Package: glmfitmiss 2.1.0

Vivek Pradhan

glmfitmiss: Fitting GLMs with Missing Data in Both Responses and Covariates

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.

Authors:Vivek Pradhan [aut, cre], Douglas Nychka [aut], Soutir Bandyopadhyay [aut]

glmfitmiss_2.1.0.tar.gz
glmfitmiss_2.1.0.tar.gz(r-4.7-any)glmfitmiss_2.1.0.tar.gz(r-4.6-any)
glmfitmiss_2.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
glmfitmiss/json (API)

# Install 'glmfitmiss' in R:
install.packages('glmfitmiss', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • est - EST data - Eastern Cooperative Oncology Group clinical trials, EST 2282
  • est45 - EST data - Eastern Cooperative Oncology Group clinical trials, EST 2282
  • felinedata - Felinedata - Chlamydial Infection in Cats
  • ibrahim - Ibrahim data - Ibrahim (1990) JASA
  • incontinence - Incontinence- incontinence Data taken from brlrmr pacakge
  • meningitis - Meningitis- Meningococcal Disease Data with missing data in the response variable
  • meningitis60ymis - Meningitis60ymis- Meningococcal Disease Data with missing data in the response variable
  • metastmelanoma - Metastmelanoma - metastatic melanoma trial data
  • sixcitydata - Sixcitydata - A very well published Six city data published in many articles including Ware et al (1984), Ibrahim and Lipsitz (1996). Also avaialble in LogXact User Manual. The dataset is a longitudinal study of the health effects of air pollution (ware et al., 1984).
  • testyxm - Simulated Test Data - 'testyxm'

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 156 downloads 11 exports 26 dependencies

Last updated from:d0ff6accd9. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK160
source / vignettesOK173
linux-release-x86_64OK157
wasm-releaseOK101

Exports:emBinRegMARemBinRegMixedMARemBinRegNonIGemforbetaemilemyxmissllkmisslogRegMARsimulateCovariateDatasimulateDatasimulateMissDfYorX

Dependencies:abindbrglm2clidata.tabledplyrenrichwithgenericsgluelatticelifecyclemagrittrMASSMatrixnleqslvnnetnumDerivpillarpkgconfigR6rlangstatmodtibbletidyselectutf8vctrswithr

Readme and manuals

Help Manual

Help pageTopics
glmfitmiss: Fitting Binary Regression Models with Missing DataglmFitMiss-package glmfitmiss-package glmfitmiss
Fitting binary regression with missing categorical covariates using Expectation-Maximisation (EM) based methodemBinRegMAR
Fits binary regression models with both nonignorable missing responses and missing categorical covariates.emBinRegMixedMAR
Fitting binary regression with missing responses that are nonignorable based on Ibrahim and Lipsitz (1996)emBinRegNonIG
Fitting binary regression with missing categorical covariates using likelihood based methodemforbeta
Fitting binary regression model with missing responses based on Ibrahim and Lipsitz (1996)emil
Fitting generalized linear models with Incomplete dataemyxmiss
EST data - Eastern Cooperative Oncology Group clinical trials, EST 2282est
EST data - Eastern Cooperative Oncology Group clinical trials, EST 2282est45
felinedata - Chlamydial Infection in Catsfelinedata
ibrahim data - Ibrahim (1990) JASAibrahim
incontinence- incontinence Data taken from brlrmr pacakgeincontinence
Fitting binary regression with missing categorical covariates using new likelihood based method that does not require EM algorithmllkmiss
Fitting binary regression with missing categorical covariates using new likelihood based methodlogRegMAR
meningitis- Meningococcal Disease Data with missing data in the response variablemeningitis
meningitis60ymis- Meningococcal Disease Data with missing data in the response variablemeningitis60ymis
metastmelanoma - metastatic melanoma trial datametastmelanoma
Simulate data with independent categorical covariatessimulateCovariateData
Simulate data based on an input covariate datasimulateData
Simulate missing covariate or missing responses data based on an input covariate datasimulateMissDfYorX
sixcitydata - A very well published Six city data published in many articles including Ware et al (1984), Ibrahim and Lipsitz (1996). Also avaialble in LogXact User Manual. The dataset is a longitudinal study of the health effects of air pollution (ware et al., 1984).sixcitydata
Simulated Test Data - 'testyxm'testyxm