Changelog

Source: NEWS.md

coresynth (development version)

A review of the GSC, TASC, SI and SDID code against their papers and against gsynth/fect. Estimates from method = "gsc" with covariates, from method = "tasc", and the intervals of sdid_inference(), gsc_inference(), si_inference() and gsc_boot() change.

Bug fixes

• GSC with covariates no longer stops before its estimates settle. The loop that alternates the factors and the covariate coefficients updated beta by plain alternating least squares and stopped silently after 50 iterations. When the covariates move with the factors – the design of Xu (2017)’s own simulations – that left beta at (0.79, 2.77) against a true (1, 3) and biased the effect by +0.06. beta is now updated with the loadings concentrated out (Bai 2009), which reaches the same fixed point in a few dozen iterations; the loop runs to a tolerance of 1e-8 within at most 1000 iterations and warns if it does not get there. gsc_ife_cpp() reports n_iter and converged.

• gsc_inference() and si_inference() resample the treated units, not only the controls. Holding the treated units fixed left their own noise out of the standard error: with one treated unit, a 95% interval covered the truth 30% of the time for GSC, and 43% with five treated units for SI. The bootstrap now draws treated and control units separately, and the jackknife drops each of them in turn. A fit with a single treated unit (in any arm) is an error that points to gsc_boot() or conformal_inference(), since no resampling of units can reach that unit’s noise. Coverage in simulations is now 0.90-0.96 for GSC and 0.85-0.93 for SI.

• On staggered fits, the bootstrap draws the controls once for all cohorts. It used to redraw them independently for each cohort, although under the default control_group = "never_treated" every cohort shares the same donors, which dropped the correlation between the cohort effects (a 95% interval covered 42% of the time). Treated units are drawn from all cohorts together, so the cohorts and their weights are rebuilt from each draw, as in Clarke et al. (2024, Algorithm 2). Coverage is now 0.93-0.95.

• **sdid_inference() follows Arkhangelsky et al. (2021) and Clarke et al.

  2024. for the bootstrap and the jackknife.** The bootstrap resampled the controls only, holding the treated mean and zeta2 fixed, and on staggered fits redrew the controls separately for each cohort; the jackknife dropped only controls and re-estimated the weights. Their 95% intervals covered 0.68-0.90 of the time. Now method = "bootstrap" (Algorithm 2) draws units with replacement from the whole panel, treated and control alike, redraws a sample without a treated unit or with fewer than two controls, and reruns the whole estimator – the covariate partial-out, zeta2 (unless fixed in scm_fit()) and both sets of weights – with the cohorts rebuilt from the treated units drawn. method = "jackknife" (Algorithm 3) drops every unit in turn with the fitted unit and time weights held fixed. Coverage is now 0.88-0.97. Both need more than one treated unit and point to method = "placebo" otherwise, which is unchanged. This is the same scheme sdid_event_study() already used, so the two now agree.

• The jackknife of a staggered fit needs two treated units in every cohort for sdid_inference(), gsc_inference() and si_inference(): dropping a cohort’s only treated unit leaves its effect undefined (Clarke et al. 2024, Algorithm 3), and such a fit now raises an error that points to the bootstrap.

• The per-cohort jackknife of a staggered multi-arm SI fit no longer inflates its standard error. It took the variance of each cohort’s weighted sum over arms rather than of its weighted mean, which made the standard error about 20 times too large.

• gsc_boot() follows Xu (2017, Algorithm 2). It drew independent normal errors with a single variance, which ignored serial correlation (an interval covered 77% of the time when the errors had an autocorrelation of 0.5) and gave the treated units the in-sample error of the controls. It now resamples whole residual series: the controls’ in-sample residuals, and for the treated units the prediction errors of control units left out as if they were treated. Coverage is 0.92-0.93 in both cases.

• TASC estimates its state-space model by the exact EM algorithm. The M-step used the smoothed state means only, leaving out their covariances. That understates the state noise, and the understatement compounded until the state noise collapsed to zero within the 20 iterations the fit ran. The M-step now follows Rho et al. (2026, Algorithm 7) – smoothed covariances included, the initial state learned, diagonal noise covariances – with the missing cells of Shumway & Stoffer (1982), and runs until the fitted values settle. em_iter is now the maximum number of iterations (default 2000), em_tol (default 1e-6) sets the tolerance, and a fit that does not converge warns.

