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

ee_mean(theta, y[, weights])	Estimating equation for the mean.
ee_mean_robust(theta, y, k[, loss, lower, upper])	Estimating equation for the (unscaled) robust mean.
ee_mean_geometric(theta, y[, weights, log_theta])	Estimating equations for the geometric mean.
ee_mean_variance(theta, y)	Estimating equations for the mean and variance. The estimating equations for the mean and
ee_meta_random(theta, point_est, var_est)	Estimating equation for random-effects meta-analysis using the Paule-Mandel method.
ee_percentile(theta, y, q)	Estimating equation for the q percentile.
ee_positive_mean_deviation(theta, y)	Estimating equations for the positive mean deviation.

Regression

ee_regression(theta, X, y, model[, weights, ...])	Estimating equation for regression.
ee_mlogit(theta, X, y[, weights, offset])	Estimating equation for multinomial logistic regression.
ee_glm(theta, X, y, distribution, link[, ...])	Estimating equation for regression with a generalized linear model.
ee_beta_regression(theta, X, y[, weights, ...])	Estimating equation for a beta regression model.
ee_tobit(theta, X, y[, lower, upper, ...])	Estimating equation for linear regression with censored outcomes.
ee_robust_regression(theta, X, y, model, k)	Estimating equations for (unscaled) robust regression.
ee_ridge_regression(theta, X, y, model, penalty)	Estimating equations for ridge regression.
ee_dlasso_regression(theta, X, y, model, penalty)	Estimating equation for a differentiable LASSO (least absolute shrinkage and selection operator) regressor.
ee_lasso_regression(theta, X, y, model, penalty)	Estimating equation for an approximate LASSO (least absolute shrinkage and selection operator) regressor.
ee_elasticnet_regression(theta, X, y, model, ...)	Estimating equations for Elastic-Net regression.
ee_bridge_regression(theta, X, y, model, ...)	Estimating equation for bridge penalized regression.
ee_additive_regression(theta, X, y, ...[, ...])	Estimating equation for Generalized Additive Models (GAMs).
ee_meta_regression(theta, point_est, var_est)	Estimating equation for random-effects meta-regression using the Paule-Mandel method.

Measurement

ee_rogan_gladen(theta, y, y_star, r[, weights])	Estimating equation for the Rogan-Gladen correction for mismeasured binary outcomes.
ee_rogan_gladen_extended(theta, y, y_star, r, X)	Estimating equation for the extended Rogan-Gladen correction for mismeasured binary outcomes.
ee_regression_calibration(theta, beta, a, ...)	Estimating equation for regression calibration with external data for a mismeasured binary action.

Survival

ee_survival_model(theta, t, delta, distribution)	Estimating equation for a parametric survival models.
ee_aft(theta, X, t, delta, distribution[, ...])	Estimating equation for a generalized accelerated failure time (AFT) model.
ee_plogit(theta, X, t, delta[, S, ...])	Estimating equation for pooled logistic regression with discrete-time survival data.

Pharmacokinetic Models

ee_emax(theta, dose, response[, robust, k])	Estimating equations for the (hyperbolic) E-max model, or Hill Equation.
ee_emax_ed(theta, dose, delta, ed50)	Estimating equation for the \(\delta\)-effective dose with the E-max model.
ee_loglogistic(theta, dose, response[, ...])	Estimating equations for the 4 parameter log-logistic dose-response model.
ee_loglogistic_ed(theta, dose, delta, lower, ...)	Estimating equation for the \(\delta\)-effective dose with the 4 parameter log-logistic model.

Causal Inference

ee_gformula(theta, y, X, X1[, X0, ...])	Estimating equations for the g-formula (or g-computation).
ee_ipw(theta, y, A, W[, truncate, weights])	Estimating equation for inverse probability weighting (IPW) estimator.
ee_ipw_msm(theta, y, A, W, V, distribution, link)	Estimating equation for parameters of a marginal structural model estimated using inverse probability weighting.
ee_aipw(theta, y, A, W, X, X1, X0[, ...])	Estimating equation for augmented inverse probability weighting (AIPW) estimator.
ee_gestimation_snmm(theta, y, A, W, V[, X, ...])	Estimating equations for g-estimation of structural mean models (SMMs).
ee_iv_causal(theta, y, A, Z[, weights])	Estimating equation for instrumental variable (IV) analysis with the usual IV.
ee_2sls(theta, y, A, Z[, W, weights])	Estimating equations for Two-Stage Least Squares (2SLS) for instrumental variable (IV) analysis.
ee_gestimation_snmm_iv(theta, y, Z, A, W, V)	Estimating equations for g-estimation of structural mean models (SMMs) with an instrumental variable (IV). The
ee_mean_sensitivity_analysis(theta, y, ...)	Estimating equation for weighted sensitivity analysis estimator of the mean.