tur-stats
Statistical analysis on dataframes for Turmeric: summary statistics,
distributions, hypothesis tests, OLS regression, and resampling.
Overview
tur-stats is a Tier 1 spice (inline-C only). It builds on tur-frame to
provide a comprehensive statistical toolkit for data analysis in Turmeric.
The spice covers:
- Summary statistics: count, sum, mean, median, mode, variance, standard
deviation, min, max, range, quantiles, IQR, skewness, kurtosis
- Covariance and correlation: covariance, Pearson correlation,
Spearman rank correlation, correlation matrices
- Probability distributions: normal, t, chi-square, F, uniform, binomial,
Poisson, exponential, beta, gamma — with PDF, CDF, quantile, and random
sampling functions
- Hypothesis tests: t-tests (1-sample, 2-sample, paired), ANOVA,
chi-square goodness-of-fit, chi-square contingency, variance tests, correlation
tests, Mann-Whitney U, Wilcoxon signed-rank, Kolmogorov-Smirnov
- OLS regression: linear regression with diagnostics
- Resampling: bootstrap, permutation tests, train-test split, cross-validation
- Random number generation: various RNG utilities including shuffling
and sampling
Install
:spices {
"stats" {:url "https://github.com/turmeric-lang/turmeric-spices"
:ref "stats-v0.1.0"
:subdir "spices/stats"}
}
Quick start
(import frame/csv :refer [read-csv-string])
(import stats/summary :refer [describe])
;; Load data and get summary statistics
(let [f (read-csv-string "a,b,c\n1,2,3\n4,5,6\n7,8,9\n" 0 0 1 0 "")]
(describe f))
#lang sweet-exp
import frame/csv :refer [read-csv-string]
import stats/summary :refer [describe]
;; Load data and get summary statistics
let [f read-csv-string("a,b,c\n1,2,3\n4,5,6\n7,8,9\n" 0 0 1 0 "")]
describe(f)
(import stats/dist :refer [dnorm pnorm qnorm rnorm])
;; Work with normal distribution
(println (dnorm 0.0 0.0 1.0)) ; PDF at 0
(println (pnorm 1.96 0.0 1.0)) ; CDF at 1.96
(println (qnorm 0.975 0.0 1.0)) ; 97.5th percentile
(println (rnorm 0.0 1.0)) ; random sample
import stats/dist :refer [dnorm pnorm qnorm rnorm]
;; Work with normal distribution
println(dnorm(0.0 0.0 1.0))
println(pnorm(1.96 0.0 1.0))
println(qnorm(0.975 0.0 1.0))
println(rnorm(0.0 1.0))
(import stats/test :refer [t-test-2samp])
(import frame/frame :refer [frame])
;; Two-sample t-test
(let [a (frame "x" (list 1.0 2.0 3.0))
b (frame "x" (list 4.0 5.0 6.0))
r (t-test-2samp a b)]
(println r))
import stats/test :refer [t-test-2samp]
import frame/frame :refer [frame]
;; Two-sample t-test
let [a frame("x" list(1.0 2.0 3.0))
b frame("x" list(4.0 5.0 6.0))
r t-test-2samp(a b)]
println(r)
See also
