Calculate statistics using the online algorithm.
.Copyright © 2022 Michael Truog
Version: 2.0.5 Jun 20 2023 18:35:36 ------------------------------------------------------------------------
Authors: Michael Truog (mjtruog at protonmail dot com).
Calculate statistics using the online algorithm.
Philippe Pébay, Timothy B. Terriberry, Hemanth Kolla, Janine Bennett. Formulas for the Computation of Higher-Order Central Moments. Technical Report SAND2014-17343J, Sandia National Laboratories, 2014.
Pébay, Philippe. Formulas for Robust, One-Pass Parallel Computation of Covariances and Arbitrary-Order Statistical Moments. Technical Report SAND2008-6212, Sandia National Laboratories, 2008.
Welford, B. P.. Note on a method for calculating corrected sums of squares and products. Technometrics vol. 4, no. 3, pp. 419–420, 1962.describe_distribution() = normal | uniform | logistic | exponential | gamma_family | log_normal_family | undefined
describe_kurtosis() = leptokurtic | platykurtic | mesokurtic | undefined
describe_skewness() = highly_skewed | moderately_skewed | approximately_symmetric | undefined
state() = #statistics{n = non_neg_integer(), mean = float(), m2 = float(), m3 = float(), m4 = float(), minimum = float(), maximum = float(), cached_kurtosis = float() | undefined, cached_skewness = float() | undefined, cached_stddev = float() | undefined, cached_variance = float() | undefined}
count(State::state()) -> non_neg_integer()
describe_distribution(State::state()) -> {describe_distribution(), state()}
describe_kurtosis(State::state()) -> {describe_kurtosis(), state()}
describe_skewness(State::state()) -> {describe_skewness(), state()}
Only use if the sample count is large enough to cover the distribution.
(based on) Bulmer, M. G.. Principles of Statistics. Dover Publications, 1979.
maximum(State::state()) -> float()
mean(State::state()) -> float()
minimum(State::state()) -> float()
new() -> state()
normal_from_log_normal(State::state()) -> {{NormalMean::float(), NormalStdDev::float()} | undefined, state()}
normal_to_log_normal(State::state()) -> {{LogNormalMean::float(), LogNormalStdDev::float()} | undefined, state()}
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