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.

Functions

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.