anumbat.r-universe.dev

[anumbat] balnet 0.0.3

erik.sverdrup@monash.edu (Erik Sverdrup) — Tue, 26 May 2026 04:38:52 GMT

Provides pathwise estimation of regularized logistic propensity score models using covariate balancing loss functions rather than maximum likelihood. Regularization paths are fit via the 'adelie' elastic-net solver with a 'glmnet'-like interface, yielding balancing weights that target covariate balance for the ATE and ATT. Under lasso penalization, lambda bounds the maximum covariate imbalance, so the regularization path traces a sequence of decreasing imbalance tolerances. For details, see Sverdrup & Hastie (2026) .

[anumbat] grf 2.6.1

erik.sverdrup@monash.edu (Erik Sverdrup) — Wed, 04 Mar 2026 06:30:43 GMT

Forest-based statistical estimation and inference. GRF provides non-parametric methods for heterogeneous treatment effects estimation (optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables), as well as least-squares regression, quantile regression, and survival regression, all with support for missing covariates.

[anumbat] policytree 1.2.4

erik.sverdrup@monash.edu (Erik Sverdrup) — Wed, 18 Feb 2026 07:10:02 GMT

Learn optimal policies via doubly robust empirical welfare maximization over trees. Given doubly robust reward estimates, this package finds a rule-based treatment prescription policy, where the policy takes the form of a shallow decision tree that is globally (or close to) optimal.

[anumbat] maq 0.6.0

erik.sverdrup@monash.edu (Erik Sverdrup) — Mon, 14 Apr 2025 11:40:02 GMT

Policy evaluation using generalized Qini curves: Evaluate data-driven treatment targeting rules for one or more treatment arms over different budget constraints in experimental or observational settings under unconfoundedness.