Alpha diversity measures (skbio.diversity.alpha)

This package provides implementations of alpha diversity measures, including measures of richness, dominance, and evenness. Some functions generate confidence intervals (CIs). These functions have the suffix _ci.


ace(counts[, rare_threshold]) Calculate the ACE metric (Abundance-based Coverage Estimator).
berger_parker_d(counts) Calculate Berger-Parker dominance.
brillouin_d(counts) Calculate Brillouin index of alpha diversity.
chao1(counts[, bias_corrected]) Calculate chao1 richness estimator.
chao1_ci(counts[, bias_corrected, zscore]) Calculate chao1 confidence interval.
dominance(counts) Calculate dominance.
doubles(counts) Calculate number of double occurrences (doubletons).
enspie(counts) Calculate ENS_pie alpha diversity measure.
esty_ci(counts) Calculate Esty’s CI.
faith_pd(counts, otu_ids, tree[, validate]) Compute Faith’s phylogenetic diversity metric (PD)
fisher_alpha(counts) Calculate Fisher’s alpha, a metric of diversity.
gini_index(data[, method]) Calculate the Gini index.
goods_coverage(counts) Calculate Good’s coverage of counts.
heip_e(counts) Calculate Heip’s evenness measure.
kempton_taylor_q(counts[, lower_quantile, ...]) Calculate Kempton-Taylor Q index of alpha diversity.
lladser_ci(counts, r[, alpha, f, ci_type]) Calculate single CI of the conditional uncovered probability.
lladser_pe(counts[, r]) Calculate single point estimate of conditional uncovered probability.
margalef(counts) Calculate Margalef’s richness index.
mcintosh_d(counts) Calculate McIntosh dominance index D.
mcintosh_e(counts) Calculate McIntosh’s evenness measure E.
menhinick(counts) Calculate Menhinick’s richness index.
michaelis_menten_fit(counts[, num_repeats, ...]) Calculate Michaelis-Menten fit to rarefaction curve of observed OTUs.
observed_otus(counts) Calculate the number of distinct OTUs.
osd(counts) Calculate observed OTUs, singles, and doubles.
pielou_e(counts) Calculate Pielou’s Evenness index J’.
robbins(counts) Calculate Robbins’ estimator for the probability of unobserved outcomes.
shannon(counts[, base]) Calculate Shannon entropy of counts, default in bits.
simpson(counts) Calculate Simpson’s index.
simpson_e(counts) Calculate Simpson’s evenness measure E.
singles(counts) Calculate number of single occurrences (singletons).
strong(counts) Calculate Strong’s dominance index.