MasterBayes v2.55

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ML and MCMC Methods for Pedigree Reconstruction and Analysis

The primary aim of MasterBayes is to use MCMC techniques to integrate over uncertainty in pedigree configurations estimated from molecular markers and phenotypic data. Emphasis is put on the marginal distribution of parameters that relate the phenotypic data to the pedigree. All simulation is done in compiled C++ for efficiency.

Functions in MasterBayes

Name Description
MLE.beta Maximum Likelihood Estimation of Beta
MLE.ped Maximum Likelihood Estimation of the Pedigree
GdataPed GdataPed Object
MCMCped Markov chain Monte Carlo Methods for Pedigree Reconstruction and Analysis
WarblerP Seychelles Warbler Phenotypes
autocorrP Autocorrelation Function for Parenatge Assignment
PdataPed PdataPed Object
WarblerG Seychelles Warbler Genotypes
MLE.popsize Maximum Likelihood Estimation of the Unsampled Population Size
MasterBayes Maximum Likelihood and Markov chain Monte Carlo methods for Pedigree Reconstruction, Analysis and Simulation.
genotype.list Genotype Objects for all Loci
genotypeD genotypeD Object
orderPed Orders a Pedigree
popsize.loglik Log-Likelihood of Unsampled Population Size
beta.loglik Log-Likelihood of Beta
consensusG Obtains a consensus genotype from duplicate samples
reordXlist Reorders Design Matrices
simgenotypes Genotype and Genotyping Error Simulation
legalG Legal Genotype Configurations
mismatches Parent-Offspring Genotype Mismatches
post.pairs Returns pairs of individuals that fall into specific relatedness categories
priorPed priorPed Object
summary.genotypeD genotypeD Object
tunePed tunePed Object
getXlist Design Matrices for the Multinomial Log-Linear Model
insertPed Inserts Founders into a Pedigree
modeG Posterior Mode of Genotypes
modeP Posterior Mode of Parents
extractA Allele Frequencies
fillX.G Mendelian Transition Probabilities
simpedigree Simulates a Pedigree given a Log-Linear Model
varPed Transforms Variables for a Multinomial Log-Linear Model
startPed startPed Object
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Vignettes of MasterBayes

Name
Figures/Tutorial.Rnw
Figures/buildCN.R
MasterBayes.bib
Rlogo.pdf
Tutorial-006.pdf
Tutorial-007.pdf
Tutorial-008.pdf
Tutorial-009.pdf
Tutorial-010.pdf
Tutorial-011.pdf
Tutorial-023.pdf
Tutorial-024.pdf
Tutorial-025.pdf
Tutorial-028.pdf
Tutorial-029.pdf
Tutorial-030.pdf
Tutorial-031.pdf
Tutorial-032.pdf
Tutorial-033.pdf
Tutorial-036.pdf
Tutorial-037.pdf
Tutorial-038.pdf
Tutorial-040.pdf
Tutorial-041.pdf
Tutorial-042.pdf
Tutorial-045.pdf
Tutorial-046.pdf
Tutorial-047.pdf
Tutorial-048.pdf
Tutorial-049.pdf
Tutorial-050.pdf
Tutorial-051.pdf
Tutorial-052.pdf
Tutorial-053.pdf
Tutorial-059.pdf
Tutorial-061.pdf
Tutorial-062.pdf
Tutorial-064.pdf
Tutorial-065.pdf
Tutorial-067.pdf
Tutorial-070.pdf
Tutorial-072.pdf
Tutorial-074.pdf
Tutorial-075.pdf
Tutorial-077.pdf
Tutorial-078.pdf
Tutorial-080.pdf
Tutorial-082.pdf
Tutorial-083.pdf
Tutorial-085.pdf
Tutorial-086.pdf
Tutorial-088.pdf
Tutorial-089.pdf
Tutorial-091.pdf
Tutorial-092.pdf
Tutorial-094.pdf
Tutorial-095.pdf
Tutorial-097.pdf
Tutorial-098.pdf
Tutorial-100.pdf
Tutorial-101.pdf
Tutorial-103.pdf
Tutorial-104.pdf
Tutorial-106.pdf
Tutorial-107.pdf
Tutorial-109.pdf
Tutorial.Rnw
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Details

Date 2017-07-29
License GPL (>= 2)
NeedsCompilation yes
Packaged 2017-07-29 09:26:47 UTC; jhadfiel
Repository CRAN
Date/Publication 2017-07-29 17:38:33 UTC

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