NMAR 0.1.2

• Bootstrap replicate evaluation backend is now configurable via options(nmar.bootstrap_apply = "auto"|"base"|"future"). Default bootstrap behavior (nmar.bootstrap_apply = "auto") uses base::lapply() unless the current future plan has more than one worker; if so, it uses future.apply::future_lapply(future.seed = TRUE) when available.
• Exptilt validation now rejects non-finite values (e.g., Inf, -Inf) in covariates (and non-finite observed outcomes).

NMAR 0.1.1

• CRAN release-related fixes
• Fix return roxygen keyword in S3 Functions
• Add research doi references to DESCRIPTION file

NMAR 0.1.0

Initial CRAN Release

• First release of the NMAR package for estimating nonignorable nonresponse (NMAR) bias in survey data.

Methods

• Empirical Likelihood (EL): Added el_engine() implementing the estimator of Qin, Leung, and Shao (2002). This method uses empirical likelihood weights satisfying response mechanism equations and auxiliary moment constraints.
• Exponential Tilting (Parametric & Nonparametric): Included robust implementations for both microdata (exptilt_engine) and aggregated contingency tables (exptilt_nonparam_engine) based on Riddles, Kim, and Im (2016).

Key Features

• Unified API: All estimators are accessible via a single, consistent nmar() interface supporting standard formula syntax (e.g., Y ~ X | Z).
• Complex Survey Support: Seamless integration with the survey package. nmar() accepts survey.design objects, automatically handling weights and stratification.
• Variance Estimation: Robust bootstrapping (S3) implementation for standard errors and confidence intervals across all engines.
• Diagnostics: Rich return objects including convergence statistics, Jacobian condition numbers, and detailed weight summaries.

Major Changes

• Refactored Architecture: The exptilt and el engines share a unified structural design, ensuring consistent behavior for controls, standardization, and error handling.
• Standardization: Added standardize = TRUE argument to engines to improve numerical stability during optimization.