cyanea-stats

Statistical methods for life sciences: descriptive statistics, hypothesis testing, distributions, and domain-specific analysis.

What's Inside

• Descriptive statistics -- mean, median, variance, std dev, quantiles, IQR, MAD, skewness, kurtosis
• Correlation -- Pearson, Spearman rank, pairwise correlation matrices
• Hypothesis testing -- t-tests (one-sample, two-sample/Welch's), Mann-Whitney U, Fisher's exact, chi-squared, ANOVA
• Distributions -- Normal, Poisson, Binomial, ChiSquared, F, NegativeBinomial with PDF/CDF
• Multiple testing correction -- Bonferroni, Benjamini-Hochberg FDR
• Effect sizes -- Cohen's d, eta-squared, odds ratio, relative risk
• PCA -- principal component analysis (with optional BLAS acceleration)
• Bayesian conjugate priors -- Beta, Gamma, NormalConjugate, Dirichlet
• Combinatorics -- factorial, binomial coefficients, permutations, multinomial (exact and log-space)
• Population genetics -- allele frequencies, HWE, Fst (Weir-Cockerham/Hudson), nucleotide diversity, Tajima's D, linkage disequilibrium, genotype PCA
• Expression normalization -- TPM, FPKM, CPM, DESeq2 size factors
• Differential expression -- negative binomial Wald test, Wilcoxon, volcano plot data
• Enrichment analysis -- ORA (hypergeometric), GSEA (preranked with permutations), GO enrichment
• Ecological diversity -- Shannon, Simpson, Chao1, Bray-Curtis, rarefaction curves
• Survival analysis -- Kaplan-Meier, log-rank test, Cox proportional hazards

Quick Start

[dependencies]
cyanea-stats = "0.1"

use cyanea_stats::{describe, pearson, t_test_two_sample};

let stats = describe(&[1.0, 2.0, 3.0, 4.0, 5.0]).unwrap();
println!("Mean: {}, Median: {}", stats.mean, stats.median);

let r = pearson(&[1.0, 2.0, 3.0], &[2.0, 4.0, 6.0]).unwrap();
println!("Pearson r: {}", r);

let test = t_test_two_sample(&[1.0, 2.0, 3.0], &[4.0, 5.0, 6.0], false).unwrap();
println!("p = {}", test.p_value);

Feature Flags

Modules

See Also

• API Reference (STATUS.md)
• Architecture
• Build Guide