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PhaseGMM

0.1.1

Phase-Function Based Estimation and Inference for Linear Errors-in-Variables (EIV) Models

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Overview

About
Maintained by Chang LiuFirst published 2026-04-022 releasesCRAN page ↗

Estimation and inference for coefficients of linear EIV models with symmetric measurement errors. The measurement errors can be homoscedastic or heteroscedastic, for the latter, replication for at least some observations needs to be available. The estimation method and asymptotic inference are based on a generalised method of moments framework, where the estimating equations are formed from (1) minimising the distance between the empirical phase function (normalised characteristic function) of the response and that of the linear combination of all the covariates at the estimates, and (2) minimising a corrected least-square discrepancy function. Specifically, for a linear EIV model with p error-prone and q error-free covariates, if replicates are available, the GMM approach is based on a 2(p+q) estimating equations if some replicates are available and based on p+2q estimating equations if no replicate is available. The details of the method are described in Nghiem and Potgieter (2020) doi:10.1093/biomet/asaa025 and Nghiem and Potgieter (2025) doi:10.5705/ss.202022.0331.

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Documentation

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Dependencies

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2 external dependencies (excludes base and recommended)
Depends (1)
R >= 3.5
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Package Timeline

2 releases. Pick two to compare their code metrics. R releases are shown for context.

  • 0.1.1Latest
    2026-04-28 · current release · diff ↗
  • R
    R 4.6.0 released · 2026-04-24
  • 0.1.0
    2026-04-02
  • R
    R 4.5.0 released · 2025-04-11

Package metadata

First published
2026-04-02
Total releases
2 / 1 yrs
License
GPL-2 OSI
Minimum R
≥ 3.5
Bundled data
38 KB / 1 file
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not tracked yet
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