diff --git a/src/base/edition.rs b/src/base/edition.rs
index 84f0b571..424374b0 100644
--- a/src/base/edition.rs
+++ b/src/base/edition.rs
@@ -39,7 +39,7 @@ impl<N: Scalar + Zero, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
         where I: IntoIterator<Item = &'a usize>,
               I::IntoIter: ExactSizeIterator + Clone,
               DefaultAllocator: Allocator<N, Dynamic, C> {
-        let mut irows = irows.into_iter();
+        let irows = irows.into_iter();
         let ncols = self.data.shape().1;
         let mut res = unsafe { MatrixMN::new_uninitialized_generic(Dynamic::new(irows.len()), ncols) };
 
@@ -69,7 +69,7 @@ impl<N: Scalar + Zero, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
         where I: IntoIterator<Item = &'a usize>,
               I::IntoIter: ExactSizeIterator,
               DefaultAllocator: Allocator<N, R, Dynamic> {
-        let mut icols = icols.into_iter();
+        let icols = icols.into_iter();
         let nrows = self.data.shape().0;
         let mut res = unsafe { MatrixMN::new_uninitialized_generic(nrows, Dynamic::new(icols.len())) };
 
diff --git a/src/base/iter.rs b/src/base/iter.rs
index 2b5a12d4..8a5f10ab 100644
--- a/src/base/iter.rs
+++ b/src/base/iter.rs
@@ -5,7 +5,7 @@ use std::mem;
 
 use base::dimension::{Dim, U1};
 use base::storage::{Storage, StorageMut};
-use base::{Scalar, Matrix, MatrixSlice, MatrixSliceMut, Dynamic};
+use base::{Scalar, Matrix, MatrixSlice, MatrixSliceMut};
 
 macro_rules! iterator {
     (struct $Name:ident for $Storage:ident.$ptr: ident -> $Ptr:ty, $Ref:ty, $SRef: ty) => {
@@ -104,13 +104,14 @@ iterator!(struct MatrixIterMut for StorageMut.ptr_mut -> *mut N, &'a mut N, &'a
  *
  */
 #[derive(Clone)]
+/// An iterator through the rows of a matrix.
 pub struct RowIter<'a, N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> {
     mat: &'a Matrix<N, R, C, S>,
     curr: usize
 }
 
 impl<'a, N: Scalar, R: Dim, C: Dim, S: 'a + Storage<N, R, C>> RowIter<'a, N, R, C, S> {
-    pub fn new(mat: &'a Matrix<N, R, C, S>) -> Self {
+    pub(crate) fn new(mat: &'a Matrix<N, R, C, S>) -> Self {
         RowIter {
             mat, curr: 0
         }
@@ -151,15 +152,15 @@ impl<'a, N: Scalar, R: Dim, C: Dim, S: 'a + Storage<N, R, C>> ExactSizeIterator
 }
 
 
-
+/// An iterator through the mutable rows of a matrix.
 pub struct RowIterMut<'a, N: Scalar, R: Dim, C: Dim, S: StorageMut<N, R, C>> {
     mat: *mut Matrix<N, R, C, S>,
     curr: usize,
-    phantom: PhantomData<&'a Matrix<N, R, C, S>>
+    phantom: PhantomData<&'a mut Matrix<N, R, C, S>>
 }
 
 impl<'a, N: Scalar, R: Dim, C: Dim, S: 'a + StorageMut<N, R, C>> RowIterMut<'a, N, R, C, S> {
-    pub fn new(mat: &'a mut Matrix<N, R, C, S>) -> Self {
+    pub(crate) fn new(mat: &'a mut Matrix<N, R, C, S>) -> Self {
         RowIterMut {
             mat,
             curr: 0,
@@ -214,13 +215,14 @@ impl<'a, N: Scalar, R: Dim, C: Dim, S: 'a + StorageMut<N, R, C>> ExactSizeIterat
  *
  */
 #[derive(Clone)]
+/// An iterator through the columns of a matrix.
 pub struct ColumnIter<'a, N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> {
     mat: &'a Matrix<N, R, C, S>,
     curr: usize
 }
 
 impl<'a, N: Scalar, R: Dim, C: Dim, S: 'a + Storage<N, R, C>> ColumnIter<'a, N, R, C, S> {
-    pub fn new(mat: &'a Matrix<N, R, C, S>) -> Self {
+    pub(crate) fn new(mat: &'a Matrix<N, R, C, S>) -> Self {
         ColumnIter {
             mat, curr: 0
         }
@@ -261,15 +263,15 @@ impl<'a, N: Scalar, R: Dim, C: Dim, S: 'a + Storage<N, R, C>> ExactSizeIterator
 }
 
