.onLoad <- function(lib, pkg) {
    if(is.null(getOption("max.print")))
	options(max.print = 10000)#-> show() of large matrices
}

## ------------- Virtual Classes ----------------------------------------

# Mother class of all Matrix objects
setClass("Matrix", representation(Dim = "integer", Dimnames = "list",
				  "VIRTUAL"),
	 prototype = prototype(Dim = integer(2), Dimnames = list(NULL,NULL)),
	 validity = function(object) {
	     Dim <- object@Dim
	     if (length(Dim) != 2)
		 return("Dim slot must be of length 2")
	     if (any(Dim < 0))
		 return("Dim slot must contain non-negative values")
	     Dn <- object@Dimnames
	     if (!is.list(Dn) || length(Dn) != 2)
		 return("'Dimnames' slot must be list of length 2")
	     ## 'else'	ok :
	     TRUE
	 })

setClass("symmetricMatrix",
	 representation(uplo = "character", "VIRTUAL"),
	 contains = "Matrix")

setClass("triangularMatrix",
	 representation(uplo = "character", diag = "character",
			"VIRTUAL"), contains = "Matrix")

# Virtual class of numeric matrices
setClass("dMatrix",
	 representation(x = "numeric", "VIRTUAL"), contains = "Matrix")

# Virtual class of integer matrices
setClass("iMatrix",
	 representation(x = "integer", "VIRTUAL"), contains = "Matrix")

# Virtual class of logical matrices
setClass("lMatrix", representation("VIRTUAL"), contains = "Matrix")
## Note that logical sparse matrices do not need an x slot so the x
## slot is part of the ldenseMatrix class

# Virtual class of complex matrices
setClass("zMatrix", # letter 'z' is as in the names of Lapack subroutines
	 representation(x = "complex", "VIRTUAL"), contains = "Matrix")

# Virtual class of dense matrices
setClass("denseMatrix", representation("VIRTUAL"), contains = "Matrix")

# Virtual class of dense, numeric matrices
setClass("ddenseMatrix",
	 representation(rcond = "numeric", factors = "list", "VIRTUAL"),
	 contains = c("dMatrix", "denseMatrix"))

# Virtual class of dense, logical matrices
setClass("ldenseMatrix",
	 representation(x = "logical", factors = "list", "VIRTUAL"),
	 contains = c("lMatrix", "denseMatrix"))

## virtual SPARSE ------------

setClass("sparseMatrix", representation("VIRTUAL"), contains = "Matrix")

## general Triplet Matrices (dgT, lgT, ..):
setClass("gTMatrix", representation(i = "integer", j = "integer", "VIRTUAL"),
	 contains = "sparseMatrix")

setClass("dsparseMatrix", representation("VIRTUAL"),
	 contains = c("dMatrix", "sparseMatrix"))

setClass("lsparseMatrix", representation("VIRTUAL"),
	 contains = c("lMatrix", "sparseMatrix"))

## ------------------ Proper (non-virtual) Classes ----------------------------

##----------------------  DENSE	 -----------------------------------------

# numeric, dense, general matrices
setClass("dgeMatrix", contains = "ddenseMatrix",
	 ## checks that length( @ x) == prod( @ Dim):
	 validity = function(object) .Call("dgeMatrix_validate", object)
	 )
## i.e. "dgeMatrix" cannot be packed, but "ddenseMatrix" can ..

# numeric, dense, non-packed, triangular matrices
setClass("dtrMatrix",
	 contains = c("dgeMatrix","triangularMatrix"),
	 prototype = prototype(uplo = "U", diag = "N"),
	 validity = function(object) .Call("dtrMatrix_validate", object)
	 )

# numeric, dense, packed, triangular matrices
setClass("dtpMatrix",
	 contains = c("ddenseMatrix", "triangularMatrix"),
	 prototype = prototype(uplo = "U", diag = "N"),
	 validity = function(object) .Call("dtpMatrix_validate", object)
	 )

# numeric, dense, non-packed symmetric matrices
setClass("dsyMatrix",
	 contains = c("dgeMatrix", "symmetricMatrix"),
	 prototype = prototype(uplo = "U"),
	 validity = function(object) .Call("dsyMatrix_validate", object)
	 )

# numeric, dense, packed symmetric matrices
setClass("dspMatrix",
	 prototype = prototype(uplo = "U"),
	 contains = c("ddenseMatrix", "symmetricMatrix"),
	 validity = function(object) .Call("dspMatrix_validate", object)
	 )

# numeric, dense, non-packed, positive-definite, symmetric matrices
setClass("dpoMatrix", contains = "dsyMatrix",
	 validity = function(object) .Call("dpoMatrix_validate", object)
	 )

# numeric, dense, packed, positive-definite, symmetric matrices
setClass("dppMatrix", contains = "dspMatrix",
	 validity = function(object) .Call("dppMatrix_validate", object)
	 )

