
         MatrixMN<N, DimMinimum<R, C>, C>,
+        PermutationSequence<DimMinimum<R, C>>,
     )
     where
         DimMinimum<R, C>: DimMin<C, Output = DimMinimum<R, C>>,
         DefaultAllocator:
-            Allocator<N, R, DimMinimum<R, C>> + Reallocator<N, R, C, DimMinimum<R, C>, C>,
+            Allocator<N, R, DimMinimum<R, C>> + Reallocator<N, R, C, DimMinimum<R, C>, C> + Allocator<(usize, usize), DimMinimum<R, C>>,
     {
-        (self.q(), self.unpack_r())
+        (self.q(), self.r(), self.p)
     }
 
     #[doc(hidden)]
@@ -300,18 +306,18 @@ where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D> +
         true
     }
 
-    // /// Computes the determinant of the decomposed matrix.
-    // pub fn determinant(&self) -> N {
-    //     let dim = self.qrp.nrows();
-    //     assert!(self.qrp.is_square(), "QRP determinant: unable to compute the determinant of a non-square matrix.");
+     /// Computes the determinant of the decomposed matrix.
+     pub fn determinant(&self) -> N {
+         let dim = self.qrp.nrows();
+         assert!(self.qrp.is_square(), "QRP determinant: unable to compute the determinant of a non-square matrix.");
 
-    //     let mut res = N::one();
-    //     for i in 0 .. dim {
-    //         res *= unsafe { *self.diag.vget_unchecked(i) };
-    //     }
+         let mut res = N::one();
+         for i in 0 .. dim {
+             res *= unsafe { *self.diag.vget_unchecked(i) };
+         }
 
-    //     res self.q_determinant()
-    // }
+         res * self.p.determinant()
+     }
 }
 
