Final touches

Cargo.toml

@@ -1,7 +1,17 @@
 [package]
 name = "ndarray-stats"
 version = "0.1.0"
-authors = ["Jim Turner <[email protected]>"]
+authors = ["Jim Turner <[email protected]>", "LukeMathWalker <[email protected]>"]
+
+license = "MIT/Apache-2.0"
+
+repository = "https://github.com/jturner314/ndarray-stats"
+documentation = "https://docs.rs/ndarray-stats/"
+
+description = "Statistical routines for ArrayBase, the n-dimensional array data structure provided by ndarray."
+
+keywords = ["array", "multidimensional", "statistics", "matrix", "ndarray"]
+categories = ["data-structures", "science"]
 
 [dependencies]
 ndarray = "0.12"

src/correlation.rs

@@ -2,7 +2,7 @@ use ndarray::prelude::*;
 use ndarray::Data;
 use num_traits::{Float, FromPrimitive};
 
-/// Extension trait for ArrayBase providing functions
+/// Extension trait for `ArrayBase` providing functions
 /// to compute different correlation measures.
 pub trait CorrelationExt<A, S>
 where

src/histogram/strategies.rs

@@ -21,7 +21,7 @@
 use ndarray::prelude::*;
 use ndarray::Data;
 use num_traits::{FromPrimitive, NumOps, Zero};
-use super::super::{QuantileExt, QuantileExt1d};
+use super::super::{QuantileExt, Quantile1dExt};
 use super::super::interpolate::Nearest;
 use super::{Edges, Bins};
 

src/lib.rs

@@ -1,3 +1,29 @@
+//! The [`ndarray-stats`] crate exposes statistical routines for `ArrayBase`,
+//! the *n*-dimensional array data structure provided by [`ndarray`].
+//!
+//! Currently available routines include:
+//! - [`order statistics`] (minimum, maximum, quantiles, etc.);
+//! - [`partitioning`];
+//! - [`correlation analysis`] (covariance, pearson correlation);
+//! - [`histogram computation`].
+//!
+//! Please feel free to contribute new functionality! A roadmap can be found [`here`].
+//!
+//! Our work is inspired by other existing statistical packages such as
+//! [`NumPy`] (Python) and [`StatsBase.jl`] (Julia) - any contribution bringing us closer to
+//! feature parity is more than welcome!
+//!
+//! [`ndarray-stats`]: https://github.com/jturner314/ndarray-stats/
+//! [`ndarray`]: https://github.com/rust-ndarray/ndarray
+//! [`order statistics`]: trait.QuantileExt.html
+//! [`partitioning`]: trait.Sort1dExt.html
+//! [`correlation analysis`]: trait.CorrelationExt.html
+//! [`histogram computation`]: histogram/index.html
+//! [`here`]: https://github.com/jturner314/ndarray-stats/issues/1
+//! [`NumPy`]: https://docs.scipy.org/doc/numpy-1.14.1/reference/routines.statistics.html
+//! [`StatsBase.jl`]: https://juliastats.github.io/StatsBase.jl/latest/
+
+
 #[macro_use(azip, s)]
 #[cfg_attr(test, macro_use(array))]
 extern crate ndarray;
@@ -13,7 +39,7 @@ extern crate ndarray_rand;
 extern crate quickcheck;
 
 pub use maybe_nan::{MaybeNan, MaybeNanExt};
-pub use quantile::{interpolate, QuantileExt, QuantileExt1d};
+pub use quantile::{interpolate, QuantileExt, Quantile1dExt};
 pub use sort::Sort1dExt;
 pub use correlation::CorrelationExt;
 pub use histogram::HistogramExt;

src/quantile.rs

@@ -411,8 +411,8 @@ where
     }
 }
 
-/// Quantile methods for 1-dimensional arrays.
-pub trait QuantileExt1d<A, S>
+/// Quantile methods for 1-D arrays.
+pub trait Quantile1dExt<A, S>
     where
         S: Data<Elem = A>,
 {
@@ -450,7 +450,7 @@ pub trait QuantileExt1d<A, S>
         I: Interpolate<A>;
 }
 
-impl<A, S> QuantileExt1d<A, S> for ArrayBase<S, Ix1>
+impl<A, S> Quantile1dExt<A, S> for ArrayBase<S, Ix1>
     where
         S: Data<Elem = A>,
 {