Crate average

This crate provides estimators for statistics on a sequence of numbers. The typical workflow looks like this:

1. Initialize the estimator of your choice with new().
2. Add some subset (called "sample") of the sequence of numbers (called "population") for which you want to estimate the statistic, using add() or collect().
3. Calculate the statistic with mean() or similar.

You can run several estimators in parallel and merge them into one with merge().

Everything is calculated iteratively in a single pass using constant memory, so the sequence of numbers can be an iterator. The used algorithms try to avoid numerical instabilities.

Example

use average::MeanWithError;

let mut a: MeanWithError = (1..6).map(Into::into).collect();
a.add(42.);
println!("The mean is {} ± {}.", a.mean(), a.error());

Estimators

• Mean (Mean) and its error (MeanWithError).
• Weighted mean (WeightedMean) and its error (WeightedMeanWithError).
• Variance (Variance), skewness (Skewness) and kurtosis (Kurtosis).
• Quantiles (Quantile).
• Minimum (Min) and maximum (Max).

Estimating several statistics at once

The estimators are designed to have minimal state. The recommended way to calculate several of them at once is to create a struct with all the estimators you need. You can then implement add for your struct by forwarding to the underlying estimators.

Note that calculating moments requires calculating the lower moments, so you only need to include the highest moment in your struct.

Example

use average::{Min, Max};

struct MinMax {
    min: Min,
    max: Max,
}

impl MinMax {
    pub fn new() -> MinMax {
        MinMax { min: Min::new(), max: Max::new() }
    }

    pub fn add(&mut self, x: f64) {
        self.min.add(x);
        self.max.add(x);
    }

    pub fn min(&self) -> f64 {
        self.min.min()
    }

    pub fn max(&self) -> f64 {
        self.max.max()
    }
}

let mut s = MinMax::new();
for i in 1..6 {
    s.add(i as f64);
}

assert_eq!(s.min(), 1.0);
assert_eq!(s.max(), 5.0);

Macros

assert_almost_eq

Assert that two numbers are almost equal to each other.

Structs

Kurtosis	Estimate the arithmetic mean, the variance, the skewness and the kurtosis of a sequence of numbers ("population").
Max	Estimate the maximum of a sequence of numbers ("population").
Mean	Estimate the arithmetic mean of a sequence of numbers ("population").
Min	Estimate the minimum of a sequence of numbers ("population").
Quantile	Estimate the p-quantile of a sequence of numbers ("population").
Skewness	Estimate the arithmetic mean, the variance and the skewness of a sequence of numbers ("population").
Variance	Estimate the arithmetic mean and the variance of a sequence of numbers ("population").
WeightedMean	Estimate the weighted and unweighted arithmetic mean of a sequence of numbers ("population").
WeightedMeanWithError	Estimate the weighted and unweighted arithmetic mean and the unweighted variance of a sequence of numbers ("population").

Type Definitions

MeanWithError

Alias for Variance.