• conformal_inference() keeps the covariates of a GSC fit. It refitted the factor model without them, so an interval for a fit with covariates was computed on a different model.

• scm_design() uses the order-statistic quantile of Abadie & Zhao (2026, Equation 18) for its split-conformal intervals, instead of interpolating below it, which narrowed the intervals when there were few blank periods. When it samples permutations it no longer resets the random number generator of the calling session.

New features

• method = "gsc" takes additive fixed effects through force: "unit", "time" or "two-way" (default "none"), the extended model of Xu (2017, Remark 3). With additive effects r = 0 is allowed, and the cross-validation of r starts from it. Estimates match gsynth to machine precision for each of these choices.

• TASC gives each unit its own intercept in the observation equation (intercept = TRUE, the default). Without one, the states have to carry each unit’s level, and on panels far from zero the EM drifted for tens of thousands of iterations, moving the effect by up to 0.4 depending on when it stopped. intercept = FALSE fits the paper’s model as written.

Internal

• Staggered GSC fits the never-treated donors once rather than once per cohort, and sdid_inference(), gsc_inference() and si_inference() share one resampling routine. GSC and SI staggered fits record control_group; SDID staggered fits record zeta2_user and zeta_t, and the raw outcomes and covariates when they have covariates.

coresynth 1.1.0

New features

• sdid_event_study() computes period-by-period SDID treatment effects with cluster or unit bootstrap inference. For a sharp method = "sdid" fit, it returns the event-study series aligned on the lambda-weighted pre-treatment gap – so the post-treatment mean of the period effects equals fit$estimate (the ATT) – alongside bootstrap standard errors and both normal-approximation and percentile confidence intervals for every period and for the overall ATT from the same bootstrap draws:

  • cluster accepts a column name in data (or one-sided formula ~ col) or a named vector keyed by unit ID (positional unnamed vectors are rejected so unit-to-cluster alignment never depends on row order). When cluster = NULL (default), individual units are resampled.
  • Each bootstrap draw resamples both treated and control clusters/units with replacement, treats duplicated units as distinct columns, and re-estimates sigma_hat, zeta2, unit weights omega, and time weights lambda. Draws in which either the treated or control group is empty (or has fewer than two controls) are discarded, and n_boot_valid and n_boot_dropped report the valid and discarded counts.
  • boot_gaps (n_boot_valid x T matrix) and boot_att (length n_boot_valid vector) store the replication draws for full reproducibility with seed.
  • plot(), plot_data(), tidy(), and glance() methods format and plot the event-study series with point estimates, confidence intervals, zero and treatment reference lines, and observed-period axis breaks that handle non-consecutive time indices cleanly.

• sdid_inference() documents and enforces its control-only bootstrap scope. Its method = "bootstrap" resamples control units only with the treated mean series and zeta2 held fixed; passing cluster to sdid_inference() now raises an informative error pointing to sdid_event_study() (and notes when a non-bootstrap method was selected).

coresynth 1.0.1

CRAN release: 2026-10-11

Bug fixes

• gsc_boot() reports a confidence interval for the ATT. Its replicates are generated with no treatment effect, so they are draws of the estimation error (Xu 2017, Algorithm 2), and the interval is the estimate minus their quantiles. ci_lower/ci_upper were the raw quantiles instead – an acceptance region around zero that never contained a non-zero estimate. ci_lower/ci_upper change; p_value, se and boot_dist do not.

• scm_fit() rejects a transformed outcome or treatment in the formula. The columns are read from data by name, so log(y) ~ d | id + time was fitted as y and y ~ I(t > 1990) | id + time used t as the treatment, without a word. Both are now errors that ask for the variable to be created in data first.

• The parallel placebo loops cannot take the R session down. An exception thrown inside an OpenMP region terminates the process; the outcomes-only SCM and the SDID placebo loops now catch it and mark that placebo NaN, as the predictor-spec SCM loop already did.

