Fitting GLMs with Missing Data in Both Responses and Covariates
Function to check if any character variables exist in a formula and sh...
Data Augmentation Function
Fitting binary regression with missing categorical covariates using Ex...
Fits binary regression models with both nonignorable missing responses...
Fitting binary regression with missing responses that are nonignorable...
Fitting binary regression with missing categorical covariates using li...
Fitting binary regression model with missing responses based on Ibrahi...
Fitting generalized linear models with Incomplete data
formula generation
glmfitmiss: Fitting Binary Regression Models with Missing Data
Fitting binary regression with missing categorical covariates using ne...
Fitting binary regression with missing categorical covariates using ne...
Simulate data with independent categorical covariates
Simulate data based on an input covariate data
Simulate missing covariate or missing responses data based on an input...
This function performs data augmentation on the provided dataset.
Fits generalized linear models (GLMs) when there is missing data in both the response and categorical covariates. The functions implement likelihood-based methods using the Expectation and Maximization (EM) algorithm and optionally apply Firth’s bias correction for improved inference. See Pradhan, Nychka, and Bandyopadhyay (2025) <https:>, Maiti and Pradhan (2009) <doi:10.1111/j.1541-0420.2008.01186.x>, Maity, Pradhan, and Das (2019) <doi:10.1080/00031305.2017.1407359> for further methodological details.