 
-
+/// An iterator through the mutable columns of a matrix.
 pub struct ColumnIterMut<'a, N: Scalar, R: Dim, C: Dim, S: StorageMut<N, R, C>> {
     mat: *mut Matrix<N, R, C, S>,
     curr: usize,
-    phantom: PhantomData<&'a Matrix<N, R, C, S>>
+    phantom: PhantomData<&'a mut Matrix<N, R, C, S>>
 }
 
 impl<'a, N: Scalar, R: Dim, C: Dim, S: 'a + StorageMut<N, R, C>> ColumnIterMut<'a, N, R, C, S> {
-    pub fn new(mat: &'a mut Matrix<N, R, C, S>) -> Self {
+    pub(crate) fn new(mat: &'a mut Matrix<N, R, C, S>) -> Self {
         ColumnIterMut {
             mat,
             curr: 0,
diff --git a/src/base/matrix.rs b/src/base/matrix.rs
index 61434bb9..89078943 100644
--- a/src/base/matrix.rs
+++ b/src/base/matrix.rs
@@ -247,11 +247,13 @@ impl<N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
         MatrixIter::new(&self.data)
     }
 
+    /// Iterate through the rows of this matrix.
     #[inline]
     pub fn row_iter(&self) -> RowIter<N, R, C, S> {
         RowIter::new(self)
     }
 
+    /// Iterate through the columns of this matrix.
     #[inline]
     pub fn column_iter(&self) -> ColumnIter<N, R, C, S> {
         ColumnIter::new(self)
@@ -739,7 +741,7 @@ impl<N: Scalar, R: Dim, C: Dim, S: StorageMut<N, R, C>> Matrix<N, R, C, S> {
             for i in 0..nrows {
                 unsafe {
                     let e = self.data.get_unchecked_mut(i, j);
-                    let rhs = rhs.get_unchecked(i, j);
+                    let rhs = rhs.get_unchecked((i, j));
                     *e = f(*e, *rhs)
                 }
             }
@@ -776,8 +778,8 @@ impl<N: Scalar, R: Dim, C: Dim, S: StorageMut<N, R, C>> Matrix<N, R, C, S> {
             for i in 0..nrows {
                 unsafe {
                     let e = self.data.get_unchecked_mut(i, j);
-                    let b = b.get_unchecked(i, j);
-                    let c = c.get_unchecked(i, j);
+                    let b = b.get_unchecked((i, j));
+                    let c = c.get_unchecked((i, j));
                     *e = f(*e, *b, *c)
                 }
             }
diff --git a/src/base/mod.rs b/src/base/mod.rs
index be54eb6c..0ec6311c 100644
--- a/src/base/mod.rs
+++ b/src/base/mod.rs
@@ -30,7 +30,7 @@ mod scalar;
 mod swizzle;
 mod unit;
 mod statistics;
-pub mod norm;
+mod norm;
 
 #[doc(hidden)]
 pub mod helper;
diff --git a/src/base/norm.rs b/src/base/norm.rs
index 6e392bd5..354f2ca3 100644
--- a/src/base/norm.rs
+++ b/src/base/norm.rs
@@ -1,4 +1,4 @@
-use num::{Signed, Zero};
+use num::Signed;
 use std::cmp::PartialOrd;
 
 use allocator::Allocator;
@@ -9,9 +9,14 @@ use constraint::{SameNumberOfRows, SameNumberOfColumns, ShapeConstraint};
 