• OpenMP honours a thread limit. The placebo loops and the multi-start search used every core, which the CRAN Repository Policy forbids during checks. options(coresynth.threads = n) now caps the thread count, and R CMD check --as-cran runs on at most two threads (see ?coresynth-package). Results do not depend on the thread count.

• R CMD check runs the test suite. tests/testthat.R was missing, so the checks (locally and on CI) never ran the tests.

Performance

• SDID weights are solved without forming the donor Gram matrix. The unit weight QP has one row per pre-treatment period and a column per donor, so with more donors than periods the gradient and the step size now come from the factor instead of an N-by-N matrix and its eigendecomposition. Same iterates up to round-off (estimates, standard errors and p-values agree to ten digits); on a 300-donor panel the placebo, jackknife and bootstrap methods of sdid_inference() run about 6 times faster.

• The TASC Kalman filter avoids an N-by-N inverse per period. The observation noise is diagonal, so the gain reduces to an r-by-r solve by the push-through identity. Same moments up to round-off; a 200-unit fit runs about 10 times faster.

coresynth 1.0.0

Breaking changes

• Staggered fits of "scm", "sdid", "gsc" and "si" now take their donors from the never-treated units only (control_group = "never_treated"). The old default, "clean", also admitted units that adopt after the cohort. Each cohort’s effect is averaged from its adoption date to the end of the panel, so those units were treated themselves during part of that window, and their post-adoption outcomes entered the cohort’s counterfactual. With a constant effect of 3 in a three-cohort simulation the aggregate ATT came out 0.24 to 0.59 too low depending on the method, against a bias of at most 0.04 with never-treated donors. Neither source design does this: Clarke et al. (2024, Algorithm 1) subset to pure controls, and Ben-Michael, Feller & Rothstein (2022, S.2.2) admit a donor only if it is untreated through the last event time estimated. Staggered estimates from these four methods change. "clean" still runs but warns and will be removed in the next major release. A staggered panel with fewer than two never-treated units is now an error that points to "mc" and "tasc", which impute from untreated cells and need no donor pool.

• Covariate partial-out now includes unit and time fixed effects. The covariates argument of "sdid" (sharp and staggered) and of staggered "scm" with cov_method = "partial_out" implements the projection of Kranz (2022) that Clarke et al. (2024, S.2.2) adopt: regress the outcome on the covariates and two-way fixed effects over the untreated cells, then subtract X * beta. The fixed effects were missing, so beta also absorbed how the covariates’ unit levels and trends line up with the outcome’s, and subtracting it wrote a spurious differential trend back into the series. In a simulation with a true coefficient of 1, beta_hat was 3.75 and the staggered SCM and SDID estimates of an effect of 2 were 1.65 and 1.18; they are now 1.00, 2.01 and 2.01. Sharp SDID also regressed on the control units’ pre-treatment cells only; it now uses every untreated cell, the default of Kranz’s own implementation, as staggered fits did. Estimates with covariates change. A covariate that varies only across units or only over time is absorbed by the fixed effects; it now gets a zero coefficient and a warning (for staggered SCM, cov_method = "balance" balances such covariates instead).

• scm_inference() defaults to the leave-one-unit-out jackknife of Ben-Michael, Feller & Rothstein (2022, Appendix A.1), which drops each unit of the panel, donors included, refits with nu held fixed, and uses the spread of the estimates. It refits once per unit – about 0.7 s at 100 units and 3 s at 200 – skipping donors that carry no weight in any block, whose removal leaves the solution unchanged, and warm-starting the rest from the full fit. It warns below 3 treated units, where it over-rejected mildly (8.5% at a nominal 5%). The result carries the leave-one-out estimates in jack_ests.

