NEWS

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rd2d 1.0.0 (2026-05-28)

• Adds fitmethod = c("joint", "separate") to rd2d(), rdbw2d(), rd2d.distance(), and rdbw2d.distance(). The new default fitmethod = "joint" preserves legacy point estimates while using joint variance corrections, including joint HC1 degrees-of-freedom factors and cluster-robust covariance calculations that account for clusters observed on both treatment sides. fitmethod = "separate" retains the previous side-specific variance calculation. Bandwidth selection keeps the existing plug-in bias estimator and now applies the same joint/separate convention to the selector's variance constants.
• Adds covs.eff to rd2d(), rdbw2d(), rd2d.distance(), and rdbw2d.distance() for efficiency adjustment using pre-intervention covariates with a common covariate coefficient across treatment sides, including sharp, fuzzy, joint, separate, fixed-bandwidth, and automatic-bandwidth paths.
• Adds covs.drop and covs.tol safeguards for rank-deficient covs.eff. Redundant covariate columns are dropped by default after residualizing on the local polynomial basis, with diagnostics stored in opt.
• Adds S3 methods requested for the JSS resubmission: plot() for estimation and bandwidth-selection objects, plus coef(), vcov(), and confint() for rd2d() and rd2d.distance() estimation objects.
• Improves exported R argument validation so invalid inputs raise informative errors directly rather than printing a message followed by an empty error.
• Adds fuzzy boundary RD support for both location-based and distance-based methods, including fuzzy main effects, ITT, first-stage, and optional one-sided outputs.
• Adds params.other and params.cov controls to rd2d() for optional companion tables and covariance storage.
• Moves the Gaussian simulation count repp from rd2d() and rd2d.distance() to the summary methods that use it for uniform confidence bands and LBATE critical values.
• Reports pointwise CI columns and optional uniform CB columns separately in summary.rd2d() and summary.rd2d.distance().
• Speeds up location-based and distance-based fits by reducing repeated low-order basis, kernel, covariance projection, and masspoint-counting work.
• Moves location-based uniform confidence band, WBATE, and LBATE construction to summary.rd2d().
• Adds distance-based WBATE and LBATE construction to summary.rd2d.distance(), including fuzzy main, ITT, and first-stage outputs.
• Updates location-based return tables to use lowercase column names such as estimate.p, std.err.q, t.value, and p.value.
• Fixes location-based covariance construction for signed cross-evaluation covariance, clustered finite-sample scaling, and right-sided simulated critical values.
• Fixes distance-based covariance construction to use signed local-polynomial residuals, corrected cluster covariance halves, and side-specific bandwidth plug-in matrices.
• Adds CER-optimal bandwidth selectors cerrd, certwo, icerrd, and icertwo for location-based and distance-based methods.
• Replaces the distance-based kink/rbc options with kink.unknown and kink.position, including adaptive known-kink bandwidths based on boundary point locations.
• Uses smooth-boundary robust bias-corrected inference as the distance-based default, and uses the same stabilized Gram-matrix inversion helper as the location-based methods.
• Uses q = p by default for distance-based unknown-kink specifications to avoid compounding the unknown-kink bandwidth shrinkage with an additional polynomial-order change.
• Aligns distance-based return tables with location-based naming conventions, including main, main.0, main.1, bw, estimate.p, std.err.q, N.Co, and N.Tr.
• Updates public notation to use fuzzy for treatment receipt/status, tau.itt/tau.itt.q for reduced-form outcome estimates, tau.fs/tau.fs.q for first-stage estimates, assignment for assignment, cluster for cluster identifiers, and distance for signed distance scores.
• Removes user-facing derivative notation from distance-based help files and printed output.
• Adds focused tests for location-based returns, lazy confidence bands, aggregate inference, covariance regularization, clustered covariance consistency, and numerical preservation.
• Adds focused tests for distance-based return names, fuzzy outputs, heteroskedastic and clustered standard errors, covariance diagonals, aggregate inference, and polynomial-order validation.
• Simplifies repository illustration files to R/rd2d_illustration.R and R/rd2d_plot.R.

rd2d 0.0.3 (2025-10-24)

Version published on CRAN on 2025-10-24.