##-------------------- S P A R S E (non-virtual) --------------------------

##---------- numeric sparse matrix classes --------------------------------

# numeric, sparse, triplet general matrices
setClass("dgTMatrix",
	 representation(factors = "list"),
	 contains = c("gTMatrix", "dsparseMatrix"),
	 validity = function(object) .Call("dgTMatrix_validate", object)
	 )

# numeric, sparse, triplet triangular matrices
setClass("dtTMatrix",
	 contains = c("dgTMatrix", "triangularMatrix"),
	 prototype = prototype(uplo = "U", diag = "N"),
	 validity = function(object) .Call("dtTMatrix_validate", object)
	 )

# numeric, sparse, triplet symmetric matrices
setClass("dsTMatrix",
	 contains = c("dgTMatrix", "symmetricMatrix"),
	 prototype = prototype(uplo = "U"),
	 validity = function(object) .Call("dsTMatrix_validate", object)
	 )

# numeric, sparse, sorted compressed sparse column-oriented general matrices
setClass("dgCMatrix",
	 representation(i = "integer", p = "integer", factors = "list"),
	 contains = "dsparseMatrix",
	 prototype = prototype(p = 0:0),# to be valid
	 validity = function(object) .Call("dgCMatrix_validate", object)
	 )

# numeric, sparse, sorted compressed sparse column-oriented triangular matrices
setClass("dtCMatrix",
	 contains = c("dgCMatrix", "triangularMatrix"),
	 prototype = prototype(p = 0:0, uplo = "U", diag = "N"),# to be valid
	 validity = function(object) .Call("tsc_validate", object)
	 )

# numeric, sparse, sorted compressed sparse column-oriented symmetric matrices
setClass("dsCMatrix",
	 contains = c("dgCMatrix", "symmetricMatrix"),
	 prototype = prototype(p = 0:0, uplo = "U"),# to be valid
	 validity = function(object) .Call("dsCMatrix_validate", object)
	 )

# numeric, sparse, sorted compressed sparse row-oriented general matrices
setClass("dgRMatrix",
	 representation(j = "integer", p = "integer", factors = "list"),
	 contains = "dsparseMatrix",
	 ##TODO: validity = function(object) .Call("dgRMatrix_validate", object)
	 )

# numeric, sparse, sorted compressed sparse row-oriented triangular matrices
setClass("dtRMatrix",
	 contains = c("dgRMatrix", "triangularMatrix"),
	 ##TODO: validity = function(object) .Call("dtRMatrix_validate", object)
	 )

# numeric, sparse, sorted compressed sparse row-oriented symmetric matrices
setClass("dsRMatrix",
	 contains = c("dgRMatrix", "symmetricMatrix"),
	 ##TODO: validity = function(object) .Call("dsRMatrix_validate", object)
	 )

##---------- logical sparse matrix classes --------------------------------

## these classes are used in symbolic analysis to determine the
## locations of non-zero entries

# logical, sparse, triplet general matrices
setClass("lgTMatrix",
	 contains = c("gTMatrix", "lsparseMatrix"),
	 validity = function(object) .Call("lgTMatrix_validate", object)
	 )

# logical, sparse, triplet triangular matrices
setClass("ltTMatrix",
	 contains = c("lgTMatrix", "triangularMatrix"),
	 validity = function(object) .Call("ltTMatrix_validate", object)
	 )

# logical, sparse, triplet symmetric matrices
setClass("lsTMatrix",
	 contains = c("lgTMatrix", "symmetricMatrix"),
	 validity = function(object) .Call("lsTMatrix_validate", object)
	 )

# logical, sparse, sorted compressed sparse column-oriented general matrices
setClass("lgCMatrix",
	 representation(i = "integer", p = "integer"),
	 contains = "lsparseMatrix",
	 prototype = prototype(p = 0:0),# to be valid
	 validity = function(object) .Call("lgCMatrix_validate", object)
	 )

# logical, sparse, sorted compressed sparse column-oriented triangular matrices
setClass("ltCMatrix",
	 contains = c("lgCMatrix", "triangularMatrix"),
	 prototype = prototype(p = 0:0, uplo = "U", diag = "N"),# to be valid
	 validity = function(object) .Call("ltCMatrix_validate", object)
	 )

# logical, sparse, sorted compressed sparse column-oriented symmetric matrices
setClass("lsCMatrix",
	 contains = c("lgCMatrix", "symmetricMatrix"),
	 prototype = prototype(p = 0:0, uplo = "U"),# to be valid
	 validity = function(object) .Call("lsCMatrix_validate", object)
	 )

# logical, sparse, sorted compressed sparse row-oriented general matrices
setClass("lgRMatrix",
	 representation(j = "integer", p = "integer"),
	 contains = "lsparseMatrix",
	 validity = function(object) .Call("lgRMatrix_validate", object)
	 )

# logical, sparse, sorted compressed sparse row-oriented triangular matrices
setClass("ltRMatrix",
	 contains = c("lgRMatrix", "triangularMatrix"),
	 validity = function(object) .Call("ltRMatrix_validate", object)
	 )

# logical, sparse, sorted compressed sparse row-oriented symmetric matrices
setClass("lsRMatrix",
	 contains = c("lgRMatrix", "symmetricMatrix"),
	 validity = function(object) .Call("lsRMatrix_validate", object)
	 )

