Package: glmfitmiss Title: Fitting GLMs with Missing Data in Both Responses and Covariates Description: 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) , Maiti and Pradhan (2009) , Maity, Pradhan, and Das (2019) for further methodological details. Version: 2.1.0 Authors@R: c(person(given = "Vivek", family = "Pradhan", role = c("aut", "cre"), email = "vpradhan2009@gmail.com"), person(given = "Douglas", family = "Nychka", role = "aut",email = "nychka@mines.edu"), person(given = "Soutir", family = "Bandyopadhyay", role = "aut", email = "bsoutir@gmail.com")) Depends: R (>= 4.0.0) License: MIT + file LICENSE Encoding: UTF-8 LazyData: true RoxygenNote: 7.3.1 Imports: data.table (>= 1.12.8), dplyr (>= 1.0.0), abind (>= 1.4-5), MASS (>= 7.3-53), brglm2 (>= 0.7.1) Suggests: testthat (>= 3.0.0) Config/testthat/edition: 3 NeedsCompilation: no Packaged: 2026-06-20 09:57:49 UTC; root Author: Vivek Pradhan [aut, cre], Douglas Nychka [aut], Soutir Bandyopadhyay [aut] Maintainer: Vivek Pradhan Repository: https://cran.r-universe.dev Date/Publication: 2025-04-22 14:10:02 UTC RemoteUrl: https://github.com/cran/glmfitmiss RemoteRef: HEAD RemoteSha: d0ff6accd96439a8d204523285711e2ffdd967af