Lately, at Thought Into Design, we've been trying out Swift. This was partially so that we could write apps for iOS and OSX (or is that MacOS now?) but it is also good to learn the foibles of a new language's syntax.
Our most recent work has myself (Alan) contributing towards an open source statistics library called SigmaSwiftStatistics. It's been great fun so far and we've contributed a few descriptive functions (to be cleaned up and made better by the project's maintainer, Evgenii Neumerzhitckii) such as the coefficient of variation, geometric mean, harmonic mean, skewness, kurtosis, Hyndman & Fans 9 quantile methods and a handful of nonparametric tests like a decent mode routine and a routine to rank data.
The mode is more useful than, say, that found in SPSS because it reports not just the modal value but all the indices at which it occurs. It's been some years since I used SPSS, but when I did, I recall it only provided the first index of occurrence. Sometimes, it's useful to know everywhere that it occurs.
Anyway, we've been committing to this project and are sure you'll find it useful. It's in Cocoapods so it should be simple to use into a project.
Over the last few evenings, we've been coding up a routine for univariate analysis of variance (both within and between subjects). It's passed its initial tests (using ANOVA tables generated from SPSS) so the code is almost ready to go. We hope to get it committed sometime this weekend.
We're also keen to produce some decent nonparametric tests. When people write routines, there is a tendency to focus on tests for parametric data and tests for nonparametric data are the forgotten children. We've found nonparametric tests to be excellent in many real-life circumstances (heavily skewed or kurtotic data, no normal distribution, scales of less than 11 items if I remember my Nunally correctly) so we want to ensure they are included.
In the future, I would like to investigate putting a Swift wrapper of some kind conforming to BLAS so that a massive range of linear algebraic routines (often heavily optimised) will be available. When using Swift, I find I miss Numpy and SciPy, and a layer on top of BLAS would help bring some of that serious power over.
Here's to statistics on Swift!
Our most recent work has myself (Alan) contributing towards an open source statistics library called SigmaSwiftStatistics. It's been great fun so far and we've contributed a few descriptive functions (to be cleaned up and made better by the project's maintainer, Evgenii Neumerzhitckii) such as the coefficient of variation, geometric mean, harmonic mean, skewness, kurtosis, Hyndman & Fans 9 quantile methods and a handful of nonparametric tests like a decent mode routine and a routine to rank data.
The mode is more useful than, say, that found in SPSS because it reports not just the modal value but all the indices at which it occurs. It's been some years since I used SPSS, but when I did, I recall it only provided the first index of occurrence. Sometimes, it's useful to know everywhere that it occurs.
Anyway, we've been committing to this project and are sure you'll find it useful. It's in Cocoapods so it should be simple to use into a project.
Over the last few evenings, we've been coding up a routine for univariate analysis of variance (both within and between subjects). It's passed its initial tests (using ANOVA tables generated from SPSS) so the code is almost ready to go. We hope to get it committed sometime this weekend.
We're also keen to produce some decent nonparametric tests. When people write routines, there is a tendency to focus on tests for parametric data and tests for nonparametric data are the forgotten children. We've found nonparametric tests to be excellent in many real-life circumstances (heavily skewed or kurtotic data, no normal distribution, scales of less than 11 items if I remember my Nunally correctly) so we want to ensure they are included.
In the future, I would like to investigate putting a Swift wrapper of some kind conforming to BLAS so that a massive range of linear algebraic routines (often heavily optimised) will be available. When using Swift, I find I miss Numpy and SciPy, and a layer on top of BLAS would help bring some of that serious power over.
Here's to statistics on Swift!
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