SwiftyStats

SwiftyStats is a generic statistical framework completely written in Swift 4. The framework is basically a port from an existing Objective C framework I've written years ago. The original framework includes often used statistical routines. The project includes a macOS and iOS target.

Installation

clone the repo
drag the SwiftyStats.xcodeproj into your project
add "SwiftyStats"/"SwiftyStatsMobile" to "Targets" -> "Build Phases" -> "Target Dependencies "
add "SwiftyStats"/"SwiftyStatsMobile" to "Targets" -> "Build Phases" -> "Link Binary With Libraries"
add "SwiftyStats"/"SwiftyStatsMobile" to "Targets" -> "Build Phases" -> "Embed Frameworks"

SSExamine

This is the central class. SSExamine objects encapsulate your data and delivers various statistics. To initialize a new instance follow the steps below.

import SwiftyStats

// example data
let data: Array<Double> = [3.14,1.21,5.6]
// because our data are double valued items, the parameter "characterSet" is ignored
let test = SSExamine<Double>.init(withObject: data, levelOfMeasurement: .interval, characterSet: nil)
// prints out the arithmetic mean
print("\(test.arithmeticMean)")
// you can use the class to analyze strings too:
let testString = "This string must be analyzed!"
// in this case, only characters contained in CharacterSet.alphanumerics are added
let stringAnalyze = VTExamine<String>(withObject: data, levelOfMeasurement: .nominal, characterSet: CharacterSet.alphanumerics)
print("\(stringAnalyze.frequency("i")")

SSExamine objects can be stored and restored:

do {
	try myExamineObject.archiveTo(filePath: "~/data/myexamine.ssexamine", overwrite: true)
}
catch {
    // error handling
}
...
do {
	newObject: SSExamine<Double> = try SSExamine<Double>.unarchiveFrom(filePath: "~/data/myexamine.ssexamine")
}
catch {
    // error handling
}

Obtainable Statistics

(This list is not exhaustive.)

sample size
length (= number of unique elements)
frequencies (absolute, relative, cumulative)
empirical cdf
means (arithmetic, geometric, harmonic, contraharmonic)
empirical dispersion measures (variance, semi variance, standard deviation, standard error)
empirical moments (central, about the origin, standardized)
mode
maximum, minimum
quantiles
...

SSHypothesisTesting

The framework implements the following tests so far:

Kolmogorov Smirnov test (one/two sample))
Anderson Darling test
Bartlett test
Levene test (with variants)
Grubbs test
ESD test (Rosner test)
t test (matched, 2-sample)
Mann Whitney U-test
Wilcoxon matched pairs test
sign test
one factor ANOVA
Tukey-Kramer post hoc test
Scheffé-Test ...

SSProbabilityDistributions

The class provides the following functions/parameters for the probability distributions listed below:

PDF
CDF
Quantile (= inverse CDF)
Parameters (kurtosis, skewness, variance, mean)

List of supported distributions:

Normal Distribution
F-Ratio Distribution
Student's T Distribution
Chi^2 Distribution
Beta Distribution
Gamma Distribution
Log Normal Distribution
Cauchy Distribution
Laplace Distribution
Pareto Distribution
Wald Distribution
Exponential Distribution
Uniform Distribution
Triangular Distribution

Inakitajes / SwiftyStats