2. DoubleML Models

2.1. doubleml.plm

DoubleMLPLR(obj_dml_data, ml_l, ml_m[, ...])	Double machine learning for partially linear regression models
DoubleMLPLIV(obj_dml_data, ml_l, ml_m, ml_r)	Double machine learning for partially linear IV regression models

2.2. doubleml.irm

DoubleMLIRM(obj_dml_data, ml_g, ml_m[, ...])	Double machine learning for interactive regression models
DoubleMLAPO(obj_dml_data, ml_g, ml_m, ...[, ...])	Double machine learning average potential outcomes for interactive regression models.
DoubleMLAPOS(obj_dml_data, ml_g, ml_m, ...)	Double machine learning for interactive regression models with multiple discrete treatments.
DoubleMLIIVM(obj_dml_data, ml_g, ml_m, ml_r)	Double machine learning for interactive IV regression models
DoubleMLPQ(obj_dml_data, ml_g, ml_m[, ...])	Double machine learning for potential quantiles
DoubleMLLPQ(obj_dml_data, ml_g, ml_m[, ...])	Double machine learning for local potential quantiles
DoubleMLCVAR(obj_dml_data, ml_g, ml_m[, ...])	Double machine learning for conditional value at risk for potential outcomes
DoubleMLQTE(obj_dml_data, ml_g[, ml_m, ...])	Double machine learning for quantile treatment effects
DoubleMLSSM(obj_dml_data, ml_g, ml_pi, ml_m)	Double machine learning for sample selection models

2.3. doubleml.did

DoubleMLDIDMulti(obj_dml_data, ml_g[, ml_m, ...])	Double machine learning for multi-period difference-in-differences models.
DoubleMLDIDAggregation(frameworks, ...[, ...])	Class for aggregating multiple difference-in-differences (DID) frameworks.
DoubleMLDIDBinary(obj_dml_data, g_value, ...)	Double machine learning for difference-in-differences models with panel data (binary setting in terms of group and time
DoubleMLDID(obj_dml_data, ml_g[, ml_m, ...])	Double machine learning for difference-in-differences models with panel data (two time periods).
DoubleMLDIDCS(obj_dml_data, ml_g[, ml_m, ...])	Double machine learning for difference-in-difference with repeated cross-sections.

2.4. doubleml.rdd

RDFlex(obj_dml_data, ml_g[, ml_m, fuzzy, ...])

Flexible adjustment with double machine learning for regression discontinuity designs