• **method = "wild_bootstrap" now perturbs every unit, as Otsu & Rai

  2017. prescribe.** The bootstrap BFR (2022, S.5.3) use adapts Otsu & Rai’s wild bootstrap for matching estimators, whose key step is to resample “not only the regression residuals of treated and untreated observations but also the ones to estimate the average treatment effects” (their Eq. 9). The previous implementation perturbed only the treated units’ deviations from the ATT, so it missed the error of the synthetic control that every treated unit of a cohort shares, and under-covered: a nominal 95% interval covered 80% with 8 donors and 8 treated units per cohort, and 45%, 63%, 82% and 91% with 2, 3, 4 and 8 treated units. Each donor now carries its own multiplier on its residual, weighted by how much the synthetic controls use it, and only the treated units’ heterogeneity is centred at the ATT – as in Otsu & Rai, and unlike BFR’s Eq. (13) as printed and augsynth, which centre every unit and so widen the interval in proportion to the effect. Residuals are against the two-way model of the untreated outcome, so a common period shock does not widen it either. On the same designs it covers 93% to 99.5% (93% with 2 treated units); it is conservative where outcomes carry unit-specific trends the two-way model does not absorb. It needs no refitting, which makes it the option for panels too large for the jackknife, and now warns below 3 treated units rather than 5. Its intervals change.

• Staggered SCM fits store every unit’s adoption date (T_adopt), and scm_balance_frontier() and the new jackknife rebuild the panel from it. Rebuilding it from cohort_fits, as before, turned the treated units of a cohort the fit had skipped (for too short a pre-treatment window) into never-treated donors for the refits.

• "gsc", "mc" and "si" now choose their tuning parameter the way their source papers say to. All three picked it by a fixed rule that appears nowhere in the literature: r = 2 factors for GSC, a penalty of 1% of the spectral norm for MC, and k = floor(sqrt(min(T_pre, N_co))) components for SI. Each paper specifies a data-driven selection instead, and each is now the default, so estimates from all three change unless the parameter is supplied explicitly.

  • "gsc": r = NULL runs the leave-one-period-out cross-validation of Xu (2017, Algorithm 1) — for each candidate r, hold out one pre-treatment period, refit the treated units’ factor loadings on the rest, and score the prediction. Xu presents this as central to the method (“it avoids specification searches”). On panels built with a known number of factors it recovered r exactly for 1 through 4, where the old fixed r = 2 was right only by coincidence. The scoring table is kept in fit$r_cv. Candidates start at 1: r = 0 is not representable by a rank-r truncated SVD.
  • "mc": lambda = NULL runs the K-fold cross-validation of Athey et al. (2021, S.4), drawing folds so the observed fraction inside a fold matches the panel’s and scoring each penalty on the held-out cells. It uses its own RNG stream, so the penalty is reproducible and the caller’s random state is left untouched; cv_folds, cv_nlambda and cv_seed control it, and fit$lambda_cv reports whether the search ran.
  • "si": k = NULL applies the optimal hard threshold of Gavish & Donoho
    2014. to the pre-treatment donor block, one of the three data-driven rules Agarwal et al. name. It costs a single SVD, where cross-validating k would cost a PCR fit per candidate. On the same known-rank panels it recovered the rank exactly for 1 through 4; the old heuristic returned 4 in every case. A staggered fit applies the threshold to each cohort’s own donor block, and clips a supplied k to that cohort’s dimensions, so fit$k is one value per cohort, named by adoption date.

• Staggered "mc" and "tasc" fits now report the same estimand as every other method. Both estimators recover the counterfactual by imputation: their observation mask already blanks each treated unit from its own adoption date, so a staggered panel needs no cohort-by-cohort refitting and the single fit was, and remains, a valid staggered fit. What was wrong was the aggregation. They averaged the per-unit post-treatment gaps with one weight per treated unit, while "scm", "sdid", "gsc", and "si" use the N_treated,g * T_post,g weights of Clarke et al. (2024). The two agree only when every cohort has the same size and the same number of post-treatment periods; otherwise the same panel yielded a materially different number from "mc" and "tasc" than from the other four, with nothing to indicate it. They now use the cohort weights as well, so staggered "mc" and "tasc" estimates change. Sharp fits are unaffected and return bit-identical results.

  The fits also declare themselves properly. They gain staggered = TRUE, cohort_estimates, and cohort_fits, and inherit from "coresynth_staggered". Consequently glance() reports staggered = TRUE rather than FALSE, tidy() returns the per-cohort table instead of an empty data frame, augment() returns one series per cohort with .period keyed to that cohort’s own adoption date, and plot() errors and points at augment(), as it already did for the other staggered fits. Most consequentially, conformal_inference() rejects them: a staggered "mc" fit previously passed its sharp-only guard unnoticed and returned a p-value computed against a single global treatment date.