 
 // FIXME: this should be be a trait on alga?
+/// A trait for abstract matrix norms.
+///
+/// This may be moved to the alga crate in the future.
 pub trait Norm<N: Scalar> {
+    /// Apply this norm to the given matrix.
     fn norm<R, C, S>(&self, m: &Matrix<N, R, C, S>) -> N
         where R: Dim, C: Dim, S: Storage<N, R, C>;
+    /// Use the metric induced by this norm to compute the metric distance between the two given matrices.
     fn metric_distance<R1, C1, S1, R2, C2, S2>(&self, m1: &Matrix<N, R1, C1, S1>, m2: &Matrix<N, R2, C2, S2>) -> N
         where R1: Dim, C1: Dim, S1: Storage<N, R1, C1>,
               R2: Dim, C2: Dim, S2: Storage<N, R2, C2>,
@@ -104,12 +109,16 @@ impl<N: Real, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
     }
 
     /// The L2 norm of this matrix.
+    ///
+    /// Use `.apply_norm` to apply a custom norm.
     #[inline]
     pub fn norm(&self) -> N {
         self.norm_squared().sqrt()
     }
 
-    /// Computes the metric distance between `self` and `rhs` using the Euclidean metric.
+    /// Compute the distance between `self` and `rhs` using the metric induced by the euclidean norm.
+    ///
+    /// Use `.apply_metric_distance` to apply a custom norm.
     #[inline]
     pub fn metric_distance<R2, C2, S2>(&self, rhs: &Matrix<N, R2, C2, S2>) -> N
         where R2: Dim, C2: Dim, S2: Storage<N, R2, C2>,
@@ -117,11 +126,13 @@ impl<N: Real, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
         self.apply_metric_distance(rhs, &EuclideanNorm)
     }
 
+    /// Uses the given `norm` to compute the norm of `self`.
     #[inline]
     pub fn apply_norm(&self, norm: &impl Norm<N>) -> N {
         norm.norm(self)
     }
 
+    /// Uses the metric induced by the given `norm` to compute the metric distance between `self` and `rhs`.
     #[inline]
     pub fn apply_metric_distance<R2, C2, S2>(&self, rhs: &Matrix<N, R2, C2, S2>, norm: &impl Norm<N>) -> N
         where R2: Dim, C2: Dim, S2: Storage<N, R2, C2>,
diff --git a/src/base/statistics.rs b/src/base/statistics.rs
index 8f95e447..106673c4 100644
--- a/src/base/statistics.rs
+++ b/src/base/statistics.rs
@@ -3,6 +3,8 @@ use storage::Storage;
 use allocator::Allocator;
 
 impl<N: Real, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
+    /// Returns a row vector where each element is the result of the application of `f` on the
+    /// corresponding column of the original matrix.
     #[inline]
     pub fn compress_rows(&self, f: impl Fn(VectorSliceN<N, R, S::RStride, S::CStride>) -> N) -> RowVectorN<N, C>
         where DefaultAllocator: Allocator<N, U1, C> {
@@ -12,12 +14,16 @@ impl<N: Real, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
 
         for i in 0..ncols.value() {
             // FIXME: avoid bound checking of column.
-            unsafe { *res.get_unchecked_mut(0, i) = f(self.column(i)); }
+            unsafe { *res.get_unchecked_mut((0, i)) = f(self.column(i)); }
         }
 
         res
     }
 
+    /// Returns a column vector where each element is the result of the application of `f` on the
+    /// corresponding column of the original matrix.
+    ///
+    /// This is the same as `self.compress_rows(f).transpose()`.
     #[inline]
     pub fn compress_rows_tr(&self, f: impl Fn(VectorSliceN<N, R, S::RStride, S::CStride>) -> N) -> VectorN<N, C>
         where DefaultAllocator: Allocator<N, C> {
@@ -33,6 +39,7 @@ impl<N: Real, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
         res
     }
 
