AuxSurvey - Survey Analysis with Auxiliary Discretized Variables

Probability surveys often use auxiliary continuous data from administrative records, but the utility of this data is diminished when it is discretized for confidentiality. We provide a set of survey estimators to make full use of information from the discretized variables. See Williams, S.Z., Zou, J., Liu, Y., Si, Y., Galea, S. and Chen, Q. (2024), Improving Survey Inference Using Administrative Records Without Releasing Individual-Level Continuous Data. Statistics in Medicine, 43: 5803-5813. <doi:10.1002/sim.10270> for details.

Last updated 6 days ago

auxilary-variablescategorical-variablessurvey-analysis

4.70 score 1 stars 5 scripts 157 downloads

SBMTrees - Sequential Imputation with Bayesian Trees Mixed-Effects Models for Longitudinal Data

Implements a sequential imputation framework using Bayesian Mixed-Effects Trees ('SBMTrees') for handling missing data in longitudinal studies. The package supports a variety of models, including non-linear relationships and non-normal random effects and residuals, leveraging Dirichlet Process priors for increased flexibility. Key features include handling Missing at Random (MAR) longitudinal data, imputation of both covariates and outcomes, and generating posterior predictive samples for further analysis. The methodology is designed for applications in epidemiology, biostatistics, and other fields requiring robust handling of missing data in longitudinal settings.

Last updated 16 days ago

bayesian-machine-learninglongitudinal-datamissing-data-imputationopenblascpp

4.40 score 1 stars 10 scripts 2 downloads