• method = "scm" now fits panels where several units adopt at the same date, instead of erroring. scm_fit() refused any sharp panel with more than one treated unit, while the same units with adoption dates one period apart went down the staggered path and fitted without complaint. Simultaneous adoption is one cohort, and S.4.1 of Ben-Michael, Feller & Rothstein (2022) shows that the across-unit variation putting unit-level fits into the ATT error bound vanishes when the adoption date is shared, so the bound is minimised by pooling those units: one synthetic control for their average. That is the step their Appendix A.2 hierarchy already takes within each cohort, and the one the staggered path here already took.

  The fit reports the treated average in Y_treat and keeps the raw per-unit series in Y_treat_mat, alongside n_treated, which print(), summary(), glance() and export_json() all report. Donor weights stay a single simplex vector, so predictors, v_window, lambda_pen, v_selection = "oos", plot(), placebo_in_time(), loo_donors() and augment_scm() all work as they do for one treated unit. Each pred() row of the treated predictor vector is the average over the pooled units, under the same weights the outcome rows are pooled with, so the predictor and outcome sides describe one target series rather than two.

  mspe_ratio_pval() does not, and now says so, pointing at scm_rank_pval() below. It compares the treated unit against placebo runs on single donors, and an average of several treated units is less noisy than any one donor, which inflates its pre/post MSPE ratio. Simulated under no treatment effect, the test rejected at 28% with three pooled units and 53% with six, against a nominal 10%.

• Staggered "scm" defaults to the pooling heuristic and the intercept shift. nu defaulted to separate per-cohort SCM and fixedeff to FALSE. Ben-Michael, Feller & Rothstein argue against exactly that combination: separate SCM is “unlikely to yield good results” (abstract), S.4.2 advises against values of nu near 0 or 1, and S.6 reports that the intercept-shifted estimator “dominates either of the alternatives”. Their own implementation, augsynth, defaults to both. So staggered SCM estimates change; nu = "separate" and fixedeff = FALSE restore the old behaviour, and nu gained "separate" as a name for the older per-cohort path so that 0 can mean what it means in the paper.

  Reproducing their S.6 design – 49 units, 39 periods, selection into treatment through the unit effects, about 20 treated units, a sharp null – bears this out. Under the two-way fixed effects model the heuristic improved on separate SCM in bias, MAD and RMSE (MAD 0.0395 against 0.0413); under the factor model full pooling was much worse than the heuristic (MAD 0.1492 against 0.0607), as the paper reports. The gain depends on selection into treatment: in a panel where treatment is assigned at random the heuristic is the worse choice, which is worth knowing before reaching for it.

  The default falls back to "separate" where the pooled solver cannot go (donor_mspe_threshold, lambda_pen, v_selection = "oos"); naming a numeric nu there is still an error. The fallback reports itself in a message naming the argument that caused it, rather than changing the estimator in silence, and fit$nu records which of the two ran: the number the pooled solver settled on, or "separate". The heuristic itself was checked against augsynth and agrees to four or five decimal places, across cohorts of equal and unequal size alike.

• qp_solver now defaults to "auto", which is the Wolfe solver wherever the inner QP can be degenerate. The inner SCM problem projects the treated unit onto the convex hull of the donor columns in predictor space, so by Caratheodory’s theorem an optimum supported on at most k + 1 donors always exists, and Wolfe (1976) returns such a solution. "active_set" returns an exact optimum too, but when the QP is degenerate — fewer predictors than donors in the support, where the optimal set is a face rather than a point — it breaks the tie on round-off, so its weights can move with the arithmetic. This was opt-in since 0.4.0 precisely because it changes those weights; 1.0.0 is where that change belongs.

  On a degenerate predictor spec the difference is not only which optimum is reported. In a 30-donor test panel with one or two aggregated predictors, Wolfe reached a lower pre-treatment loss than the active-set path (2.75 against 3.25, and 2.79 against 3.31) on 3 and 2 non-zero donors rather than 13 and 17 — the sparser, more stable inner solutions give the non-convex outer V search a cleaner objective to descend.

