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Changelog

Source: NEWS.md

coresynth 0.4.0.9000

Changed

  • Sharp SCM fits with predictors = NULL now emit a one-line message naming the default model (“using the outcome in each of the k pre-treatment periods as predictors”), so the implied specification is visible rather than only documented. The message is suppressed where it would be noise: staggered fits (always outcomes-only, no alternative) and v_selection = "oos" (its own split is described under that argument). Wrap the call in suppressMessages() to silence it.

  • A predictor specification consisting solely of the outcome at each single pre-treatment period is now fitted through the outcomes-only path, returning a fit identical to predictors = NULL (with a message). Such a spec builds the same X0/X1 as the outcomes-only fit – it is the standard way reference implementations express an outcomes-only SCM (Synth requires it: with no predictors at all, dataprep() errors) – but previously it entered the predictor path (per-row SD scaling + multi-start V search), which could return slightly different weights for the same model and ran orders of magnitude slower. Verified on Proposition 99: predictors = NULL, the per-period spec, and Synth’s per-period special.predictors run now agree (weights within 3e-3 of Synth, identical between the two coresynth forms). Pass v_optim = "multistart" explicitly to force the predictor-path optimiser for such a spec.

Performance

  • Predictor matrix construction is no longer a bottleneck for predictor-based SCM fits. build_predictor_matrices() aggregated each predictor row with one full scan of the input data frame per unit (O(rows x units x data size)); with many pred() entries – e.g. one outcome lag per pre-treatment year – this R-level loop dominated the fit time (1.75 s of a 3.9 s fit at N_co = 150, T_pre = 80, 15k rows). Each predictor row is now computed with a single grouped aggregation (0.06 s for the same spec, ~29x). Output is bit-identical to the previous implementation across ops (mean/median/sum), id types, NA handling, and the empty-window error path, so every fit is unchanged. The same per-unit-scan pattern was removed from build_covariate_array() (the covariate paths of GSC, SDID, and staggered SCM) and from scm_design()’s predictor builder, likewise with bit-identical output.

  • Outcomes-only SCM fits are ~2-2.5x faster again. Hardening the inner active-set QP for the predictor multi-start search (0.4.0) replaced the cheap bordered-KKT face solve with a scale-robust null-space projection on every pivot. That robustness is only needed when predictors are supplied (rank-deficient V, scale invariance under predictor rescaling); the outcomes-only path – dense V, well-conditioned faces, no rescaling – paid the cost without the benefit, running roughly twice as slow as 0.3.x despite returning the identical fit. The outcomes-only inner QP now uses the cheap bordered-KKT solve again (scm_weights_cpp(cheap_face = TRUE), wired automatically for outcomes-only fits, staggered SCM, the outcomes-only in-space placebo, placebo_in_time(), and OOS V selection). Predictor-based fits and the multi-start search keep the null-space solver, so their results and scale invariance are untouched. Outcomes-only results are bit-identical to the 0.2/0.3 line and differ from 0.4.0 only at round-off (<= 5e-14 in weights), i.e. not observably. Verified against Synth on Proposition 99 (ATT, synthetic series, pre-RMSPE, donor weights all agree) and across a degeneracy/noise sweep.

coresynth 0.4.0

CRAN release: 2026-07-20

Breaking changes

  • Predictor-based SCM fits now default to a multi-start outer V optimisation (v_optim = "auto", the new default, resolves to "multistart" when predictors are supplied and to the single-start "coord_descent" for outcomes-only fits). The outer problem – choose V so that the implied donor weights minimise pre-treatment outcome MSPE – is non-convex, and the previous single start from uniform V could settle in a poor basin: on the Proposition 99 predictor specification it reported a pre-treatment SSR of 137.8 where 58.5 is attainable (below the reference Synth/tidysynth solution’s 78.1), halving the treated unit’s MSPE ratio. The multi-start search screens a fixed, fully deterministic start set (uniform, one-hot per predictor, 100 fixed-seed draws) with warm-started exact inner QPs, then runs the leaders through a Nelder-Mead / coordinate-descent refinement pipeline; its solution is never worse (in pre-treatment loss) than the single-start path, and no R RNG state is consumed. mspe_ratio_pval() applies the same search to every placebo refit so the permutation test stays symmetric. Numeric results for predictor-based fits therefore change (toward better pre-treatment fits); pass v_optim = "coord_descent" to reproduce the previous behaviour. Outcomes-only fits are unchanged. Note that the old "auto" (choose "bfgs" when k <= 15) is gone; "bfgs" itself remains available.

