Package: mdmb 1.9-22

Alexander Robitzsch

mdmb: Model Based Treatment of Missing Data

Contains model-based treatment of missing data for regression models with missing values in covariates or the dependent variable using maximum likelihood or Bayesian estimation (Ibrahim et al., 2005; <doi:10.1198/016214504000001844>; Luedtke, Robitzsch, & West, 2020a, 2020b; <doi:10.1080/00273171.2019.1640104><doi:10.1037/met0000233>). The regression model can be nonlinear (e.g., interaction effects, quadratic effects or B-spline functions). Multilevel models with missing data in predictors are available for Bayesian estimation. Substantive-model compatible multiple imputation can be also conducted.

Authors:Alexander Robitzsch [aut, cre], Oliver Luedtke [aut]

mdmb_1.9-22.tar.gz
mdmb_1.9-22.tar.gz(r-4.5-noble)mdmb_1.9-22.tar.gz(r-4.4-noble)
mdmb_1.9-22.tgz(r-4.4-emscripten)mdmb_1.9-22.tgz(r-4.3-emscripten)
mdmb.pdf |mdmb.html
mdmb/json (API)
NEWS

# Install 'mdmb' in R:
install.packages('mdmb', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/alexanderrobitzsch/mdmb/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • data.mb01 - Example Datasets for 'mdmb' Package
  • data.mb02 - Example Datasets for 'mdmb' Package
  • data.mb03 - Example Datasets for 'mdmb' Package
  • data.mb04 - Example Datasets for 'mdmb' Package
  • data.mb05 - Example Datasets for 'mdmb' Package

2.23 score 26 scripts 1.3k downloads 26 exports 73 dependencies

Last updated 5 months agofrom:5d2dada2e4. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 13 2024
R-4.5-linux-x86_64OKNov 13 2024

Exports:bc_antitrafobc_trafobct_regressiondbct_scaleddoprobitdt_scaleddyjt_scaledeval_prior_listeval_prior_list_sumlogfit_bct_scaledfit_oprobitfit_t_scaledfit_yjt_scaledfrm_emfrm_fbfrm2datlistlogistic_regressionoffset_values_extractoprobit_regressionrbct_scaledremove_NA_data_framert_scaledryjt_scaledyj_antitrafoyj_trafoyjt_regression

Dependencies:admiscbackportsbitbit64bootbroomCDMclicliprcodacodetoolscpp11crayonDBIdplyrfansiforcatsforeachgenericsglmnetgluehavenhmsiteratorsjomolatticelifecyclelme4magrittrMASSMatrixmicemiceaddsminqamitmlmitoolsmvtnormnlmenloptrnnetnumDerivordinalpanpbapplypbvpillarpkgconfigpolycorprettyunitsprogresspurrrR6RcppRcppArmadilloRcppEigenreadrrlangrpartshapesirtstringistringrsurvivalTAMtibbletidyrtidyselecttzdbucminfutf8vctrsvroomwithr

Readme and manuals

Help Manual

Help pageTopics
Model Based Treatment of Missing Datamdmb-package mdmb
Example Datasets for 'mdmb' Packagedata.mb data.mb01 data.mb02 data.mb03 data.mb04 data.mb05
Evaluates Several Prior Distributionseval_prior_list eval_prior_list_sumlog
Factored Regression Model: Generalized Linear Regression Model with Missing Covariatescoef.frm_em coef.frm_fb frm frm2datlist frm_em frm_fb logLik.frm_em plot.frm_fb summary.frm_em summary.frm_fb vcov.frm_em vcov.frm_fb
Several Regression Models with Prior Distributions and Sampling Weightsbct_regression coef.bct_regression coef.logistic_regression coef.oprobit_regression coef.yjt_regression logistic_regression logLik.bct_regression logLik.logistic_regression logLik.oprobit_regression logLik.yjt_regression oprobit_regression predict.bct_regression predict.logistic_regression predict.oprobit_regression predict.yjt_regression summary.bct_regression summary.logistic_regression summary.oprobit_regression summary.yjt_regression vcov.bct_regression vcov.logistic_regression vcov.oprobit_regression vcov.yjt_regression yjt_regression
Extracts Offset Valuesoffset_values_extract
Ordinal Probit Modelscoef.fit_oprobit doprobit fit_oprobit logLik.fit_oprobit oprobit_dist summary.fit_oprobit vcov.fit_oprobit
Removes Rows with Some Missing Entries in a Data Frameremove_NA_data_frame
Scaled t Distribution with Yeo-Johnson and Box-Cox Transformationsbc_antitrafo bc_trafo coef.fit_bct_scaled coef.fit_t_scaled coef.fit_yjt_scaled dbct_scaled dt_scaled dyjt_scaled fit_bct_scaled fit_t_scaled fit_yjt_scaled logLik.fit_bct_scaled logLik.fit_t_scaled logLik.fit_yjt_scaled rbct_scaled rt_scaled ryjt_scaled summary.fit_bct_scaled summary.fit_t_scaled summary.fit_yjt_scaled vcov.fit_bct_scaled vcov.fit_t_scaled vcov.fit_yjt_scaled yjt_dist yj_antitrafo yj_trafo