DLMRMV (version 1.0.0)

Distributed Linear Regression Models with Response Missing Variables

Description

As a distributed imputation strategy, the Distributed full information Multiple Imputation method is developed to impute missing response variables in distributed linear regression. The philosophy of the package is described in 'Guo' (2025) .

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Version

Install

install.packages('DLMRMV')

Monthly Downloads

133

Version

1.0.0

License

Apache License (== 2.0)

Maintainer

Guangbao Guo

Last Published

July 24th, 2025

Functions in DLMRMV (1.0.0)

EMRE

EM Algorithm for Linear Regression with Missing Data

DfiMI_lasso

Distributed Full-information Multiple Imputation (DfiMI) using LASSO

ERLS

Exponentially Weighted Recursive Least Squares with Missing Value Imputation

PPLS

Penalized Partial Least Squares (PPLS) Estimation

fiMI

fiMI: Predict Missing Response Variables using Multiple Imputation

MCEM

MCEM Algorithm for Missing Response Variables

PMMI

Predictive Mean Matching with Multiple Imputation

DERLS_InfoFilter

Distributed Exponentially Weighted Recursive Least Squares (DERLS) using Information Filter

DAVGMMI

Impute Missing Values in Response Variable Y Using Distributed AVGMMI Method (With Grouping)

DMCEM

Distributed Monte Carlo Expectation-Maximization (DMCEM) Algorithm

DfiMI

Distributed Full-information Multiple Imputation (DfiMI)

DERLS

Distributed Exponentially Weighted Recursive Least Squares (DERLS)

DCSLMI

Distributed and Consensus-Based Stochastic Linear Multiple Imputation (DCSLMI)

AVGM

Averaged Generalized Method of Moments Imputation (AVGM)

CSLMI

CSLMI: Consensus-based Stochastic Linear Multiple Imputation (Simplified Version)

DERLS_Woodbury

Distributed Exponentially Weighted Recursive Least Squares (DERLS) using Woodbury Identity

IMI

Improved Multiple Imputation (IMI) Estimation

Least Squares Estimation for Grouped Data with Ridge Regularization

FimIMI

FimIMI: Multiple Runs of Improved Multiple Imputation (IMI)

GMD

Generate Missing Data function