Package index • deli

Core

Primary estimator classes and estimation interface.

m_estimate(): One-step M-estimation
gmm_estimate(): One-step GMM estimation
MEstimator(): M-Estimator
GMMEstimator(): GMM Estimator
estimate(): Estimate parameters and sandwich variance

Statistical Inference

Tools for statistical inference from fitted estimators.

coef(<deli::deli_estimator>) vcov(<deli::deli_estimator>) confint(<deli::deli_estimator>) nobs(<deli::deli_estimator>) df.residual(<deli::deli_estimator>) fitted(<deli::deli_estimator>) residuals(<deli::deli_estimator>) weights(<deli::deli_estimator>) model.frame(<deli::deli_estimator>) model.matrix(<deli::deli_estimator>) formula(<deli::deli_estimator>) terms(<deli::deli_estimator>) sigma(<deli::deli_estimator>) logLik(<deli::deli_estimator>) deviance(<deli::deli_estimator>): Standard S3 generics for deli estimators
print(<deli::deli_estimator>) summary(<deli::deli_estimator>): Display methods for deli estimators
tidy(<deli::deli_estimator>) glance(<deli::deli_estimator>): Broom tidiers for deli estimators
reexports tidy glance augment: Objects exported from other packages
confidence_intervals(): Confidence intervals for M-Estimator parameters
z_scores(): Z-scores for M-Estimator parameters
p_values(): P-values for M-Estimator parameters
s_values(): S-values (surprisal) for M-Estimator parameters
confidence_bands(): Confidence bands for parameter vectors
compute_confidence_bands(): Compute confidence bands from theta and covariance
delta_method(): Delta method for variance of transformed parameters
influence_functions(): Influence functions for M-Estimator
compute_sandwich(): Compute the empirical sandwich variance estimator

Basic Estimating Equations

Estimating equations for means, variances, and percentiles.

ee_mean(): Estimating equation for the mean
ee_mean_variance(): Estimating equations for the mean and variance
ee_mean_geometric(): Estimating equation for the geometric mean
ee_mean_robust(): Estimating equation for the robust mean
ee_percentile(): Estimating equation for the percentile
ee_positive_mean_deviation(): Estimating equations for the positive mean deviation

Regression Estimating Equations

Estimating equations for generalized linear models and penalized regression.

ee_regression(): Estimating equation for regression
ee_glm(): Estimating equation for generalized linear models
ee_robust_regression(): Estimating equation for robust regression
ee_ridge_regression(): Estimating equation for ridge regression
ee_lasso_regression(): Estimating equation for approximate LASSO regression
ee_dlasso_regression(): Estimating equation for differentiable LASSO regression
ee_elasticnet_regression(): Estimating equation for elastic net regression
ee_bridge_regression(): Estimating equation for bridge penalized regression
ee_beta_regression(): Estimating equation for beta regression
ee_tobit(): Estimating equation for Tobit regression (Type I)
ee_mlogit(): Estimating equation for multinomial logistic regression
ee_additive_regression(): Estimating equation for additive regression (GAM)

Causal Estimating Equations

Estimating equations for causal inference.

ee_gformula(): Estimating equations for the g-formula (g-computation)
ee_ipw(): Estimating equations for inverse probability weighting (IPW)
ee_aipw(): Estimating equations for augmented inverse probability weighting (AIPW)
ee_ipw_msm(): Estimating equations for IPW marginal structural model
ee_gestimation_snmm(): Estimating equations for g-estimation of structural nested mean models
ee_iv_causal(): Estimating equations for instrumental variable (IV) estimation
ee_2sls(): Estimating equations for Two-Stage Least Squares (2SLS)
ee_mean_sensitivity_analysis(): Estimating equations for weighted sensitivity analysis of the mean

Survival Estimating Equations

Estimating equations for survival analysis.

ee_aft(): Estimating equation for accelerated failure time models
ee_plogit(): Estimating equation for pooled logistic regression
ee_survival_model(): Estimating equation for parametric survival models

Measurement Estimating Equations

Estimating equations for measurement error correction.

ee_rogan_gladen(): Estimating equation for Rogan-Gladen correction
ee_rogan_gladen_extended(): Estimating equation for extended Rogan-Gladen correction
ee_regression_calibration(): Estimating equation for regression calibration

Pharmacokinetics Estimating Equations

Estimating equations for dose-response models.

ee_emax(): Estimating equation for E-max dose-response model
ee_emax_ed(): Estimating equation for delta-effective dose (E-max)
ee_loglogistic(): Estimating equation for 4-parameter log-logistic dose-response model
ee_loglogistic_ed(): Estimating equation for delta-effective dose (log-logistic)

Predictions

Prediction helpers for fitted models.

predict(<deli::deli_estimator>): Predictions from a fitted deli estimator
augment(<deli::deli_estimator>): Augment data with predictions from a fitted deli estimator
regression_predictions(): Generate predicted values from a regression model
survival_predictions(): Generate predicted survival measures from a parametric survival model
aft_predictions_individual(): Predicted survival measures from an AFT model
aft_predictions_function(): Function-level predicted survival measures from an AFT model
plogit_predict(): Predicted survival measures from a pooled logistic regression model
convert_survival_measures(): Convert between survival analysis measures

Transformations and Math

Transformations and special functions that mirror base R counterparts such as plogis(), qlogis(), pnorm(), dnorm(), and psigamma(). Several are required in place of those counterparts under exact differentiation, which the base R versions do not support.

logit(): Logistic transformation
inverse_logit(): Inverse logistic transformation
identity_transform(): Identity transformation
standard_normal_cdf(): Standard normal CDF
standard_normal_pdf(): Standard normal PDF
deli_polygamma(): Polygamma function
deli_digamma(): Digamma function

Model Building Utilities

Helpers for building estimating equations and design matrices. None has a base R counterpart that returns the same values.

robust_loss_functions(): Robust loss function derivatives
aggregate_efuncs(): Aggregate estimating function contributions by group
deli_spline(): Generate polynomial spline basis terms
additive_design_matrix(): Build an additive design matrix for GAMs

Conditions

The condition classes deli’s errors and warnings carry, so that a caller can answer one by class rather than by matching its message.

deli-conditions: The condition classes deli raises

Datasets

Bundled example datasets.

shaq_free_throws: Shaquille O'Neal free throw data
inderjit: Inderjit dose-response data
robust_regress: Robust regression example data
breast_cancer: Breast cancer survival data
mroz: Mroz labor force participation data
crime: US state crime data
collett_bladder: Collett bladder cancer recurrence data
cutler1995: Cutler (1995) pharmacodynamic data
bonate_adverse: Bonate adverse events data
lau_wihs: Lau WIHS HIV/CD4 data
sdss_quasar: SDSS quasar survey data
nsduh: NSDUH substance use survey data
actg175: ACTG 175 clinical trial data
pparg: PPARg cheminformatics data
get_tested: GetTested randomized trial data