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Changelog

Source: NEWS.md

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: 2026-01-16

  • 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.