  Degeneracy needs few rows next to the donor pool, so "auto" is Wolfe for every predictor fit and for outcomes-only fits with fewer than half as many pre-treatment periods as donors. The other outcomes-only fits have a unique optimum in practice, which both solvers return, and there "auto" keeps the active set: Wolfe cold-starts each of the coordinate sweep’s inner problems and was three times slower for identical weights.

  "auto" also falls back to "active_set" on the paths that run their own QP outside the sharp inner solver (v_selection = "oos", lambda_pen, v_optim = "bfgs", and staggered adoption). Naming "wolfe" explicitly on one of those is still an error rather than a silent downgrade, and fit$qp_solver records the value actually used.

• The default vline_offset in plot.coresynth() and plot.scm_placebo() is now -1 instead of 0, moving the treatment line from the first post-treatment period to the last pre-treatment one. At offset 0 the treatment is already in effect, so the treated path and its counterfactual have usually separated by the time the line is reached and the figure reads as though the gap opened before treatment. With -1 everything left of the line is the period the fit was asked to match. Pass vline_offset = 0 for the previous position.

• scm_fit() now takes ... ahead of its estimator-specific arguments, which must therefore be named in full; partial matches such as v_opt = and any use of the fourth or later argument by position no longer work. This is what makes lambda reach method = "mc": while lambda_pen (an SCM-only argument) sat before ..., R matched lambda to it partially, so MC’s nuclear-norm penalty could not be set through scm_fit() at all and the value silently went to an argument MC ignores.

• scm_fit() rejects arguments the chosen estimator does not take. One formula interface serves six estimators, so an argument meant for another one — predictors or nu with method = "mc", covariates or fixedeff with "tasc", control_group with either — or simply misspelled, was absorbed by ... and changed nothing about the fit that came back. These now error and name the methods the argument applies to.

• Sharp method = "scm" rejects covariates instead of ignoring it. The partial-out ran inside the staggered branch only, so on a single-cohort panel the argument reached the fit function, was never read, and returned an estimate identical to the one without it – the same silent no-op the argument check above was added to stop, one level deeper. It is not an oversight that SCM has no partial-out: SCM balances covariates rather than residualising them. Abadie, Diamond & Hainmueller (2010, S.2.3) put them in the V-weighted predictor distance, and Ben-Michael, Feller & Rothstein (2022, S.5.2) add a separate balance term with its own weight, noting in S.4 that “where Abadie et al. (2010, 2015) balance auxiliary covariates, we focus exclusively on lagged outcomes”. Partialling out is the device of Clarke et al. (2024, S.2.2), which SDID needs because it has nothing to balance with.

  A sharp fit does have something: predictors = list(pred("x", <pre-treatment times>, "mean")) balances a covariate on its pre-treatment aggregate, which is how BFR carry a time-varying covariate into the balance problem too. The error names that route. Staggered SCM keeps the partial-out, because predictors is unavailable there.

• pool_cohorts now takes effect on the per-cohort staggered path. It was read only by the partially pooled solver, so pool_cohorts = FALSE was silently discarded whenever the fit went down the nu = "separate" path – including the automatic fallback that donor_mspe_threshold, lambda_pen and v_selection = "oos" trigger. The fit then carried cohort-level blocks the caller had asked not to have, and scm_rank_pval(), which needs one synthetic control per treated unit, rejected it with a message telling the caller to pass the argument they had just passed. Both staggered paths now build their blocks the same way, and fit$pool_cohorts records it. Sharp fits reject the argument, as they already did nu and fixedeff.

• Partially pooled staggered SCM weights each cohort by its size. The two imbalance measures nu trades off, and the heuristic that sets it, treated every cohort as equally important however many units it held. Appendix A.2 of Ben-Michael, Feller & Rothstein (2022) – the hierarchy that pools units sharing an adoption date, which is what pool_cohorts = TRUE implements – writes the cohort problem on the sum over the cohort’s units: its target is sum_i 1{T_i = T(g)} Y_i and its weights satisfy sum_i gamma_ig = n_g. A cohort’s residual is therefore n_g times its mean gap, so a cohort of n_g units counts n_g times towards the pooled imbalance and n_g^2 towards the separate one. Weighting them all equally is neither, and it is also not what the fit aggregates with, since the cohort ATTs combine with the N_treated,g * T_post,g weights of Clarke et al. (2024).