New features

  • plot(fit, type = "pred_weights") draws the predictor/variable weight matrix of a sharp SCM fit as a horizontal bar chart, the companion to the existing donor type = "weights" chart. Bars are labelled with the predictor names (or V1..V_Tpre for outcomes-only fits) and honour the fill and top_n arguments. Staggered SCM and the other estimators do not estimate a V matrix and raise an informative error.
  • plot_data() returns the tidy data.frame behind any plot() view, taking the same type argument ("trend", "gap", "weights", "pred_weights" for a fit; "gaps", "ratios" for an scm_placebo object). It is the data-side companion to plot(): extract the plotted series, weights, or placebo paths to relabel the simplified series names, feed them into a table, or build a bespoke figure. Columns use plain names (time, value, series, …), and the arguments that change which rows or values appear (align, top_n, show_donors, mspe_prune) are honoured. The cosmetic near-zero-weight filter that type = "weights" applies to the chart is not used here, so every donor is returned (use top_n to subset).
  • v_window argument in scm_fit() (sharp SCM fits): a vector of pre-treatment time values over which the outer V optimisation evaluates the pre-treatment fit, e.g. v_window = 1975:1988. The default (NULL) keeps the current behaviour of evaluating on all pre-treatment periods. The window restricts only the outer evaluation loss – predictor matrices and the reported full-window loss are untouched – and mspe_ratio_pval() mirrors it in every placebo refit. Cannot be combined with v_selection = "oos", which manages its own train/validation split.
  • colors/labels in plot.coresynth() and plot.scm_placebo() are now keyed by one-word series identifiers: treated, synthetic, and (with show_donors > 0) donors for trend plots; treated and placebo for the placebo plots. The previous keys were the displayed legend strings (Treated, Synthetic Control, Placebo (donor pool)), which required quoting for the multi-word names and conflated series identity with the legend text that labels itself relabels. Write colors = c(treated = "black") instead of c(Treated = "black"); an unknown key errors with the list of valid keys. The displayed legend text is unchanged.
  • mspe_ratio_pval() placebo refits now mirror the treated fit’s predictor specification by default: use_covariates defaults to NULL (auto) instead of FALSE. When the fit was estimated with a predictors specification, each placebo unit is now refit with that same specification – the Abadie et al. (2010) / Synth convention, under which the treated and placebo test statistics are computed under one common spec. Previously the default silently switched the placebo refits to the outcomes-only spec, which (a) made the permutation compare statistics that were not exchangeable with the treated one, and (b) was extremely slow for long pre-periods because each placebo re-ran a T_pre-dimensional V optimisation. Outcomes-only fits are unaffected (auto resolves to the outcomes-only placebo path, as before). Pass use_covariates = FALSE explicitly to reproduce the old behaviour on covariate fits.
  • Treatment-line placement in plots: plot.coresynth() (type = "trend" and "gap") and plot.scm_placebo() (type = "gaps") gain a vline_offset argument controlling where the vertical treatment line is drawn, in periods relative to the first post-treatment period (the previous fixed position). vline_offset = -1 moves it to the last pre-treatment period, and fractional values such as -0.5 interpolate between adjacent observed times, on numeric, Date, and POSIXct axes alike. The vline style list now also accepts an xintercept element for one or more absolute positions (previously an error), e.g. vline = list(xintercept = "1989-01-01") on a Date axis. Hiding the line still works via vline = FALSE.
