pava: Greatest convex majorants and least concave minorants

[ gpl, library, math, statistics ] [ Propose Tags ]

Please see the README on GitHub at https://github.com/dschrempf/pava#readme


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Versions [faq] 0.1.0.0
Change log ChangeLog.md
Dependencies base (>=4.7 && <5), vector [details]
License GPL-3.0-or-later
Copyright Dominik Schrempf (2020)
Author Dominik Schrempf
Maintainer dominik.schrempf@gmail.com
Category Math, Statistics
Home page https://github.com/dschrempf/pava#readme
Bug tracker https://github.com/dschrempf/pava/issues
Source repo head: git clone https://github.com/dschrempf/pava
Uploaded by dschrempf at 2020-06-08T21:18:51Z
Distributions NixOS:0.1.0.0
Downloads 40 total (7 in the last 30 days)
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Status Hackage Matrix CI
Docs available [build log]
Last success reported on 2020-06-08 [all 1 reports]

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Readme for pava-0.1.0.0

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Pool adjacent violators algorithm

Compute greatest convex majorants and least concave minorants using the pool adjacent violators algorithm.

The pool adjacent violators algorithm (PAVA) is an iterative algorithm for solving monotonic regression problems. In particular, (antitonic) regression is the computation of a non-decreasing (non-increasing) sequence of values such that a given problem is optimized. PAVA can also be used to compute the greatest convex minorant and the least concave majorant of a given set of observables.

At the moment, greatest convex majorants and least concave minorants can be computed efficiently. More general isotonic regression is not yet supported, but may be in future releases.

Reading list

Run times

Run times with random exponential vectors of different length:

stack bench 2>&1

pava> benchmarks
Running 1 benchmarks...
Benchmark pava-bench: RUNNING...
benchmarking Greatest convex minorant/Vector of length 1e3
time                 397.8 μs   (384.9 μs .. 421.6 μs)
                     0.984 R²   (0.959 R² .. 0.999 R²)
mean                 392.7 μs   (387.4 μs .. 406.6 μs)
std dev              30.48 μs   (10.82 μs .. 55.79 μs)
variance introduced by outliers: 67% (severely inflated)

benchmarking Greatest convex minorant/Vector of length 1e4
time                 3.806 ms   (3.689 ms .. 3.892 ms)
                     0.959 R²   (0.869 R² .. 0.999 R²)
mean                 4.001 ms   (3.859 ms .. 4.631 ms)
std dev              801.4 μs   (123.5 μs .. 1.807 ms)
variance introduced by outliers: 88% (severely inflated)

benchmarking Greatest convex minorant/Vector of length 1e5
time                 39.91 ms   (38.41 ms .. 41.42 ms)
                     0.996 R²   (0.993 R² .. 0.998 R²)
mean                 40.01 ms   (39.19 ms .. 41.10 ms)
std dev              1.906 ms   (1.265 ms .. 2.777 ms)
variance introduced by outliers: 13% (moderately inflated)

Benchmark pava-bench: FINISH