+    /// Returns a column vector resulting from the folding of `f` on each column of this matrix.
     #[inline]
     pub fn compress_columns(&self, init: VectorN<N, R>, f: impl Fn(&mut VectorN<N, R>, VectorSliceN<N, R, S::RStride, S::CStride>)) -> VectorN<N, R>
         where DefaultAllocator: Allocator<N, R> {
@@ -52,23 +59,27 @@ impl<N: Real, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
      * Sum computation.
      *
      */
+    /// The sum of all the elements of this matrix.
     #[inline]
     pub fn sum(&self) -> N {
         self.iter().cloned().fold(N::zero(), |a, b| a + b)
     }
 
+    /// The sum of all the rows of this matrix.
     #[inline]
     pub fn row_sum(&self) -> RowVectorN<N, C>
         where DefaultAllocator: Allocator<N, U1, C> {
         self.compress_rows(|col| col.sum())
     }
 
+    /// The sum of all the rows of this matrix. The result is transposed and returned as a column vector.
     #[inline]
     pub fn row_sum_tr(&self) -> VectorN<N, C>
         where DefaultAllocator: Allocator<N, C> {
         self.compress_rows_tr(|col| col.sum())
     }
 
+    /// The sum of all the columns of this matrix.
     #[inline]
     pub fn column_sum(&self) -> VectorN<N, R>
         where DefaultAllocator: Allocator<N, R> {
@@ -83,6 +94,7 @@ impl<N: Real, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
      * Variance computation.
      *
      */
+    /// The variance of all the elements of this matrix.
     #[inline]
     pub fn variance(&self) -> N {
         if self.len() == 0 {
@@ -94,18 +106,21 @@ impl<N: Real, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
         }
     }
 
+    /// The variance of all the rows of this matrix.
     #[inline]
     pub fn row_variance(&self) -> RowVectorN<N, C>
         where DefaultAllocator: Allocator<N, U1, C> {
         self.compress_rows(|col| col.variance())
     }
 
+    /// The variance of all the rows of this matrix. The result is transposed and returned as a column vector.
     #[inline]
     pub fn row_variance_tr(&self) -> VectorN<N, C>
         where DefaultAllocator: Allocator<N, C> {
         self.compress_rows_tr(|col| col.variance())
     }
 
+    /// The variance of all the columns of this matrix.
     #[inline]
     pub fn column_variance(&self) -> VectorN<N, R>
         where DefaultAllocator: Allocator<N, R> {
@@ -130,6 +145,7 @@ impl<N: Real, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
      * Mean computation.
      *
      */
+    /// The mean of all the elements of this matrix.
     #[inline]
     pub fn mean(&self) -> N {
         if self.len() == 0 {
@@ -139,18 +155,21 @@ impl<N: Real, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
         }
     }
 
+    /// The mean of all the rows of this matrix.
     #[inline]
     pub fn row_mean(&self) -> RowVectorN<N, C>
         where DefaultAllocator: Allocator<N, U1, C> {
         self.compress_rows(|col| col.mean())
     }
 
+    /// The mean of all the rows of this matrix. The result is transposed and returned as a column vector.
     #[inline]
     pub fn row_mean_tr(&self) -> VectorN<N, C>
         where DefaultAllocator: Allocator<N, C> {
         self.compress_rows_tr(|col| col.mean())
     }
 
+    /// The mean of all the columns of this matrix.
     #[inline]
     pub fn column_mean(&self) -> VectorN<N, R>
         where DefaultAllocator: Allocator<N, R> {
diff --git a/tests/core/matrix.rs b/tests/core/matrix.rs
index 3b624c69..9c6d468a 100644
--- a/tests/core/matrix.rs
+++ b/tests/core/matrix.rs
@@ -292,8 +292,8 @@ fn push() {
     let a = Vector3::new(1.0, 2.0, 3.0);
     let expected_a = Vector4::new(1.0, 2.0, 3.0, 4.0);
 
-    let b = DVector::from_row_slice(3, &[1.0, 2.0, 3.0]);
-    let expected_b = DVector::from_row_slice(4, &[1.0, 2.0, 3.0, 4.0]);
+    let b = DVector::from_row_slice(&[1.0, 2.0, 3.0]);
+    let expected_b = DVector::from_row_slice(&[1.0, 2.0, 3.0, 4.0]);
 
     assert_eq!(a.push(4.0), expected_a);
     assert_eq!(b.push(4.0), expected_b);