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MIIPW (version 0.1.2)

IPW and Mean Score Methods for Time-Course Missing Data

Description

Contains functions for data analysis of Repeated measurement using GEE. Data may contain missing value in response and covariates. For parameter estimation through Fisher Scoring algorithm, Mean Score and Inverse Probability Weighted method combining with Multiple Imputation are used when there is missing value in covariates/response. Reference for mean score method, inverse probability weighted method is Wang et al(2007).

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Version

Install

install.packages('MIIPW')

Monthly Downloads

420

Version

0.1.2

License

GPL-3

Maintainer

Atanu Bhattacharjee

Last Published

April 21st, 2025

Functions in MIIPW (0.1.2)

updateALpha

internal function for updating alpha
updateBeta

internal function for updating beta through Fisher Scoring
updateSandW

internal function for sandwich estimator
summary_ipw

summary method for ipw
srdata1

protein data
miAIPW

Fit a geeglm model using miAIPW
QICmiipw

Model Selection criteria QIC
AIPW

Fit a geeglm model using AIPW
MeanScore

Fit a geeglm model using meanScore
lmemeanscore

Fits a marginal model using meanscore
lmeaipw

Fits a marginal model using AIPW
lmeipw

Fits a marginal model using IPW
SIPW

Fit a geeglm model using SIPW
miSIPW

Fit a geeglm model using miSIPW
summary_meanscore

summary method for meanscore
print_ipw

print method for ipw
UpdatePhi

internal function for updating scale parameter
print_meanscore

print method for meanscore