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# 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.