Estimating Equations
=====================
To ease use, ``delicatessen`` comes with a variety of built-in estimating equations, covering a variety of common use
cases. Below is reference documentation for the currently supported estimating equations. Please contact the
development team if there are estimating equations you would like to see added.


Basic
-------------------

.. currentmodule:: delicatessen.estimating_equations.basic

.. autosummary::
  :toctree: generated/

  ee_mean
  ee_mean_robust
  ee_mean_geometric
  ee_mean_variance
  ee_percentile
  ee_positive_mean_deviation


Regression
-------------------

.. currentmodule:: delicatessen.estimating_equations.regression

.. autosummary::
  :toctree: generated/

  ee_regression
  ee_mlogit
  ee_glm
  ee_beta_regression
  ee_tobit
  ee_robust_regression
  ee_ridge_regression
  ee_dlasso_regression
  ee_lasso_regression
  ee_elasticnet_regression
  ee_bridge_regression
  ee_additive_regression


Measurement
-------------------

.. currentmodule:: delicatessen.estimating_equations.measurement

.. autosummary::
  :toctree: generated/

  ee_rogan_gladen
  ee_rogan_gladen_extended
  ee_regression_calibration


Survival
-------------------

.. currentmodule:: delicatessen.estimating_equations.survival

.. autosummary::
  :toctree: generated/

  ee_survival_model
  ee_aft
  ee_plogit


Pharmacokinetic Models
----------------------

.. currentmodule:: delicatessen.estimating_equations.pharmacokinetics

.. autosummary::
  :toctree: generated/

  ee_emax
  ee_emax_ed
  ee_loglogistic
  ee_loglogistic_ed


Causal Inference
-------------------

.. currentmodule:: delicatessen.estimating_equations.causal

.. autosummary::
  :toctree: generated/

  ee_gformula
  ee_ipw
  ee_ipw_msm
  ee_aipw
  ee_gestimation_snmm
  ee_iv_causal
  ee_2sls
  ee_mean_sensitivity_analysis