  • Level-aligned trend and gap plots: plot.coresynth() gains an align argument for type = "trend" and "gap". align = TRUE shifts the synthetic series by its pre-treatment level gap to the treated series, so both are drawn on the same level. SDID’s unit weights are estimated with the intercept concentrated out of the QP (Arkhangelsky et al. 2021), so its raw trend plot can show the synthetic control at a different level than the treated unit; align = TRUE uses the time-weight (lambda) weighted pre-period gap, which makes the average post-period gap in the plot equal the SDID estimate exactly.
  • Donor paths in trend plots: plot.coresynth(type = "trend") gains a show_donors argument that draws the outcome paths of the show_donors donor units with the largest weights as thin background lines (Inf shows all). The new "Donors" series participates in colors/labels overrides.
  • SDID time weights in the weights plot: for SDID fits, plot.coresynth(type = "weights") now shows two panels — donor unit weights (omega) and pre-period time weights (lambda) — in the same bar style. Other methods keep the single unit-weight panel.
  • Placebo SE and CI for SDID: sdid_inference(method = "placebo") now also reports the placebo-distribution standard error (Clarke et al. 2023, Algorithm 4) and the corresponding normal-approximation confidence interval, for both sharp and staggered fits. The p-value is unchanged (permutation-based). tidy()/glance() pick the new columns up automatically.
  • Partially pooled staggered SCM (Ben-Michael, Feller & Rothstein 2022, JRSS-B): scm_fit(method = "scm") on a staggered panel gains a nu argument. nu = NULL (default) keeps the previous behaviour (per-cohort V-optimised SCM). A numeric nu in [0, 1] minimises the convex combination nu * (pooled pre-treatment imbalance)^2 + (1 - nu) * (per-cohort imbalance)^2 over all cohort weight vectors jointly, so that the aggregate ATT is anchored by the pooled fit (nu = 0 reproduces separate per-cohort SCM with uniform lag weights, nu = 1 is fully pooled). nu = "auto" selects the paper’s heuristic value. The fit stores balance diagnostics in fit$pooling (q_sep, q_pool, and their separate-SCM baselines).
  • Intercept-shifted staggered SCM: new fixedeff argument for staggered SCM fits. fixedeff = TRUE demeans every unit by its own pre-treatment mean within each cohort before fitting (Ben-Michael, Feller & Rothstein 2022, Section 5.1; Doudchenko & Imbens 2017; Ferman & Pinto 2021), turning the estimator into a weighted difference-in-differences. Works with both the default path and the partially pooled path.
  • Wild bootstrap inference for staggered SCM: new scm_inference() function implements the weighted multiplier (wild) bootstrap of Ben-Michael, Feller & Rothstein (2022, Section 5.3): donor weights are kept fixed and per-treated-unit effect contributions are perturbed with golden-ratio two-point multipliers. Returns a standard coresynth_inference object (works with tidy()/glance()). This is the first inference method available for staggered SCM fits.
  • solve_simplex_qp() gains an optional x0 warm-start argument, used by the partially pooled block coordinate descent to restart FISTA from the previous block solution. Together with an objective-based stopping rule this makes the pooled path roughly an order of magnitude faster on larger donor pools (N = 100: ~0.8 s to ~0.06 s per fit). Validated against the reference implementation augsynth (weights correlate at 1.0, identical heuristic nu, equal pooled imbalance at nu = 1).

