Simulate the effect of management or demography on allele retention and inbreeding accumulation in bottlenecked populations of animals with overlapping generations.

Methods (<doi:10.7717/peerj.11534>) are provided of calibrating and predicting shifts in allele frequencies through redundancy analysis ('vegan::rda()') and generalized additive models ('mgcv::gam()'). Visualization functions for predicted changes in allele frequencies include 'shift.dot.ggplot()', 'shift.pie.ggplot()', 'shift.moon.ggplot()', 'shift.waffle.ggplot()' and 'shift.surf.ggplot()' that are made with input data sets that are prepared by helper functions for each visualization method. Examples in the documentation show how to prepare animated climate change graphics through a time series with the 'gganimate' package. Function 'amova.rda()' shows how Analysis of Molecular Variance can be directly conducted with the results from redundancy analysis.

Implementation of gene-level rare variant association tests targeting allelic series: genes where increasingly deleterious mutations have increasingly large phenotypic effects. The COding-variant Allelic Series Test (COAST) operates on the benign missense variants (BMVs), deleterious missense variants (DMVs), and protein truncating variants (PTVs) within a gene. COAST uses a set of adjustable weights that tailor the test towards rejecting the null hypothesis for genes where the average magnitude of effect increases monotonically from BMVs to DMVs to PTVs. See McCaw ZR, O’Dushlaine C, Somineni H, Bereket M, Klein C, Karaletsos T, Casale FP, Koller D, Soare TW. (2022) "An allelic series rare variant association test for candidate gene discovery" <doi:10.1101/2022.12.23.521658>.

Estimates and plots effect estimates from models with all possible 
    combinations of a list of variables. It can be used for assessing treatment 
    effects in clinical trials or risk factors in bio-medical and epidemiological 
    research. Like Stata command 'confall' (Wang Z (2007) <doi:10.1177/1536867X0700700203> ), 
    'allestimates' calculates and stores all effect estimates, and plots them against p values or 
    Akaike information criterion (AIC) values. It currently has functions for linear 
    regression: all_lm(), logistic and Poisson regression: all_glm(), 
    and Cox proportional hazards regression: all_cox().