polle
Policy Learning
Package for learning and evaluating (subgroup) policies via doubly robust loss functions. Policy learning methods include doubly robust blip/conditional average treatment effect learning and sequential policy tree learning. Methods for (subgroup) policy evaluation include doubly robust cross-fitting and online estimation/sequential validation. See Nordland and Holst (2022) <doi:10.48550/arXiv.2212.02335> for documentation and references.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.6.2 |
rolling linux/jammy R-4.5 | polle_1.6.2.tar.gz |
786.3 KiB |
1.6.2 |
rolling linux/noble R-4.5 | polle_1.6.2.tar.gz |
784.9 KiB |
1.6.2 |
rolling source/ R- | polle_1.6.2.tar.gz |
401.6 KiB |
1.6.2 |
latest linux/jammy R-4.5 | polle_1.6.2.tar.gz |
786.3 KiB |
1.6.2 |
latest linux/noble R-4.5 | polle_1.6.2.tar.gz |
784.9 KiB |
1.6.2 |
latest source/ R- | polle_1.6.2.tar.gz |
401.6 KiB |
1.6.2 |
2026-04-26 source/ R- | polle_1.6.2.tar.gz |
401.6 KiB |
1.6.2 |
2026-04-23 source/ R- | polle_1.6.2.tar.gz |
401.6 KiB |
1.6.2 |
2026-04-09 windows/windows R-4.5 | polle_1.6.2.zip |
798.6 KiB |
1.5 |
2025-04-20 source/ R- | polle_1.5.tar.gz |
240.7 KiB |
Dependencies (latest)
Depends
Imports
- data.table (>= 1.14.5)
- lava (>= 1.7.2.1)
- future.apply
- progressr
- methods
- policytree (>= 1.2.0)
- survival
- targeted (>= 0.6)
- DynTxRegime