Rstats is a lean minimalistic library that only depends on anyhow (for its error handling).
Trait RStats is carefully checked and will report all kinds of errors, such as empty input.
Trait Vectors is unchecked to achieve speed, so some caution is advisable.
has statistical methods implemented for i64 and f64 vectors (and their slices).
For example, v.amean() computes the arithmetic mean of vector v of either Vec<i64> or Vec<f64> type (or their slices).
Included are:
means (arithmetic, geometric and harmonic),
standard deviations,
linearly weighted means useful for time dependent data analysis,
correlation and autocorrelation,
median and quartiles.
has basic vector algebra implemented for &[f64] slices.
You may get errors when invoking them on Vectors but that is easily remedied by using, e.g.: myvector.as_slice().
Version 0.3.0 completed the innovative Geometric Median. Removed duplicated implementations of Vector for Vec<f64>. Convert Vec arguments with .as_slice() to &[f64].
Version 0.2.5 breaking change: removed the struct NDPoints and its trait GMedian for more simplicity and generality. N-dimensional vector algebra is now included in Vectors.
Version 0.2.4 completed basic vector algebra.
Version 0.2.3 added Medoid.
Version 0.2.2 added simple vector calculations via trait Vectors. Renamed correlation -> icorrelation and fcorrelation -> correlation. As normal expectation here is for the arguments to be f64 vectors rather than i64s.
Version 0.2.1 added (slower) median for f64 vectors. When your data is discretisable to i64 and speed is important, use the integer version, as that one avoids sorting. Nota bene: data with fixed number of decimal places is easily discretised just by deleting the decimal points and finally dividing the result by the relevant scaling factor.
Version 0.2.0 introduced a breaking change from function calls: amean(&v) to trait method invocations: v.amean().
More methods will be added in future versions; in particular the geometric median of n-dimensional data, using an improved Weiszfeld's algorithm with guaranteed convergence.