# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "RPIV" in publications use:' type: software license: GPL-3.0-or-later title: 'RPIV: Residual Prediction Tests for Well-Specification of Instrumental Variable Models' version: 1.1.0 doi: 10.32614/CRAN.package.RPIV abstract: Two tests for the well-specification of the linear instrumental variable model. The first test is based on trying to predict the residuals of a two-stage least-squares regression using a random forest. The second test is robust to weak-identification and based on trying to predict the residuals for a particular candidate parameter and can also be used to construct confidence sets with an Anderson-Rubin-type inversion. Details can be found in Scheidegger, Londschien and Bühlmann (2025) "Machine-learning-powered specification testing in linear instrumental variable models" . authors: - family-names: Scheidegger given-names: Cyrill email: cyrill.scheidegger@stat.math.ethz.ch orcid: https://orcid.org/0009-0005-2851-1384 repository: https://cyrillsch.r-universe.dev repository-code: https://github.com/cyrillsch/RPIV commit: 4e96259d9e2d9be96ccbdc5cbc58d901aef8f798 url: https://github.com/cyrillsch/RPIV date-released: '2026-03-24' contact: - family-names: Scheidegger given-names: Cyrill email: cyrill.scheidegger@stat.math.ethz.ch orcid: https://orcid.org/0009-0005-2851-1384