add a feature = "serde"

Cargo.toml

@@ -13,6 +13,7 @@ keywords = ["stats", "mean", "skewness", "kurtosis", "quantile"]
 [dependencies]
 conv = { version = "0.3", default-features = false }
 quickersort = "3"
+serde = { version = "1", default-features = false, optional = true, features = ["derive"]}
 
 [dev-dependencies]
 bencher = "0.1"

src/lib.rs

@@ -69,6 +69,9 @@
 
 extern crate conv;
 extern crate quickersort;
+#[cfg(feature = "serde")]
+#[macro_use]
+extern crate serde;
 
 #[macro_use] mod macros;
 mod moments;

src/minmax.rs

@@ -25,6 +25,7 @@ fn max(a: f64, b: f64) -> f64 {
 /// println!("The minimum is {}.", a.min());
 /// ```
 #[derive(Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 pub struct Min {
     r: Reduce<fn(f64, f64) -> f64>,
 }
@@ -106,6 +107,7 @@ impl Merge for Min {
 /// assert_eq!(a.max(), 5.);
 /// ```
 #[derive(Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 pub struct Max {
     r: Reduce<fn(f64, f64) -> f64>,
 }

src/moments/kurtosis.rs

@@ -3,6 +3,7 @@
 ///
 /// This can be used to estimate the standard error of the mean.
 #[derive(Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 pub struct Kurtosis {
     /// Estimator of mean, variance and skewness.
     avg: Skewness,

src/moments/mean.rs

@@ -10,6 +10,7 @@
 /// println!("The mean is {}.", a.mean());
 /// ```
 #[derive(Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 pub struct Mean {
     /// Mean value.
     avg: f64,

src/moments/skewness.rs

@@ -3,6 +3,7 @@
 ///
 /// This can be used to estimate the standard error of the mean.
 #[derive(Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 pub struct Skewness {
     /// Estimator of mean and variance.
     avg: MeanWithError,

src/moments/variance.rs

@@ -13,6 +13,7 @@
 /// println!("The mean is {} ± {}.", a.mean(), a.error());
 /// ```
 #[derive(Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 pub struct Variance {
     /// Estimator of average.
     avg: Mean,

src/quantile.rs

@@ -10,6 +10,7 @@ use super::Estimate;
 // This uses the P² algorithm introduced here:
 // http://www.cs.wustl.edu/~jain/papers/ftp/psqr.pdf
 #[derive(Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 pub struct Quantile {
     /// Marker heights.
     q: [f64; 5],

src/reduce.rs

@@ -7,6 +7,7 @@ use super::{Estimate, Merge};
 /// Everything is calculated iteratively using constant memory, so the sequence
 /// of numbers can be an iterator.
 #[derive(Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 pub struct Reduce<F> {
     x: f64,
     reduce: F,

src/weighted_mean.rs

@@ -17,6 +17,7 @@ use super::{MeanWithError, Estimate, Merge};
 /// println!("The weighted mean is {}.", a.mean());
 /// ```
 #[derive(Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 pub struct WeightedMean  {
     /// Sum of the weights.
     weight_sum: f64,
@@ -135,6 +136,7 @@ impl Merge for WeightedMean {
 /// println!("The weighted mean is {} ± {}.", a.weighted_mean(), a.error());
 /// ```
 #[derive(Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 pub struct WeightedMeanWithError {
     /// Sum of the squares of the weights.
     weight_sum_sq: f64,