Deprecated

  • v_optim = "bfgs" is deprecated and will be removed in a future release. It is a single-start L-BFGS-B outer optimiser with no advantage over the alternatives now that the default is multi-start: use v_optim = "multistart" (or the "auto" default) for predictor-based fits, and "coord_descent" for outcomes-only fits. Passing "bfgs" still works but now emits a deprecation warning. "coord_descent" is not deprecated: besides being a selectable optimiser, it is the engine for outcomes-only and staggered fits and the reference against which the multi-start never-worse guarantee is defined.

Performance

  • The multi-start outer search runs at interactive speed. Outer-loss evaluations are KKT-gated warm-started active-set solves; the active set gained Bland’s-rule anti-cycling (engaged only beyond 30 pivots, so previously terminating pivot paths are untouched) and now solves each face subproblem in the null space of the sum constraint via a Cholesky/eigendecomposition hybrid that is robust to the rank-deficient metrics (k < |active set|) the multi-start screen hits constantly – and whose branch decision cannot be flipped by last-bit input perturbations, preserving the scale-invariance of scale_predictors. On a degenerate face the warm active-set solve can churn to its pivot cap, and the old cold-FISTA fallback (thousands of iterations on ill-conditioned metrics) made a single slow donor dominate placebo wall time; a finite-termination Lawson-Hanson NNLS now seeds the exact face solve on those cases in microseconds, gated by the same KKT check so the returned solution is unchanged. The rescue is confined to the multi-start internals – every other path (outcomes-only, coord_descent, oos, staggered, loo_donors(), conformal) keeps its historical solver bit-for-bit, and the multi-start uniform-start leg stays on the plain path so the never-worse guarantee holds. Candidate refinement pipelines run in parallel, and the placebo battery flattens the (donor x candidate) task list into one schedule – donor-level parallelism alone lets the slowest donor pin a thread while the others go idle. On the Proposition 99 8-predictor spec a full multi-start fit takes ~0.06s and the complete 38-donor mspe_ratio_pval() battery ~1.7s (every donor’s fit verified never worse than the single-start path). A side effect of the exact face solves: inner QPs on rank-deficient faces are now solved exactly where the previous code fell back to a loosely-converged FISTA iterate. Outcomes-only sharp, staggered, and placebo results are bit-identical to the previous release; v_selection = "oos" outcomes fits (where the training half has fewer rows than donors) can shift slightly, toward better fits.
  • The nested V/W coordinate descent is orders of magnitude faster. The solver behind scm_weights_cpp() and the outcomes-only placebo loop in scm_placebo_cpp() now solves each inner simplex QP with a warm-started active-set method (KKT-verified exact solve, with FISTA fallback), applies V-coordinate changes to the Gram matrix as implicit rank-1 updates instead of rebuilding X0' V X0 per grid point, and computes the Lipschitz constant once per coordinate-descent sweep instead of once per QP. On a monthly panel with T_pre = 139, T_post = 19, and 75 donors, the full 75-unit outcomes-only placebo run drops from roughly an hour to about 2 seconds, and a single outcomes-only scm_fit() from ~75 s to ~0.1 s. Per-unit results agree with the previous implementation within the coordinate-descent convergence tolerance: the estimator and its grid/accept/normalisation semantics are unchanged, and the inner QP solutions are now exact rather than first-order-approximate.
  • Covariate-spec placebo refits now run in parallel. When the fit was estimated with a predictors specification, mspe_ratio_pval() previously refit each placebo unit in a sequential R loop; the loop now runs in C++ under OpenMP (new low-level routine scm_placebo_x_cpp(), the covariate counterpart of scm_placebo_cpp()). Each leave-one-out problem is solved by the same coordinate-descent core as before, so per-unit results are identical to machine precision – only the wall time changes. The speedup is bounded by the slowest single placebo unit (iterations are distributed dynamically across cores), so it approaches the core count when per-unit costs are uniform and is smaller when one hard-to-fit donor dominates.

Bug fixes

  • plot.coresynth() no longer embeds non-ASCII characters (Greek letters, the Unicode minus sign) in plot titles and subtitles. Some platforms cannot compute text metrics for these characters outside a UTF-8 locale, which crashed R CMD check --run-donttest on macOS.

