subsampling
Optimal Subsampling Methods for Statistical Models
Balancing computational and statistical efficiency, subsampling techniques offer a practical solution for handling large-scale data analysis. Subsampling methods enhance statistical modeling for massive datasets by efficiently drawing representative subsamples from full dataset based on tailored sampling probabilities. These probabilities are optimized for specific goals, such as minimizing the variance of coefficient estimates or reducing prediction error.
- Version0.1.1
- R versionunknown
- LicenseGPL-3
- Needs compilation?Yes
- Last release11/05/2024
Documentation
- VignetteIntroduction to 'ssp.glm': Subsampling for Generalized Linear Models
- VignetteIntroduction to 'ssp.quantreg': Subsampling for Quantile Regression
- VignetteIntroduction to 'ssp.relogit': Subsampling for Logistic Regression Model with Rare Events
- VignetteIntroduction to 'ssp.softmax': Subsampling for Softmax (Multinomial) Regression Model
- MaterialREADME
- MaterialNEWS
Team
Qingkai Dong
Jun Yan
Qiang Zhang
Show author detailsRolesContributorYaqiong Yao
Show author detailsRolesAuthorHaiying Wang
Show author detailsRolesAuthor
Insights
Last 30 days
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
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Imports5 packages
- Suggests4 packages
- Linking To2 packages