miWQS
Multiple Imputation Using Weighted Quantile Sum Regression
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)). The imputation models are: bootstrapping imputation (Lubin et.al (2004)), univariate Bayesian imputation (Hargarten & Wheeler (2020)), and multivariate Bayesian regression imputation.
- Version0.4.4
- R versionunknown
- LicenseGPL-3
- Needs compilation?No
- Languageen-US
- miWQS citation info
- Last release04/02/2021
Documentation
Team
Paul M. Hargarten
David C. Wheeler
Show author detailsRolesAuthor, Reviewer, Thesis advisor
Insights
Last 30 days
This package has been downloaded 127 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 4 times.
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Last 365 days
This package has been downloaded 3,160 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Jul 21, 2024 with 75 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Imports18 packages
- Suggests12 packages
- Reverse Imports1 package