foldl-statistics
Statistical functions from the statistics package implemented as Folds.
See all snapshots
foldl-statistics appears in
Module documentation for 0.1.4.6
- Control
- Control.Foldl. this is achieved because foldl
Folds are
Applicative, which means that to compute, for example, the first 4 central moments as well as the count, the following could be used:
import Control.Foldl as F ... dataseries :: [Double] dataseries = ... ... let m = F.fold mean dataseries (c2,c3,c4,c5,n) = flip F.fold dataseries $ (\(c2,c3) (c4,c5) n -> (c2,c3,c4,c5,n)) <$> centralMoment 2 3 m <*> centralMoment 4 5 m <*> F.length
which traverses the data twice, once to compute the mean
m, and once to compute all the central moments and the count concurrently. This brings along with it for free the ability to compute streaming statistics, such as the mean of all data seen so far, using the
foldl's
scan function.
Where possible, care has been taken to ensure the numerical stability of the computation of statistics.
Several algorithms require the mean of the data to be known before computing the statistic, such as
skewness,
kurtosis and other
centralMoments.
There are 'fast' implementations for calculating the variance, unbiased variance and standard deviation, which can be computed without knowing the mean
a priori, but which may produce less accurate results.
Performance & Correctness
Benchmarks are included comparing performance to the statistics package. In nearly all cases, the implementations in this package perform better than those in
statistics on the same inputs, and in several cases, performing two passes (to compute the mean and another statistic) is faster than the equivalent
statistics implementation.
This speed has not come at the cost of correctness; all
Folds are tested against their
statistics counterparts to ensure the results are identical.
These results can be confirmed by running
stack build --test --bench --benchmark-arguments "--output bench.html"
which will print out the results of the tests against
statistics and then run the benchmark (this may take several minutes and is best run on a "quiet" machine which is doing very little other than running the benchmark). The results of the benchmarking are then available in the file
bench.html.
Changes
0.1.5.0
Added
histogram,
histogram'and
ordersOfMagnitude.
0.1.4.6
Relax bounds on tasty-quickcheck
0.1.4.5
Fix dependencies for GHC < 8.0
0.1.4.4
Update to foldl < 1.4
0.1.4.3
Update to statistics 0.14
0.1.4.1
- foldl >= 1.2.2 exports
meanand
variance, so hide them.
- export
lrrCount
0.1.4.0
Added monoidal interface to linear regression
0.1.3.0
Added unbiased versions of LMVSK functions
0.1.2.0
- Exposed monoidal LMVSKState
- Improved testing, including for fastLMVSK
0.1.1.0
- Add fastLMVSK (length, mean, variance, skewness and kurtosis)
- Add fastLinearReg (count, slope, (Y) intercept and correlation of
(x,y))
0.1.0.0
- Initial release