| %*%-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 |