miWQS
0.4.4Multiple Imputation Using Weighted Quantile Sum Regression
Overview
The miWQS package handles the uncertainty due to below the detection limit in a correlated component mixture problem. Researchers want to determine if a set/mixture of continuous and correlated components/chemicals is associated with an outcome and if so, which components are important in that mixture. These components share a common outcome but are interval-censored between zero and low thresholds, or detection limits, that may be different across the components. This package applies the multiple imputation (MI) procedure to the weighted quantile sum regression (WQS) methodology for continuous, binary, or count outcomes (Hargarten & Wheeler (2020) doi:10.1016/j.envres.2020.109466). The imputation models are: bootstrapping imputation (Lubin et.al (2004) doi:10.1289/ehp.7199), univariate Bayesian imputation (Hargarten & Wheeler (2020) doi:10.1016/j.envres.2020.109466), and multivariate Bayesian regression imputation.
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Documentation
- Examples that run
- 94%
- Documented parameters
- 96%
- Return-value docs
- 92%
- References docs
- 29%
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Code & Tests
People & History
5 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.5.0 released · 2025-04-11
- archivedRemoved from CRAN2025-03-21issues were not corrected in time
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 0.4.42021-04-02 · diff ↗
- 0.4.22021-01-21 · diff ↗
- RR 4.0.0 released · 2020-04-24
- 0.2.02019-12-12 · diff ↗
- 0.1.02019-07-31 · diff ↗
- RR 3.6.0 released · 2019-04-26
- 0.0.92018-12-23
- RR 3.5.0 released · 2018-04-23
Package metadata
- Total releases
- 5
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 115 KB / 2 files
- Download size
- not tracked yet
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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