Matrices (base package) and sparse matrices (Matrix package) can be used inside the RTMB objective function as part of the calculations. Behind the scenes these R objects are converted to AD representations when needed. AD objects have a temporary lifetime, so you probably won't see them / need to know them. The only important thing is which methods work for the objects.
# S3 method for advector
chol(x, ...)# S3 method for advector
determinant(x, logarithm = TRUE, ...)
# S4 method for adcomplex
eigen(x, symmetric, only.values = FALSE, EISPACK = FALSE)
# S4 method for advector
eigen(x, symmetric, only.values = FALSE, EISPACK = FALSE)
# S4 method for advector
svd(x, nu, nv, LINPACK = FALSE)
# S3 method for adsparse
t(x)
# S3 method for adsparse
[(x, ...)
# S3 method for adsparse
[(x, ...) <- value
# S4 method for adsparse,missing,missing
diag(x)
# S4 method for advector
expm(x)
# S4 method for adsparse
expm(x)
# S4 method for adsparse
dim(x)
# S4 method for anysparse,ad
%*%(x, y)
# S4 method for ad,anysparse
%*%(x, y)
# S4 method for adsparse,adsparse
%*%(x, y)
# S4 method for ad,ad
%*%(x, y)
# S4 method for ad,ad.
tcrossprod(x, y)
# S4 method for ad,ad.
crossprod(x, y)
# S4 method for advector
cov2cor(V)
# S4 method for ad,ad.
solve(a, b)
# S4 method for num,num.
solve(a, b)
# S4 method for anysparse,ad.
solve(a, b)
# S4 method for advector
colSums(x, na.rm, dims)
# S4 method for advector
rowSums(x, na.rm, dims)
# S3 method for advector
cbind(...)
# S3 method for advector
rbind(...)
List (vectors/values) with adcomplex components.
List (vectors/values) with advector components in symmetric case and adcomplex components otherwise.
Object of class advector with a dimension attribute for dense matrix operations; Object of class adsparse for sparse matrix operations.
matrix (sparse or dense)
As cbind
Not used
Logical; Is input matrix symmetric (Hermitian) ?
Ignored
Ignored
Ignored
Ignored
Ignored
Replacement value
matrix (sparse or dense)
Covariance matrix
matrix
matrix, vector or missing
Logical; Remove NAs while taping.
chol(advector): AD matrix cholesky
determinant(advector): AD log determinant
eigen(adcomplex): General AD eigen decomposition for complex matrices. Note that argument symmetric is not auto-detected so must be specified.
eigen(advector): AD eigen decomposition for real matrices. The non-symmetric case is redirected to the adcomplex method. Note that argument symmetric is not auto-detected so must be specified.
svd(advector): AD svd decomposition for real matrices.
t(adsparse): AD sparse matrix transpose. Re-directs to t,CsparseMatrix-method.
[: AD sparse matrix subsetting. Re-directs to [-methods.
`[`(adsparse) <- value: AD sparse matrix subset assignment. Re-directs to [<--methods.
diag(x = adsparse, nrow = missing, ncol = missing): AD sparse matrix diagonal extract. Re-directs to diag,CsparseMatrix-method.
expm(advector): AD matrix exponential
expm(adsparse): AD matrix exponential
dim(adsparse): AD sparse matrix dimension
x %*% y: AD matrix multiply
x %*% y: AD matrix multiply
x %*% y: AD matrix multiply
x %*% y: AD matrix multiply
tcrossprod(x = ad, y = ad.): AD matrix multiply
crossprod(x = ad, y = ad.): AD matrix multiply
cov2cor(advector): AD matrix cov2cor
solve(a = ad, b = ad.): AD matrix inversion and solve
solve(a = num, b = num.): AD matrix inversion and solve
solve(a = anysparse, b = ad.): Sparse AD matrix solve (not yet implemented)
colSums(advector): AD matrix (or array) colsums
rowSums(advector): AD matrix (or array) rowsums
cbind(advector): AD matrix column bind
rbind(advector): AD matrix row bind
F <- MakeTape(function(x) matrix(1:9,3,3) %*% x, numeric(3))
F$jacobian(1:3)
F <- MakeTape(function(x) Matrix::expm(matrix(x,2,2)), numeric(4))
F$jacobian(1:4)
F <- MakeTape(det, diag(2)) ## Indirectly available via 'determinant'
F$jacobian(matrix(1:4,2))
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