Package: polle 1.5
polle: Policy Learning
Package for evaluating user-specified finite stage policies and learning optimal treatment policies via doubly robust loss functions. Policy learning methods include doubly robust blip/CATE learning and sequential policy tree learning. The package also include methods for optimal subgroup analysis. See Nordland and Holst (2022) <doi:10.48550/arXiv.2212.02335> for documentation and references.
Authors:
polle_1.5.tar.gz
polle_1.5.zip(r-4.5)polle_1.5.zip(r-4.4)polle_1.5.zip(r-4.3)
polle_1.5.tgz(r-4.4-any)polle_1.5.tgz(r-4.3-any)
polle_1.5.tar.gz(r-4.5-noble)polle_1.5.tar.gz(r-4.4-noble)
polle_1.5.tgz(r-4.4-emscripten)polle_1.5.tgz(r-4.3-emscripten)
polle.pdf |polle.html✨
polle/json (API)
NEWS
# Install 'polle' in R: |
install.packages('polle', repos = c('https://andreasnordland.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/andreasnordland/polle/issues
Last updated 3 months agofrom:9f83c481e3. Checks:OK: 5 NOTE: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 02 2024 |
R-4.5-win | NOTE | Nov 02 2024 |
R-4.5-linux | NOTE | Nov 02 2024 |
R-4.4-win | OK | Nov 02 2024 |
R-4.4-mac | OK | Nov 02 2024 |
R-4.3-win | OK | Nov 02 2024 |
R-4.3-mac | OK | Nov 02 2024 |
Exports:Allconditionalcontrol_blipcontrol_drqlcontrol_earlcontrol_owlcontrol_ptlcontrol_rwlcopy_policy_dataestimatefit_g_functionsg_empirg_glmg_glmnetg_rfg_slg_xgboostget_action_setget_actionsget_g_functionsget_historyget_history_namesget_idget_id_stageget_Kget_nget_policyget_policy_actionsget_policy_functionsget_policy_objectget_q_functionsget_stage_action_setsget_utilityICpartialpolicy_datapolicy_defpolicy_evalpolicy_learnq_glmq_glmnetq_rfq_slq_xgboostsim_multi_stagesim_single_stagesim_single_stage_multi_actionssim_two_stagesim_two_stage_multi_actionssubset_id
Dependencies:BHbitopscaToolsclicodetoolscvAUCdata.tabledfoptimDiceKrigingdigestDynTxRegimeforeachformatRfutile.loggerfutile.optionsfuturefuture.applygamglobalsgplotsgrfgtoolsiteratorskernlabKernSmoothlambda.rlatticelavalistenvlmtestMatrixmetsmodelObjmvtnormnloptrnnlsnumDerivoptimxparallellypolicytreepracmaprogressrR6RcppRcppArmadilloRcppEigenrgenoudROCRsandwichSQUAREMSuperLearnersurvivaltargetedtimeregzoo
Estimating and Evaluating the Optimal Subgroup
Rendered fromoptimal_subgroup.Rmd
usingknitr::rmarkdown
on Nov 02 2024.Last update: 2024-07-29
Started: 2024-07-11
policy_data
Rendered frompolicy_data.Rmd
usingknitr::rmarkdown
on Nov 02 2024.Last update: 2024-04-23
Started: 2024-04-23
policy_eval
Rendered frompolicy_eval.Rmd
usingknitr::rmarkdown
on Nov 02 2024.Last update: 2024-07-26
Started: 2024-04-23
policy_learn
Rendered frompolicy_learn.Rmd
usingknitr::rmarkdown
on Nov 02 2024.Last update: 2024-04-23
Started: 2024-04-23