%*%-method | Class '"SparseplusLowRank"' |
as.matrix-method | Class '"Incomplete"' |
as.matrix-method | Class '"SparseplusLowRank"' |
biScale | Standardize a matrix to have optionally row means zero and variances one, and/or column means zero and variances one. |
clean.warm.start | rdname softImpute-internal |
coerce-method | Class '"Incomplete"' |
coerce-method | create a matrix of class 'Incomplete' |
colMeans-method | Class '"SparseplusLowRank"' |
colSums-method | Class '"SparseplusLowRank"' |
complete | make predictions from an svd object |
complete-method | make predictions from an svd object |
deBias | Recompute the '$d' component of a '"softImpute"' object through regression. |
dim-method | Class '"SparseplusLowRank"' |
impute | make predictions from an svd object |
Incomplete | create a matrix of class 'Incomplete' |
Incomplete-class | Class '"Incomplete"' |
lambda0 | compute the smallest value for 'lambda' such that 'softImpute(x,lambda)' returns the zero solution. |
lambda0-method | compute the smallest value for 'lambda' such that 'softImpute(x,lambda)' returns the zero solution. |
norm-method | Class '"SparseplusLowRank"' |
rowMeans-method | Class '"SparseplusLowRank"' |
rowSums-method | Class '"SparseplusLowRank"' |
simpute.als | rdname softImpute-internal |
simpute.svd | rdname softImpute-internal |
softImpute | impute missing values for a matrix via nuclear-norm regularization. |
softImpute.x.Incomplete | rdname softImpute-internal |
softImpute.x.matrix | Internal softImpute functions |
SparseplusLowRank-class | Class '"SparseplusLowRank"' |
splr | create a 'SparseplusLowRank' object |
Ssimpute.als | rdname softImpute-internal |
Ssimpute.svd | rdname softImpute-internal |
Ssvd.als | rdname softImpute-internal |
svd.als | compute a low rank soft-thresholded svd by alternating orthogonal ridge regression |
svd.als-method | compute a low rank soft-thresholded svd by alternating orthogonal ridge regression |