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coresynth 0.2.0

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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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