diff --git a/src/bootstrap.rs b/src/bootstrap.rs
index 6384d7f..74bcaf8 100644
--- a/src/bootstrap.rs
+++ b/src/bootstrap.rs
@@ -1,6 +1,9 @@
 use rand::distributions::{Distribution, Uniform};
 use rand::Rng;
 
+/// Get a random uniform sample of `n` numbers in the range [0,n).
+/// Duplicates are explicitly allowed. The numbers are returned in
+/// sorted order.
 pub fn get_sample_inds<R: Rng + ?Sized>(n: usize, rng: &mut R) -> Vec<usize> {
     let dist = Uniform::new(0, n);
     let mut inds: Vec<usize> = dist.sample_iter(rng).take(n).collect();
diff --git a/src/em.rs b/src/em.rs
index f4b83ff..55349d8 100644
--- a/src/em.rs
+++ b/src/em.rs
@@ -65,7 +65,6 @@ fn m_step_par<'a>(
 /// Then, `curr_counts` is computed by summing over the expected assignment
 /// likelihood for all reads mapping to each target.
 #[inline]
-#[allow(dead_code)]
 fn m_step<'a, I: Iterator<Item = (&'a [AlnInfo], &'a [f32], &'a [f64])>>(
     eq_map_iter: I,
     _tinfo: &[TranscriptInfo],
@@ -103,6 +102,15 @@ fn m_step<'a, I: Iterator<Item = (&'a [AlnInfo], &'a [f32], &'a [f64])>>(
     }
 }
 
+/// The code that actually performs the EM loop in the single-threaded context.
+/// The parameters are
+/// `em_info` : an [EMInfo] struct that contains the relevant parameters and data
+/// `make_iter`: a function that returns an iterator over the alignments and conditional
+/// probabilites
+/// `do_log`: `true` if logging information is written about this EM run and false otherwise
+///
+/// returns:
+/// A [Vec<f64>] represented the expected read assignments to each transcript.
 pub fn do_em<'a, I: Iterator<Item = (&'a [AlnInfo], &'a [f32], &'a [f64])> + 'a, F: Fn() -> I>(
     em_info: &'a EMInfo,
     make_iter: F,