Modular statistics toolkit offering descriptive analysis, streaming statistics, 28 probability distributions, hypothesis tests, correlation/regression, ranking/normalization, bootstrap and weighted sampling for data analysis and simulation.
A Kotlin Multiplatform statistics toolkit covering descriptive analysis, probability distributions, hypothesis testing, correlation, regression, and sampling. Pure Kotlin, published to Maven Central as focused modules.
val data = doubleArrayOf(2.0, 4.0, 4.0, 5.0, 7.0, 9.0)
val summary = data.describe()
// => DescriptiveStatistics(count=6, mean=5.17, median=4.5, standardDeviation=2.48, ...)
data.mean() // => 5.1667
data.standardDeviation() // => 2.4833
data.skewness()
dependencies {
implementation(platform("org.oremif:kstats-bom:0.5.0"))
implementation("org.oremif:kstats-core")
// add other modules as needed
}
kotlin {
sourceSets {
commonMain.dependencies {
implementation(project.dependencies.platform("org.oremif:kstats-bom:0.5.0"))
implementation("org.oremif:kstats-core")
}
}
}
If you only need one module: implementation("org.oremif:kstats-core:0.5.0").
val data = doubleArrayOf(2.0, 4.0, 4.0, 5.0, 7.0, 9.0)
val summary = data.describe()
summary.mean
summary.median
summary.standardDeviation
stats = OnlineStatistics()
stats.addAll(doubleArrayOf(, , , , ))
stats.mean
stats.standardDeviation()
val normal = NormalDistribution(mu = 0.0, sigma = 1.0)
normal.pdf(0.0) // => 0.3989
normal.cdf(1.96) // => 0.9750
normal.quantile(0.975) // => 1.9600
normal.sample(5, Random(42)) // => [0.11, -0.87, ...]
val sample = doubleArrayOf(2.0, 4.0, 4.0, 5.0, 7.0, 9.0)
val result = tTest(sample, mu = 5.0)
result.statistic // => 0.1644
result.pValue // => 0.8759
result.isSignificant(alpha = 0.05) // => false
val x = doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0)
val y = doubleArrayOf(2.1, 3.9, 6.2, 7.8, 10.1)
val r = pearsonCorrelation(x, y)
r.coefficient
r.pValue
reg = simpleLinearRegression(x, y)
reg.slope
reg.rSquared
reg.predict()
val data = doubleArrayOf(3.0, 1.0, 4.0, 1.0, )
.rank()
.zScore()
listOf(, , , , ).bootstrapSample(, Random())
dice = WeightedDice(mapOf( to , to ))
dice.roll()
kstats targets every platform Kotlin supports.
./gradlew jvmTest # run JVM tests
./gradlew allTests # run all platform tests
./gradlew build # full build
./gradlew :benchmark:benchmark # JMH benchmarks (kstats vs Apache Commons Math)
./gradlew :benchmark:smokeBenchmark # quick smoke run
Contributions are welcome. See CONTRIBUTING.md for the contribution workflow, issue guidelines, and
project conventions. Please also read CODE_OF_CONDUCT.md before participating in issues, pull
requests, or discussions.
Licensed under the Apache License 2.0.
| Module | Description |
|---|
kstats-core | Descriptive statistics, moments, quantiles, streaming stats |
kstats-distributions | 28 probability distributions (18 continuous + 10 discrete) |
kstats-hypothesis | Parametric, non-parametric, normality, and categorical tests |
kstats-correlation | Correlation coefficients, covariance matrices, simple linear regression |
kstats-sampling | Ranking, normalization, binning, bootstrap, weighted sampling |
| Platform | Targets |
|---|
| JVM | jvm |
| Android | android, androidNativeArm32, androidNativeArm64, androidNativeX86, androidNativeX64 |
| iOS | iosArm64, iosX64, iosSimulatorArm64 |
| macOS | macosArm64 |
| watchOS | watchosArm32, watchosArm64, watchosDeviceArm64, watchosSimulatorArm64 |
| tvOS | tvosArm64, tvosSimulatorArm64 |
| Linux | linuxArm64, linuxX64 |
| Windows | mingwX64 |
| JS | js |
| Wasm | wasmJs, wasmWasi |
Surfaced from shared tags and platforms — no rankings paid for.