PPCDT: An Optimal Subset Selection for Distributed Hypothesis Testing
In the era of big data, data redundancy and distributed characteristics present novel challenges to data analysis. This package introduces a method for estimating optimal subsets of redundant distributed data, based on PPCDT (Conjunction of Power and P-value in Distributed Settings). Leveraging PPC technology, this approach can efficiently extract valuable information from redundant distributed data and determine the optimal subset. Experimental results demonstrate that this method not only enhances data quality and utilization efficiency but also assesses its performance effectively. The philosophy of the package is described in Guo G. (2020) <doi:10.1007/s00180-020-00974-4>.
| Version: |
0.2.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
MASS, stats |
| Published: |
2024-07-08 |
| Author: |
Guangbao Guo [aut, cre, cph],
Jiarui Li [ctb] |
| Maintainer: |
Guangbao Guo <ggb11111111 at 163.com> |
| License: |
Apache License (== 2.0) |
| NeedsCompilation: |
no |
| CRAN checks: |
PPCDT results |
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