coresynth 0.3.0

CRAN release: 2026-07-12

New features

  • Outcome-series accessors: new exported generics treated_outcomes(), synthetic_outcomes(), and donor_outcomes() return the treated series, the estimated counterfactual, and the donor outcome matrix from any coresynth fit under a uniform interface, regardless of the estimation method. They replace the per-method field sniffing previously duplicated across augment(), conformal_inference(), and plot(); each returns NULL when the series is not stored in the fit (e.g. staggered fits, which keep their data per cohort in fit$cohort_fits).
  • Structural subclasses: fits with staggered adoption now additionally inherit from "coresynth_staggered", and multi-arm SI fits from "coresynth_multiarm". S3 methods (print(), summary(), tidy(), augment()) dispatch on these subclasses instead of branching on internal flags. The staggered/multi_arm list fields are retained, so existing code that reads them keeps working, and all class checks remain inherits()-compatible.
  • In-space placebo visualization (Abadie, Diamond & Hainmueller 2010, Section 3.4): mspe_ratio_pval() now returns an scm_placebo object (still fully backward compatible with the previous plain-list fields) that additionally carries the placebo gap path for every donor unit. A new plot.scm_placebo() method renders the two companion figures from the paper: type = "gaps" overlays the treated unit’s gap on the donor-pool placebo gaps (Figures 4-7, with mspe_prune implementing the paper’s relative pre-treatment MSPE pruning at 20x/5x/2x), and type = "ratios" plots the post/pre-treatment MSPE ratio for every unit (Figure 8).
  • Plot style customization: plot.coresynth() and plot.scm_placebo() gain colors, labels, vline, hline, and (for type = "weights") fill and top_n arguments. top_n restricts the weights bar chart to the top_n largest-weight donors (default Inf keeps every donor with a non-negligible weight), useful for large donor pools. vline/hline accept a list of geom_vline()/geom_hline() aesthetic overrides merged onto the built-in defaults, or NULL/FALSE to suppress the reference line entirely — since a line already added to a returned ggplot object cannot be removed, only restyled or overplotted, suppression has to happen inside the plot method. colors accepts a named vector overriding individual series colors (unmentioned series keep their default). labels accepts a named vector overriding the legend text of individual series (e.g. labels = c(Treated = "California")); keys are always the original series names, so colors and labels compose independently, and in type = "ratios" the treated unit’s axis tick follows the relabeled legend entry. All defaults reproduce the previous appearance exactly, so existing calls are unaffected.

Improvements

  • augment(fit, include_donors = TRUE) now returns donor rows for method = "si" fits too (previously it warned that control outcomes were unavailable and returned treated rows only). Estimates are unaffected.
  • plot() on a staggered fit now fails with a clear message explaining that staggered fits store their series per cohort, instead of an internal data.frame length error.
  • The gap plot (plot(fit, type = "gap")) now labels its y-axis simply “Gap” and states the definition (“Treated − synthetic control”) in a new subtitle, replacing the previous Y_treated - Y_synthetic axis label. The placebo gaps plot (plot(<scm_placebo>, type = "gaps")) uses the same “Gap” axis label — there each line is that unit’s gap relative to its own synthetic control, so the old parenthetical was dropped rather than reworded. Cosmetic only; no computed values change.

Bug fixes

  • For method = "tasc", plot(), augment(), and the Y_synth series of export_json() reported the average fitted value of all units as the counterfactual: TASC stores its fitted values as a full T x N matrix (Y_hat), unlike the other methods, and the shared extraction code averaged over every column. The treated unit’s columns are now used (synthetic_outcomes() handles this per method), so the plotted/augmented counterfactual, gap, and residuals for tasc fits change; the ATT estimate itself was always computed from the correct per-unit gaps and is unaffected.
  • scm_design() solved the weakly_targeted (eq. 9) and unit_level (eq. 10) designs by a sequential approximation: the treated weights w were always chosen to match the population average predictor vector alone, which made the xi penalty of eq. 10 effectively inert and left the eq. 9 objective jointly suboptimal. Both designs are now solved exactly for every candidate treated set — eq. 9 by two-block alternating minimisation of the jointly convex QP in (w, v), eq. 10 by folding the per-unit synthetic-control losses into the treated-weight QP target — and the solutions have been verified against an independent exact QP solver. This changes numerical results for scm_design(design = "weakly_targeted") and design = "unit_level" when m >= 2 (the default m = 1 selects a single treated unit, so w is degenerate and both old and new solvers agree).

