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