Changes in version 0.2.1                        

Bug fixes

  - v_selection = "oos" (outcomes-only case) previously fit candidate
    W(V) on the full pre-treatment outcome matrix and restricted only
    the MSPE evaluation to the validation window, allowing the V
    optimiser to fit the validation period indirectly (a data leak
    relative to Abadie (2021) S.3.2). The new .scm_oos_outcomes()
    implements the correct train/validation split: candidate W(V) are
    fitted on training-half outcomes only, V* minimises validation-half
    MSPE, and W* is refit with V* on the outcomes of the last
    floor(T_pre/2) pre-treatment periods. For OOS fits, v_weights now
    has floor(T_pre/2) entries and a new v_rows field records which
    periods they refer to. This changes numerical results for
    v_selection = "oos".

New features

  - scale_predictors (default TRUE): predictor rows supplied via
    predictors = are now divided by their standard deviation across all
    units before optimisation, matching the Synth reference
    implementation (Abadie, Diamond & Hainmueller 2011, JSS).
    predictor_table continues to report values on the original scale.
    This changes numerical results for SCM fits with user-supplied
    predictors unless scale_predictors = FALSE.
  - placebo_in_time(): in-time placebo (backdating) test for sharp SCM
    fits (Abadie, Diamond & Hainmueller 2015; Abadie &
    Vives-i-Bastida 2022).
  - loo_donors(): leave-one-out donor robustness check with the
    predictor weights V held fixed (Abadie, Diamond & Hainmueller 2015,
    footnote 20).
  - build_predictor_matrices() now errors with an informative message if
    a pred() time window produces missing or non-finite predictor
    values.

                 Changes in version 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 S.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.

                        Changes in version 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.