 impl<N: ComplexField, R: DimMin<C>, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S>
diff --git a/tests/linalg/qrp.rs b/tests/linalg/qrp.rs
new file mode 100644
index 00000000..78b96b74
--- /dev/null
+++ b/tests/linalg/qrp.rs
@@ -0,0 +1,156 @@
+#[cfg_attr(rustfmt, rustfmt_skip)]
+
+use na::Matrix4;
+
+#[test]
+fn qrp() {
+    let m = Matrix4::new (
+            1.0, -1.0, 2.0, 1.0,
+            -1.0, 3.0, -1.0, -1.0,
+            3.0, -5.0, 5.0, 3.0,
+            1.0, 2.0, 1.0, -2.0);
+    let qrp = m.qrp();
+    assert!(relative_eq!(qrp.determinant(), 0.0, epsilon = 1.0e-7));
+
+    let (q, r, p) = qrp.unpack();
+
+    let mut qr = q * r;
+    p.inv_permute_columns(& mut qr);
+
+    assert!(relative_eq!(m, qr, epsilon = 1.0e-7));
+}
+
+#[cfg(feature = "arbitrary")]
+mod quickcheck_tests {
+    macro_rules! gen_tests(
+        ($module: ident, $scalar: ty) => {
+            mod $module {
+                use na::{DMatrix, DVector, Matrix3x5, Matrix4, Matrix4x3, Matrix5x3, Vector4};
+                use std::cmp;
+                #[allow(unused_imports)]
+                use crate::core::helper::{RandScalar, RandComplex};
+    
+                quickcheck! {
+                    fn qrp(m: DMatrix<$scalar>) -> bool {
+                        let m = m.map(|e| e.0);
+                        let qrp = m.clone().qrp();
+                        let q  = qrp.q();
+                        let r  = qrp.r();
+    
+                        println!("m: {}", m);
+                        println!("qrp: {}", &q * &r);
+    
+                        relative_eq!(m, &q * r, epsilon = 1.0e-7) &&
+                        q.is_orthogonal(1.0e-7)
+                    }
+    
+                    fn qrp_static_5_3(m: Matrix5x3<$scalar>) -> bool {
+                        let m = m.map(|e| e.0);
+                        let qrp = m.qrp();
+                        let q  = qrp.q();
+                        let r  = qrp.r();
+    
+                        relative_eq!(m, q * r, epsilon = 1.0e-7) &&
+                        q.is_orthogonal(1.0e-7)
+                    }
+    
+                    fn qrp_static_3_5(m: Matrix3x5<$scalar>) -> bool {
+                        let m = m.map(|e| e.0);
+                        let qrp = m.qrp();
+                        let q  = qrp.q();
+                        let r  = qrp.r();
+
+                    relative_eq!(m, q * r, epsilon = 1.0e-7) &&
+                    q.is_orthogonal(1.0e-7)
+                }
+
+                    fn qrp_static_square(m: Matrix4<$scalar>) -> bool {
+                        let m = m.map(|e| e.0);
+                        let qrp = m.qrp();
+                        let q  = qrp.q();
+                        let r  = qrp.r();
+    
+                        println!("{}{}{}{}", q, r, q * r, m);
+    
+                        relative_eq!(m, q * r, epsilon = 1.0e-7) &&
+                        q.is_orthogonal(1.0e-7)
+                    }
+    
+                    fn qrp_solve(n: usize, nb: usize) -> bool {
+                        if n != 0 && nb != 0 {
+                            let n  = cmp::min(n, 50);  // To avoid slowing down the test too much.
+                            let nb = cmp::min(nb, 50); // To avoid slowing down the test too much.
+                            let m  = DMatrix::<$scalar>::new_random(n, n).map(|e| e.0);
+    
+                            let qrp = m.clone().qrp();
+                            let b1 = DVector::<$scalar>::new_random(n).map(|e| e.0);
+                            let b2 = DMatrix::<$scalar>::new_random(n, nb).map(|e| e.0);
+    
+                            if qrp.is_invertible() {
+                                let sol1 = qrp.solve(&b1).unwrap();
+                                let sol2 = qrp.solve(&b2).unwrap();
+    
+                                return relative_eq!(&m * sol1, b1, epsilon = 1.0e-6) &&
+                                    relative_eq!(&m * sol2, b2, epsilon = 1.0e-6)
+                            }
+                        }
+    
+                        return true;
+                    }
+    
+                    fn qrp_solve_static(m: Matrix4<$scalar>) -> bool {
+                         let m = m.map(|e| e.0);
+                         let qrp = m.qrp();
+                         let b1 = Vector4::<$scalar>::new_random().map(|e| e.0);
+                         let b2 = Matrix4x3::<$scalar>::new_random().map(|e| e.0);
+    
+                         if qrp.is_invertible() {
+                             let sol1 = qrp.solve(&b1).unwrap();
+                             let sol2 = qrp.solve(&b2).unwrap();
+    
+                             relative_eq!(m * sol1, b1, epsilon = 1.0e-6) &&
+                             relative_eq!(m * sol2, b2, epsilon = 1.0e-6)
+                         }
+                         else {
+                             false
+                         }
+                    }
+
+                    fn qrp_inverse(n: usize) -> bool {
+                        let n = cmp::max(1, cmp::min(n, 15)); // To avoid slowing down the test too much.
+                        let m = DMatrix::<$scalar>::new_random(n, n).map(|e| e.0);
+    
+                        if let Some(m1) = m.clone().qrp().try_inverse() {
+                            let id1 = &m  * &m1;
+                            let id2 = &m1 * &m;
+    
+                            id1.is_identity(1.0e-5) && id2.is_identity(1.0e-5)
+                        }
+                        else {
+                            true
+                        }
+                    }
+    
+                    fn qrp_inverse_static(m: Matrix4<$scalar>) -> bool {
+                        let m  = m.map(|e| e.0);
+                        let qrp = m.qrp();
+    
+                        if let Some(m1) = qrp.try_inverse() {
+                            let id1 = &m  * &m1;
+                            let id2 = &m1 * &m;
+    
+                            id1.is_identity(1.0e-5) && id2.is_identity(1.0e-5)
+                        }
+                        else {
+                            true
+                        }
+                    }
+                }
+            }
+        }
+    );
+
+    gen_tests!(complex, RandComplex<f64>);
+    gen_tests!(f64, RandScalar<f64>);
+}
+