Package: BEDASSLE 1.6.1

BEDASSLE: Quantifies Effects of Geo/Eco Distance on Genetic Differentiation

Provides functions that allow users to quantify the relative contributions of geographic and ecological distances to empirical patterns of genetic differentiation on a landscape. Specifically, we use a custom Markov chain Monte Carlo (MCMC) algorithm, which is used to estimate the parameters of the inference model, as well as functions for performing MCMC diagnosis and assessing model adequacy.

Authors:Gideon Bradburd

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BEDASSLE.pdf |BEDASSLE.html✨
BEDASSLE/json (API)

# Install 'BEDASSLE' in R:
install.packages('BEDASSLE', repos = c('https://gbradburd.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • HGDP.bedassle.data - The Eurasian subset of the HGDP dataset used in example BEDASSLE analyses
  • mcmc.operators - Operator parameters that control the operation of the MCMC

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

49 exports 2 stars 2.86 score 12 dependencies 1 dependents 14 mentions 30 scripts 393 downloads

Last updated 8 months agofrom:72da7d622f. Checks:OK: 7. Indexed: yes.

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Doc / VignettesOKAug 28 2024
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R-4.4-winOKAug 28 2024
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R-4.3-winOKAug 28 2024
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Exports:a0_gibbs_rateBB_Likelihood_countsBB_Prior_prob_phiBB_Update_muBB_Update_phiBB_Update_thetascalculate.all.pairwise.Fstcalculate.pairwise.FstCovarianceidentify_invariant_lociInitialize.paramsLikelihood_countsLikelihood_thetaslink.up.posteriorsload_MCMC_outputload_posterior_predictive_samplesmake.continuing.paramsMCMCMCMC_BBplot_acceptance_rateplot_all_acceptance_ratesplot_all_joint_marginalsplot_all_marginalsplot_all_phi_marginalsplot_all_phi_traceplot_all_traceplot_joint_marginalplot_marginalplot_phi_marginalplot_phi_traceplot_posterior_predictive_samplesplot_traceposterior.predictive.samplePrior_prob_alpha0Prior_prob_alpha2Prior_prob_alphaDPrior_prob_alphaEPrior_prob_betaPrior_prob_muShiftsimulate_allele_count_datatransform_frequenciesUpdate_a0Update_a2Update_aDUpdate_aEUpdate_betaUpdate_muUpdate_thetas

Dependencies:bbmlebdsmatrixcodaemdbooklatticeMASSMatrixmatrixcalcmvtnormnumDerivplyrRcpp

Readme and manuals

Help Manual

Help pageTopics
Disentangling the contributions of geographic and ecological isolation to genetic differentiationBEDASSLE-package BEDASSLE
Internal BEDASSLE Functionsa0_gibbs_rate BB_Likelihood_counts BB_Prior_prob_phi BB_Update_mu BB_Update_phi BB_Update_thetas identify_invariant_loci Initialize.params Likelihood_counts Likelihood_thetas load_MCMC_output load_posterior_predictive_samples Prior_prob_alpha0 Prior_prob_alpha2 Prior_prob_alphaD Prior_prob_alphaE Prior_prob_beta Prior_prob_mu Shift simulate_allele_count_data transform_frequencies Update_a0 Update_a2 Update_aD Update_aE Update_beta Update_mu Update_thetas
Calculates unbiased pairwise Fst between all sampled populationscalculate.all.pairwise.Fst
Calculates unbiased pairwise Fst between a pair of populationscalculate.pairwise.Fst
The parametric covariance matrixCovariance
The Eurasian subset of the HGDP dataset used in example BEDASSLE analysesHGDP.bedassle.data
Links up multiple MCMC output objectslink.up.posteriors
Generates an R object containing the last parameter values of an MCMC run (to be used for a subsequent run)make.continuing.params
Runs the Markov chain Monte Carlo with the standard (Binomial) modelMCMC
Runs the Markov chain Monte Carlo with the overdispersion (Beta-Binomial) modelMCMC_BB
Operator parameters that control the operation of the MCMCmcmc.operators
Plots the acceptance rate of a parameter across MCMC generationsplot_acceptance_rate
Plots the acceptance rates of all parameters across MCMC generationsplot_all_acceptance_rates
Plots the joint marginals for all parameter pairsplot_all_joint_marginals
Plots the marginal densities for all parametersplot_all_marginals
Plot all the marginals for the phi parameters for all populationsplot_all_phi_marginals
Plots all the trace plots for the phi parameters for all populationsplot_all_phi_trace
Plots all the trace plots for all parametersplot_all_trace
Plots the joint marginal for a pair of parametersplot_joint_marginal
Plots the marginal density of a parameterplot_marginal
Plots the marginal for the phi parameter estimated in a single populationplot_phi_marginal
Plots the trace plot for the phi parameter estimated in a single populationplot_phi_trace
Plots posterior predictive samplingplot_posterior_predictive_samples
Plot the trace plot for a parameterplot_trace
Generates posterior predictive samplesposterior.predictive.sample