Finds equivalence classes corresponding to a symmetric relation
    or undirected graph.  Finds total order consistent with partial order
    or directed graph (so-called topological sort).

Enables the creation of object pools, which make it less
    computationally expensive to fetch a new object. Currently the only
    supported pooled objects are 'DBI' connections.

Provides functions to simulate Pool-seq data under models of
    demographic formation and to import Pool-seq data from real populations.
    Implements two ABC algorithms for performing parameter estimation and 
    model selection using Pool-seq data. Cross-validation can also be 
    performed to assess the accuracy of ABC estimates and model choice.
    Carvalho et al., (2022) <doi:10.1111/1755-0998.13834>.

When using pooled p-values to adjust for multiple testing, there is an inherent balance that must be struck between rejection based on weak evidence spread among many tests and strong evidence in a few, explored in Salahub and Olford (2023) <arXiv:2310.16600>. This package provides functionality to compute marginal and central rejection levels and the centrality quotient for p-value pooling functions and provides implementations of the chi-squared quantile pooled p-value (described in Salahub and Oldford (2023)) and a proposal from Heard and Rubin-Delanchy (2018) <doi:10.1093/biomet/asx076> to control the quotient's value.

Pool dilution is a isotope tracer technique wherein a 
    biogeochemical pool is artifically enriched with its heavy isotopologue 
    and the gross productive and consumptive fluxes of that pool are 
    quantified by the change in pool size and isotopic composition over time. 
    This package calculates gross production and consumption rates from
    closed-system isotopic pool dilution time series data. Pool size 
    concentrations and heavy isotope (e.g., 15N) content are measured over time 
    and the model optimizes production rate (P) and the first order rate 
    constant (k) by minimizing error in the model-predicted total pool size, 
    as well as the isotopic signature. The model optimizes rates by weighting 
    information against the signal:noise ratio of concentration and heavy-
    isotope signatures using measurement precision as well as the magnitude of 
    change over time. The calculations used here are based on von Fischer and 
    Hedin (2002) <doi:10.1029/2001GB001448> with some modifications.