## Compressed sparse column matrix in blocks

setClass("dgBCMatrix",
	 representation(p = "integer", i = "integer", x = "array"))

## Factorization classes

setClass("Cholesky", contains = "dtrMatrix")

setClass("pCholesky", contains = "dtpMatrix")

setClass("BunchKaufman", representation(perm = "integer"), contains = "dtrMatrix",
	 validity = function(object) .Call("BunchKaufman_validate", object));

setClass("pBunchKaufman", representation(perm = "integer"), contains = "dtpMatrix",
	 validity = function(object) .Call("pBunchKaufman_validate", object));

setClass("dCholCMatrix",
	 representation(perm = "integer", Parent = "integer", D = "numeric"),
	 contains = "dtCMatrix",
	 validity = function(object) .Call("dCholCMatrix_validate", object))

setClass("lCholCMatrix",
	 representation(perm = "integer", Parent = "integer"),
	 contains = "ltCMatrix",
	 validity = function(object) .Call("lCholCMatrix_validate", object))

##-------------------- permutation ----------------------------------------

setClass("pMatrix", representation(perm = "integer"), contains = "Matrix",
	 validity = function(object) {
	     dd <- object@Dim
	     n <- dd[1]
	     perm <- object@perm
	     if (dd[2] != n) return("pMatrix must be symmetric")
	     if (length(perm) != n)
		 return(paste("length of 'perm' slot must be", n))
	     if(n > 0 &&
		!(all(range(perm) == c(1, n)) && length(unique(perm)) == n))
		 return("'perm' slot is not a valid permutation")
	     TRUE
	 })

## --------------------- non-"Matrix" Classes --------------------------------

## --- "General" (not Matrix at all) ----

## for 'i' in x[i] or A[i,] :
setClassUnion("index", members =  c("numeric", "logical", "character"))


## --- Matrix - related ----

setClass("determinant",
	 representation(modulus ="numeric",
			logarithm = "logical",
			sign = "integer",
			call = "call"))

setClass("LU", representation(x = "numeric",
			      perm = "integer"),
	 validity = function(object) .Call("LU_validate", object))

## Deprecated:
		       # positive-definite symmetric matrices as matrices
setClass("pdmatrix", contains="matrix")

#			 # factors of positive-definite symmetric matrices
# setClass("pdfactor", representation("matrix", logDet = "numeric"))

		       # correlation matrices and standard deviations
setClass("corrmatrix", representation("matrix", stdDev = "numeric"))

## -------------------- lmer-related Classes --------------------------------

setOldClass("data.frame")
setOldClass("family")
setOldClass("logLik")
setOldClass("terms")

setClass("VarCorr",
	 representation(scale="numeric",
			reSumry="list",
			useScale="logical"),
	 prototype = list(scale = 1.0, useScale = TRUE))

## mixed effects representation
setClass("mer",
	 representation(
			flist = "list", # list of grouping factors
			perm = "list",	# list of permutations of levels (0-based)
			Parent = "list",# list of Parent arrays for ZZpO
			D = "list",	# list of diagonal factors (upper triangle)
			bVar = "list",	# list of conditional variance factors (upper triangle)
			L = "list",	# list of blocks of L
			ZZpO = "list",	# list of diagonal blocks of Z'Z+Omega
			Omega = "list", # list of relative precision matrices
			method = "character", # parameter estimation method
			RXX = "matrix", # Augmented RXX component or its inverse
			RZX = "matrix", # Augmented RZX component or its inverse
			XtX = "matrix", # Original X'X matrix
			ZtZ = "list",	# list of blocks of Z'Z
			ZtX = "matrix", # Original Z'X matrix
			cnames = "list",# column names of model matrices
			devComp = "numeric", # Components of deviance
			deviance = "numeric", # Current deviance (ML and REML)
			nc = "integer", # number of columns in (augmented)
					# model matrices and number of observations
			Gp = "integer", # Pointers to groups of rows in RZX
			status = "logical"
			),
	 validity = function(object) {
	     .Call("lmer_validate", object, PACKAGE = "Matrix")
	 })

## Representation of a linear or generalized linear mixed effects model
setClass("lmer",
	 representation(assign = "integer", call = "call",
			family = "family", fitted = "numeric",
			fixed = "numeric", frame = "data.frame",
			logLik = "logLik", residuals = "numeric",
			terms = "terms"),
	 contains = "mer")

## Representation of a generalized linear mixed effects model
##setClass("glmer",
##	   representation(family = "family", glmmll = "numeric", fixed = "numeric"),
##	   contains = "lmer")

setClass("summary.lmer",
	 representation(useScale="logical",
			showCorrelation="logical"),
	 contains = "lmer")

setClass("lmer.ranef",
	 representation(varFac = "list", stdErr = "numeric"),
	 contains = "list")

setClass("lmer.ranef.confint", contains = "list")

setClass("lmer.coef",
	 representation(varFac = "list", stdErr = "numeric"),
	 contains = "list")