mixEMM
1.0A Mixed-Effects Model for Analyzing Cluster-Level Non-Ignorable Missing Data
Overview
Contains functions for estimating a mixed-effects model for clustered data (or batch-processed data) with cluster-level (or batch- level) missing values in the outcome, i.e., the outcomes of some clusters are either all observed or missing altogether. The model is developed for analyzing incomplete data from labeling-based quantitative proteomics experiments but is not limited to this type of data. We used an expectation conditional maximization (ECM) algorithm for model estimation. The cluster-level missingness may depend on the average value of the outcome in the cluster (missing not at random).
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1 release. R releases are shown for context.
- RR 4.3.0 released · 2023-04-21
- archivedRemoved from CRAN2022-09-06check problems were not corrected in time
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- 1.02017-06-08
- RR 3.4.0 released · 2017-04-21
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- GPL
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