diff --git a/Cargo.toml b/Cargo.toml
index a4ab680..779ba9e 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -8,7 +8,7 @@ homepage = "https://github.com/BurntSushi/rust-stats"
 repository = "https://github.com/BurntSushi/rust-stats"
 readme = "README.md"
 keywords = ["statistics", "stats", "median", "mean", "stddev"]
-license = "Unlicense/MIT"
+license = "Unlicense OR MIT"
 
 [lib]
 name = "stats"
diff --git a/README.md b/README.md
index a426827..8e18b68 100644
--- a/README.md
+++ b/README.md
@@ -3,9 +3,9 @@ some support for computing them efficiently on *streams* of data. The intent
 is to permit parallel computation of statistics on large data sets.
 
 [![Build status](https://api.travis-ci.org/BurntSushi/rust-stats.png)](https://travis-ci.org/BurntSushi/rust-stats)
-[![](http://meritbadge.herokuapp.com/streaming-stats)](https://crates.io/crates/streaming-stats)
+[![crates.io](https://img.shields.io/crates/v/streaming-stats.svg)](https://crates.io/crates/streaming-stats)
 
-Dual-licensed under MIT or the [UNLICENSE](http://unlicense.org).
+Dual-licensed under MIT or the [UNLICENSE](https://unlicense.org/).
 
 
 ### Documentation
diff --git a/src/online.rs b/src/online.rs
index 6837533..53cabc5 100644
--- a/src/online.rs
+++ b/src/online.rs
@@ -63,8 +63,8 @@ impl OnlineStats {
     /// Add a new sample.
     pub fn add<T: ToPrimitive>(&mut self, sample: T) {
         let sample = sample.to_f64().unwrap();
-        // Taken from: http://goo.gl/JKeqvj
-        // See also: http://goo.gl/qTtI3V
+        // Taken from: https://goo.gl/JKeqvj
+        // See also: https://goo.gl/qTtI3V
         let oldmean = self.mean;
         let prevq = self.variance * (self.size as f64);
 
@@ -89,7 +89,7 @@ impl OnlineStats {
 
 impl Commute for OnlineStats {
     fn merge(&mut self, v: OnlineStats) {
-        // Taken from: http://goo.gl/iODi28
+        // Taken from: https://goo.gl/iODi28
         let (s1, s2) = (self.size as f64, v.size as f64);
         let meandiffsq = (self.mean - v.mean) * (self.mean - v.mean);
         let mean = ((s1 * self.mean) + (s2 * v.mean)) / (s1 + s2);