NEWS

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coresynth 0.2.0 (2026-06-12)

New features

• Conformal inference (conformal_inference()): permutation-based p-values and confidence intervals following Chernozhukov, Wüthrich & Zhu (2021). Works with sharp fits across all supported estimation methods (scm, sdid, gsc, mc, si). The counterfactual proxy is re-estimated under the null on all T periods (essential for finite-sample validity per CWZ §2.2), and p-values are obtained via moving-block (cyclic-shift) permutation of the estimated residuals. Confidence intervals are constructed by test inversion over a user-supplied or automatically chosen grid. Returns a coresynth_inference subclass compatible with tidy() and glance().

Minor improvements

• panel_to_matrices(): fill loop replaced by vectorised match() + matrix-index assignment; removes an O(n × (T + N)) bottleneck in the shared data-prep path.
• tasc.cpp: safe_inv_sympd() helper added so the Kalman filter degrades to pinv instead of aborting when the innovation covariance is not numerically PD.
• %||% null-coalescing helper centralised in utils.R; duplicate definitions in broom.R and plot.R removed.
• check_sharp_adoption() (unused internal function) removed.

coresynth 0.1.0

First public release.

Methods

• SCM (Abadie, Diamond & Hainmueller 2010): Synthetic Control Method with unified formula interface. Supports predictor variables via pred(), out-of-sample V selection (v_selection = "oos"), donor filtering (donor_mspe_threshold), penalised SCM (lambda_pen), and staggered adoption. Inference: MSPE ratio permutation test via mspe_ratio_pval().
• SDID (Arkhangelsky et al. 2021): Synthetic Difference-in-Differences. Supports time-varying covariates (covariates =), sharp and staggered adoption. Inference: sdid_inference() with placebo / bootstrap / jackknife / jackknife_global.
• GSC (Xu 2017): Generalised Synthetic Control with interactive fixed effects. Supports time-varying covariates via the full EM algorithm, sharp and staggered adoption. Inference: parametric bootstrap (gsc_boot()) and non-parametric (gsc_inference()).
• MC (Athey et al. 2021): Matrix Completion via nuclear-norm regularisation (Soft-Impute). Supports sharp and staggered adoption.
• TASC (Rho et al. 2026): Time-Aware Synthetic Control via Kalman EM. Supports sharp and staggered adoption.
• SI (Agarwal et al. 2025): Synthetic Interventions via SI-PCR. Supports sharp, staggered, multi-arm (K > 1), and staggered × multi-arm. Inference: si_inference() with bootstrap / jackknife / jackknife_global.
• SCM-Design (Abadie & Zhao 2026): scm_design() with base / weakly_targeted / unit_level variants, blank-period permutation test, and split-conformal confidence intervals.

Unified API

• Single scm_fit(outcome ~ treatment | unit + time, data, method = ...) entry point for all methods.
• panel_to_tensor() for multi-arm SI data preparation.
• broom integration: tidy(), glance(), augment() for all methods and inference objects.
• plot.coresynth(): trend, gap, and weights plots via ggplot2.
• export_json(): JSON export for reproducibility.

Performance

All core optimisations implemented in C++ via RcppArmadillo: 50–70x faster than the Synth package for typical panel sizes (N_co ≤ 30). src/inference.cpp placebo loops parallelised with OpenMP.