Movatterモバイル変換

polle 1.6.1

- policy learning and evaluation under right-censoring/missing outcome

polle 1.6.0

- online estimation/sequential validation for (subgroup) policy evaluation- improved functionality for subgroup analysis (subgroup average treatment effect)

polle 1.5.1

- documentation and print method for policy_eval() improved- multiple thresholds for policy_learn()- bug fixes: get_policy_functions(), predict.blip_function(), predict.q_glmnet()

polle 1.5

- added target argument in policy_eval for estimating the subgroup average treatment effect.- added threshold argument in policy_learn for learning the optimal subgroup.- added vignette for learning and evaluating the optimal subgroup.

polle 1.4

- vignettes added for policy_data, policy_learn and policy_eval.

polle 1.3

- new policy_learn type: "blip". Similar to "drql", but only a single model is fitted using the doubly robust score for the blip.- q_sl now uses the folds from policy_learn when it is used a policy model for "blip" and "drql".- g_xgboost and q_xgboost.

polle 1.2

- action sets can now vary across stages (stage_action_sets)- g_empir() is a new g-model useful for calculating the empirical (conditional)probabilities- conditional() estimates the group specific policy value estimates- progressr is now implemented for policy_eval()- sim_single_stage(), sim_two_stage(), and sim_multi_stage() are new functionsfor simulating data.

polle 1.0

- Package documentation: https://arxiv.org/abs/2212.02335