Internal

  • conformal_inference()’s counterfactual refit now dispatches on the fit’s class (one S3 method per estimator) rather than an if-chain on the method string. Results are unchanged.

coresynth 0.2.4

CRAN release: 2026-07-04

Bug fixes

  • method = "mc" treated missing (id, time) panel cells and NA outcomes as observed zeros instead of excluding them from the observation mask used by the Soft-Impute solver. Missing cells are now masked out the same way as treated post-adoption cells. This changes numerical results for mc fits on unbalanced panels or panels with NA outcomes.
  • method = "tasc"’s EM loop already handles missing outcome cells (Kalman smoother plus per-unit M-steps), but its initialisation (svd(), the treated-unit loading OLS, and var()) failed on panels with missing cells. Initial values are now computed from a column-mean-imputed matrix; the EM loop itself still runs on the true, unimputed data.

Input validation

Malformed panels previously produced confusing or misleading errors — for example, a 0-row data frame (from an upstream filtering bug) was misclassified as staggered adoption and failed with an unrelated “predictors not supported” error, and missing (id, time) cells surfaced as a raw eig_sym(): decomposition failed from the C++ layer. panel_to_matrices() (shared by all six estimators) and scm_design() now validate and error clearly on:

  • 0-row input data
  • NA unit or time identifiers
  • NA or negative treatment indicator values
  • no treated units, or (for sharp fits only) no control units — staggered fits may legitimately have no never-treated units when future adopters serve as clean controls
  • missing (id, time) cells or non-finite outcomes, for the estimators that require a fully observed panel (scm, sdid, gsc, si — mc and tasc handle missing data by design)

scm_fit() also now rejects non-numeric (factor/character) outcome or treatment columns and non-integer treatment values, instead of silently coercing them (as.integer(factor(...)) returns level codes, not the original values). “All cohort-level fits failed” errors (SCM/SDID/GSC/SI staggered paths) now point back to the preceding per-cohort warnings for diagnosis.

coresynth 0.2.3

Performance

  • The inner simplex QP solvers (solve_simplex_qp() / solve_simplex_qp_lr()), used by every scm_fit(method = "scm") fit and by the method = "sdid" unit weights, now use FISTA with adaptive restart (O’Donoghue & Candès 2015, gradient scheme). The first-order solver’s iteration count grows with the condition number of Q = X0'VX0, which is large for the collinear pre-treatment outcome panels typical of synthetic control; resetting the momentum term when it works against the gradient removes this slowdown. Each inner solve converges to the same optimum (verified against an exact QP solver to within 1e-10), so the returned weights are unchanged.
  • For the common outcomes-only case the fit is effectively identical (differences ~1e-5). For v_selection = "oos" and penalised (lambda_pen) fits on ill-conditioned panels, the non-convex outer V search may now settle on a different local optimum, shifting results slightly; both the previous and the new solutions are valid SCM fits with the same objective. This can change numerical results for v_selection = "oos" and penalised fits on poorly conditioned data.

coresynth 0.2.2

CRAN release: 2026-06-26

Bug fixes

  • panel_to_matrices() (and therefore every scm_fit() method) and scm_design() now error on duplicate (id, time) entries instead of silently keeping a single arbitrary row. A balanced panel requires each unit-time cell to be unique; duplicates were previously overwritten by the last matrix-index assignment, dropping data without warning. The error reports the number of offending rows and the first duplicated unit and time.
  • plot() now renders Date/POSIXct time axes correctly. The time vector was coerced with as.numeric(), so dates appeared as days-since-epoch (e.g. 16000,
    1. on the x-axis. Date/POSIXct values are now passed through unchanged so ggplot2 selects the appropriate date scale; only character/factor time values are coerced to numeric.

coresynth 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.

coresynth 0.2.0

CRAN release: 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.

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.