Package index • MCMCglmm

All functions

ABPvirus: Data on Acute Bee Paralysis Virus Infections in Bumblebees from Pascall et al. (2018).
BTdata: Blue Tit Data for a Quantitative Genetic Experiment
BTped: Blue Tit Pedigree
Ddivergence(): d-divergence
Dexpressions: List of unevaluated expressions for (mixed) partial derivatives of fitness with respect to linear predictors.
Dtensor(): Tensor of (mixed) partial derivatives
FullGrumpy: Full Grumpy Scores of Academics
Grumpy: Grumpy Scores of Academics
KPPM(): Kronecker Product Permutation Matrix
MCMCglmm(): Multivariate Generalised Linear Mixed Models
MCMCglmm-package: Multivariate Generalised Linear Mixed Models
PlodiaPO: Phenoloxidase measures on caterpillars of the Indian meal moth.
PlodiaR: Resistance of Indian meal moth caterpillars to the granulosis virus PiGV.
PlodiaRB: Resistance (as a binary trait) of Indian meal moth caterpillars to the granulosis virus PiGV.
Ptensor(): Tensor of Sample (Mixed) Central Moments
SShorns: Horn type and genders of Soay Sheep
Tri2M(): Lower/Upper Triangle Elements of a Matrix
at.level(): Incidence Matrix of Levels within a Factor
at.set(): Incidence Matrix of Combined Levels within a Factor
buildV(): Forms expected (co)variances for GLMMs fitted with MCMCglmm
commutation(): Commutation Matrix
dcmvnorm(): Density of a (conditional) multivariate normal variate
dprior(): Prior Density of Variance or Standard Deviation
evalDtensor(): Evaluates a list of (mixed) partial derivatives
gelman.prior(): Prior Covariance Matrix for Fixed Effects.
inverseA(): Inverse Relatedness Matrix and Phylogenetic Covariance Matrix
knorm(): (Mixed) Central Moments of a Multivariate Normal Distribution
krzanowski.test(): Krzanowski's Comparison of Subspaces
kunif(): Central Moments of a Uniform Distribution
list2bdiag(): Forms the direct sum from a list of matrices
me(): Design Matrix for Measurement Error Model
mult.memb(): Design Matrices for Multiple Membership Models
path(): Design Matrix for Path Analyses
pkk(): Probability that all multinomial categories have a non-zero count.
plot(<MCMCglmm>): Plots MCMC chains from MCMCglmm using plot.mcmc
plotsubspace(): Plots covariance matrices
posterior.ante(): Posterior distribution of ante-dependence parameters
posterior.cor(): Transforms posterior distribution of covariances into correlations
posterior.evals(): Posterior distribution of eigenvalues
posterior.inverse(): Posterior distribution of matrix inverse
posterior.mode(): Estimates the marginal parameter modes using kernel density estimation
predict(<MCMCglmm>): Predict method for GLMMs fitted with MCMCglmm
IW() IG() F() tSD(): Generator Functions for Priors in MCMCglmm
prunePed(): Pedigree pruning
rIW(): Random Generation from the Conditional Inverse Wishart Distribution
rbv(): Random Generation of MVN Breeding Values and Phylogenetic Effects
residuals(<MCMCglmm>): Residuals form a GLMM fitted with MCMCglmm
resolve_prior(): Resolves a Prior Specification in MCMCglmm
rprior(): Simulate Covariance Matrices from Prior
rtcmvnorm(): Random Generation from a Truncated Conditional Normal Distribution
rtnorm(): Random Generation from a Truncated Normal Distribution
sigma: Infection Data for Sigma Virus in Drosophila melanogaster
simulate(<MCMCglmm>): Simulate method for GLMMs fitted with MCMCglmm
sir(): Design Matrix for Simultaneous and Recursive Relationships between Responses
sm2asreml(): Converts sparseMatrix to asreml's giv format
spl(): Orthogonal Spline Design Matrix
summary(<MCMCglmm>): Summarising GLMM Fits from MCMCglmm
tufted_puffin: Diet composition of Tufted Puffin