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.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", factors = "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", "VIRTUAL"),
     contains = c("dMatrix", "denseMatrix"))

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

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

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

## sparse matrices in Triplet representation (dgT, lgT, ..):
setClass("TsparseMatrix", representation(i = "integer", j = "integer", "VIRTUAL"),
         contains = "sparseMatrix")

setClass("CsparseMatrix", representation(i = "integer", p = "integer", "VIRTUAL"),
         contains = "sparseMatrix")

setClass("RsparseMatrix", representation(p = "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",
         ## FIXME?
         ##> 'ddense*' before 'dge*' so it can use d* or ddense* methods
         ##> WITHOUT a coerce to dge* (losing triangularity)
         ##> gives error from callNextMethod() in crossprod() dispatch {R bug?}
     ##> contains = c("ddenseMatrix", "dgeMatrix", "triangularMatrix"),
     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",
         ## FIXME?
         ##> 'ddense*' before 'dge*' so it can use d* or ddense* methods
         ##> WITHOUT a coerce to dge* (losing triangularity)
         ##> gives error in crossprod() dispatch
     ##> contains = c("ddenseMatrix", "dgeMatrix", "symmetricMatrix"),
     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)
     )

##----- logical dense Matrices -- e.g. as result of <ddenseMatrix>  COMPARISON

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

## numeric, dense, non-packed, triangular matrices
setClass("ltrMatrix",
     contains = c("lgeMatrix", "triangularMatrix"),
     prototype = prototype(uplo = "U", diag = "N"))

## numeric, dense, packed, triangular matrices
setClass("ltpMatrix",
     contains = c("ldenseMatrix", "triangularMatrix"),
     prototype = prototype(uplo = "U", diag = "N")
     ## validity: ldense*, triangular*  should suffice
     )

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

## numeric, dense, packed symmetric matrices
setClass("lspMatrix",
     prototype = prototype(uplo = "U"),
     contains = c("ldenseMatrix", "symmetricMatrix"),
         validity = function(object) .Call("dspMatrix_validate", object)
         ## "dsp" and "lsp" have the same validate
     )

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

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

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

## Should not have dtTMatrix inherit from dgTMatrix because a dtTMatrix could
## be less than fully stored if diag = "U".  Methods for the dgTMatrix
## class would not produce correct results even though all the slots
## are present.

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

## Should not have dsTMatrix inherit from dgTMatrix because a dsTMatrix
## is not fully stored.  Methods for the dgTMatrix class would not
## produce correct results even though all the slots are present.

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

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

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

## see comments for dsTMatrix above
## numeric, sparse, sorted compressed sparse column-oriented symmetric matrices
setClass("dsCMatrix",
     contains = c("CsparseMatrix", "dsparseMatrix", "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"),
     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("TsparseMatrix", "lsparseMatrix"),
     validity = function(object) .Call("lgTMatrix_validate", object)
     )

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

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

## logical, sparse, sorted compressed sparse column-oriented general matrices
setClass("lgCMatrix",
     contains = c("lsparseMatrix", "CsparseMatrix"),
     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("lsparseMatrix", "CsparseMatrix", "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("lsparseMatrix", "CsparseMatrix", "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 = "sparseMatrix",
     validity = function(object) {
         d <- object@Dim
         if (d[2] != (n <- d[1])) return("pMatrix must be square")
         perm <- object@perm
         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
     })

### Class Union :  no inheritance, but is(*, <class>) :
setClassUnion("packedMatrix",
              members = c("dspMatrix", "dppMatrix", "dtpMatrix",
               "lspMatrix", "ltpMatrix"))


## --------------------- 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")
     })

setClass("mer2",
     representation(
            flist = "list", # list of grouping factors
                        L = "list",     # lower Cholesky factor of Z'Z + Omega
            RXX = "dtrMatrix", # RXX component
            RZX = "dgeMatrix", # RZX component
            XtX = "dpoMatrix", # Original X'X matrix
            ZtZ = "dsCMatrix", # Original Z'Z
            ZtX = "dgeMatrix", # Original Z'X matrix
                        Zty = "numeric", # Original Z'y vector
                        Xty = "numeric", # Original X'y vector
                        rZy = "numeric", 
                        rXy = "numeric",
            Omega = "list", # list of relative precision matrices
            method = "character", # parameter estimation method
            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"
            )
    )

## 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")