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.

The 2017 American College of Cardiology and American Heart
  Association blood pressure guideline recommends using 10-year predicted 
  atherosclerotic cardiovascular disease risk to guide the decision to 
  initiate or intensify antihypertensive medication. The guideline recommends 
  using the Pooled Cohort risk prediction equations to predict 10-year 
  atherosclerotic cardiovascular disease risk. This package implements the 
  original Pooled Cohort risk prediction equations and also incorporates 
  updated versions based on more contemporary data and statistical methods.

Calculates the pooled mean group (PMG) estimator for dynamic panel data models, as described by Pesaran, Shin and Smith (1999) <doi:10.1080/01621459.1999.10474156>.

Analyzing genetic data obtained from pooled samples. 
    This package can read in Fragment Analysis output files, process the data, 
    and score peaks, as well as facilitate various analyses, including cluster 
    analysis, calculation of genetic distances and diversity indices, 
    as well as bootstrap resampling for statistical inference. Specifically 
    tailored to handle genetic data efficiently, researchers can explore 
    population structure, genetic differentiation, and genetic relatedness 
    among samples. We updated some functions from Covarrubias-Pazaran et al. 
    (2016) <doi:10.1186/s12863-016-0365-6> to allow for the use of new file formats 
    and referenced the following to write our genetic analysis functions: 
    Long et al. (2022) <doi:10.1038/s41598-022-04776-0>, Jost (2008) 
    <doi:10.1111/j.1365-294x.2008.03887.x>, Nei (1973) 
    <doi:10.1073/pnas.70.12.3321>, Foulley et al. (2006) 
    <doi:10.1016/j.livprodsci.2005.10.021>, Chao et al. (2008) 
    <doi:10.1111/j.1541-0420.2008.01010.x>.