R: Distributed Linear Regression Models with Response Missing Variables

# Distributed Linear Regression Models with Response Missing Variables

## Documentation for package ‘DLMRMV’ version 1.0.0

- DESCRIPTION file

## Help Pages

AVGM | Averaged Generalized Method of Moments Imputation (AVGM)
CSLMI | CSLMI: Consensus-based Stochastic Linear Multiple Imputation (Simplified Version)
DAVGMMI | Impute Missing Values in Response Variable Y Using Distributed AVGMMI Method (With Grouping)
DCSLMI | Distributed and Consensus-Based Stochastic Linear Multiple Imputation (DCSLMI)
DERLS | Distributed Exponentially Weighted Recursive Least Squares (DERLS)
DERLS_InfoFilter | Distributed Exponentially Weighted Recursive Least Squares (DERLS) using Information Filter
DERLS_Woodbury | Distributed Exponentially Weighted Recursive Least Squares (DERLS) using Woodbury Identity
DfiMI | Distributed Full-information Multiple Imputation (DfiMI)
DfiMI_lasso | Distributed Full-information Multiple Imputation (DfiMI) using LASSO
DMCEM | Distributed Monte Carlo Expectation-Maximization (DMCEM) Algorithm
EMRE | EM Algorithm for Linear Regression with Missing Data
ERLS | Exponentially Weighted Recursive Least Squares with Missing Value Imputation
fiMI | fiMI: Predict Missing Response Variables using Multiple Imputation
FimIMI | FimIMI: Multiple Runs of Improved Multiple Imputation (IMI)
GMD | Generate Missing Data function
IMI | Improved Multiple Imputation (IMI) Estimation
LS | Least Squares Estimation for Grouped Data with Ridge Regularization
MCEM | MCEM Algorithm for Missing Response Variables
PMMI | Predictive Mean Matching with Multiple Imputation
PPLS | Penalized Partial Least Squares (PPLS) Estimation