Groups of location parameters

Classes
class	Grp
	Base location parameter group class. More...
class	MuGrp
	Hierarchical mean. More...
class	MuGrpPEX
	"Random effect" with parameter expansion More...
class	MuGrpMiss
	Data with missing phenotype values. More...
class	MuGrpEE
	Data with measurement error. More...
class	MuGrpEEmiss
	Data with measurement error and missing phenotypes. More...
class	BetaGrpFt
	Multivariate multiple regression. More...
class	BetaGrpPEX
	Multivariate multiple regression with parameter expansion. More...
class	BetaGrpPC
	Relationship matrix regression. More...
class	BetaGrpPCpex
	Multiplicative parameter expansion for PC regression. More...
class	BetaGrpSnp
	Simple single-SNP regression class. More...
class	BetaGrpSnpCV
	Single-SNP regression with conditional variance. More...
class	BetaGrpPSR
	Single-SNP regression with partial effects. More...
class	BetaGrpSnpMiss
	Simple single-SNP regression class with missing data. More...
class	BetaGrpSnpMissCV
	Single-SNP regression with conditional variance and missing data. More...
class	BetaGrpPSRmiss
	Single-SNP regression with partial effects and missing data. More...
class	BetaGrpBVSR
	Bayesian variable selection regression. More...
class	MuBlk
	Hierarchical mean with independent blocks of traits. More...
class	BetaBlk
	Regression with independent blocks of traits. More...

Detailed Description

Location parameters grouped by common modeling features. They have common priors, common data and prior covariances, and if they are regression coefficients a common set of predictors. Values are stored in a matrix with the columns representing traits (or analogous parameters) and rows – individual location parameters. The latter are targets of MVnorm classes and are typically modified in Markov chains through update functions implemented in these individual-row objects.