A General Multivariate Imputation Framework
Cubist method for imputation
Detect variable type in a data matrix
boosting tree for imputation
Boosting for regression
Impute by (educated) guessing
General Imputation Framework in R
imputeR-package description
logistic regression with lasso for imputation
LASSO for regression
Majority imputation for a vector
Calculate mixed error when the imputed matrix is mixed type
Naive imputation for mixed type data
calculate miss-classification error
Ordered boxplot for a data matrix
Principle component regression for imputation
Plot function for imputation
Partial Least Square regression for imputation
Ridge regression with lasso for imputation
Ridge shrinkage for regression
calculate the RMSE or NRMSE
classification tree for imputation
Evaluate imputation performance by simulation
Introduce some missing values into a data matrix
Best subset for classification (backward)
Best subset (backward direction) for regression
Best subset for classification (both direction)
Best subset for regression (both direction)
Best subset for classification (forward direction)
Best subset (forward direction) for regression
Multivariate Expectation-Maximization (EM) based imputation framework that offers several different algorithms. These include regularisation methods like Lasso and Ridge regression, tree-based models and dimensionality reduction methods like PCA and PLS.