matrix completion via iterative soft-thresholded svd

Documentation for package ‘softImpute’ version 1.0

%*%-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.
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
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"'
softImpute	impute missing values for a matrix via nuclear-norm regularization.
SparseplusLowRank-class	Class '"SparseplusLowRank"'
splr	create a 'SparseplusLowRank' object
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