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NAMESPACE
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NAMESPACE
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##useDynLib(MatrixModels, .registration=TRUE)
## Import non-base functions we need explicitly,
## notably for which we define methods
## -- prefering importMethodsFrom(., ...) where applicable
## importFrom("graphics", image)
## importFrom("utils", head, tail)
##
importFrom("stats" ## potentially all these (we import into 'lme4a'):
# , anova
, coef, coefficients # confint, cov2cor, deviance,
, fitted, fitted.values
, formula # predict, profile
, model.extract, model.matrix, model.offset, model.response, model.weights
, residuals, resid # 'resid' needed too, unfortunately..
# , simulate, terms
, update
)
## according to codetoolsBioC :: writeNamespaceImports("MatrixModels"):
importClassesFrom("methods", ANY, call, character, environment, envRefClass,
integer, list, matrix, numeric, oldClass)
importMethodsFrom("methods", coerce, show)
importFrom("methods"
, callGeneric, as, is, extends, new
, getClass, getClassDef, validObject
, setClass, setClassUnion, setOldClass, setRefClass
, setGeneric, setMethod
, setValidity, slot, "slot<-", slotNames
, signature, representation, prototype)
## Those our methods and functions use:
importMethodsFrom("Matrix", as.matrix, as.vector, coerce,
## Group Methods
"Arith", "Compare", "Logic", "Math", "Math2", "Ops", "Summary",
t, "%*%", crossprod, tcrossprod,
Cholesky, # chol, chol2inv,
solve,
summary, print,
update # notably the "CHMfactor" one
)
importClassesFrom("Matrix",
CHMfactor, CHMsimpl, CHMsuper, dCHMsimpl, dCHMsuper,
Cholesky, CholeskyFactorization,
compMatrix, corMatrix,
dgCMatrix, dgeMatrix, dMatrix,
dsparseMatrix, sparseMatrix, CsparseMatrix,
ddenseMatrix, denseMatrix,
unpackedMatrix,
generalMatrix, Matrix)
importFrom("Matrix",
.solve.dgC.chol, .solve.dgC.qr,
Diagonal, invertPerm, isLDL, sparse.model.matrix)
## Generics and functions defined in this package -------------------------
export(
"glm4",
#TODO "lm.fit.sparse",# <- "somewhat experimental"
"mkRespMod",
"model.Matrix"
, "solveCoef"
, "reweightPred"
, "updateMu"
, "updateWts"
)
exportClasses(
"modelMatrix",
"sparseModelMatrix",
"denseModelMatrix",
"dsparseModelMatrix",
"ddenseModelMatrix",
## --- linear predictor modules, containing a model matrix
"predModule",
"dPredModule",# dense &
"sPredModule",# sparse (for now)
## --- response modules, containing a response vector, etc.
"respModule", # base class and also linear model
"glmRespMod", # generalized linear models
"nlsRespMod", # nonlinear regression response
"nglmRespMod", # generalized nonlinear
"glpModel", "Model"
)
exportMethods(## for both own and "other" generics:
## re-export S4 methods, for "stats"-S3-generics:
"coef", "coefficients" ## , "cov2cor"
,"fitted", "fitted.values", "formula"
,"residuals", "resid"
,"print"# print(x, ...) when show(x) is not sufficient
,"show"
## not yet ,"summary"
)