gerbil
Generalized Efficient Regression-Based Imputation with Latent Processes
Implements a new multiple imputation method that draws imputations from a latent joint multivariate normal model which underpins generally structured data. This model is constructed using a sequence of flexible conditional linear models that enables the resulting procedure to be efficiently implemented on high dimensional datasets in practice. See Robbins (2021) doi:10.48550/arXiv.2008.02243.
- Version0.1.9
- R version≥ 2.10
- LicenseGPL-2
- Needs compilation?No
- Last release01/12/2023
Documentation
Team
Michael Robbins
Pedro Nascimento de Lima
Show author detailsRolesContributorMax Griswold
Show author detailsRolesContributor
Insights
Last 30 days
This package has been downloaded 173 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 9 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 2,458 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 24, 2024 with 26 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
- Imports7 packages
- Suggests5 packages