  Staggered "scm" estimates change where the cohorts differ in size. Panels whose cohorts are all the same size, and panels with one treated unit per adoption date, are unaffected – the weighting is a common factor there, and the objective is normalised. augsynth, the authors’ implementation, already followed the appendix; on a panel with cohorts of 1 and 4 its heuristic returned 0.94114 where this one returned 0.93563, and it now returns 0.94113. Across three further panels with cohorts of 1, 2 and 8 the two now agree to four decimal places, where they previously differed by as much as 0.025.

• weights now reaches the objective, not just the aggregation. A staggered fit multiplied the supplied mass into each cohort’s aggregation weight while still fitting its weights to the unweighted average of that cohort’s treated units, so with unequal weights inside one cohort the reported ATT was neither the equally weighted quantity nor the weighted one. The mass now sets the block’s target series, its weight in the partially pooled objective, and its T_post-multiplied aggregation weight alike, and scm_inference() resamples each treated unit at its own mass rather than splitting the block’s evenly. scm_balance_frontier() refits on the same weighted objective, where it previously ignored weights entirely. Staggered "scm" estimates change when weights is supplied; equal weights reproduce the unweighted fit exactly.

• "mc" estimates unit and time fixed effects outside the nuclear norm. The S.4 model these fits implemented, Y = L + e, leaves the panel’s level inside the penalised matrix, so the nuclear norm shrinks it along with the factor structure and the imputed counterfactual is pulled towards zero. On a panel shifted to a level of 20 the ATT came out at +0.230 against a true 2 (z = 4.5 over 40 draws). S.8.1 of the same paper keeps unit and time effects out of the penalty – “we regularize the estimates of L*, but do not wish to regularize the estimates of the fixed effects” – and MCPanel, the authors’ own implementation, estimates them by default. Doing so removes the bias (-0.020, z = -0.4) and cuts RMSE by about 30%. The previous behaviour matched MCPanel with its fixed effects switched off, and the new behaviour matches its default, to within simulation error in both cases.

  fixedeff = FALSE recovers the S.4 baseline. The fit gains fe_unit, fe_time and L_lowrank; L_hat remains the completed matrix and now includes the fixed effects. conformal_inference() refits under whichever model the fit used.

  This also corrects the note published with the cross-validated penalty above: the small-panel upward bias recorded as a known limitation was this misspecification, not a property of nuclear-norm regularisation. The debiasing of Choi & Yuan (2024, Eq. 3.1) was tested against it and only halves it (+0.123, z = 2.5), adding nothing once the fixed effects are in.

New features

• scm_rank_pval() tests the aggregate effect when several units are treated (Abadie & L’Hour 2021, S.3.2). Each treated unit contributes its own statistic, the identities of the treated are reassigned at random, and the sum of the ranks of the real treated units is compared with its permutation distribution. Ranking is what makes this work where mspe_ratio_pval() cannot: every statistic being compared comes from a single unit, so the averaged treated series never enters. Simulated with no treatment effect it rejected at 7.0% and 10.0% against a nominal 10% (three and six treated units), where mspe_ratio_pval() on the same fits reached 28% and 53%; with an effect of 1.5 its power was 77% and 85%.

• mspe_ratio_pval() can discard ill-fitting placebo runs. Abadie, Diamond & Hainmueller (2010) report their permutation distribution at cutoffs of 20, 5 and 2 times the treated unit’s pre-treatment MSPE, on the grounds that a placebo run the synthetic control could not track before treatment says nothing about how rare a large post-treatment gap is. That cutoff existed only in plot.scm_placebo(), where it changed the picture but not the p-value. mspe_prune now applies it to the test, and n_placebo_pruned reports how many runs it dropped. This is a different control from mspe_threshold, which is a floor guarding the ratio’s denominator against a near-zero pre-treatment MSPE.

  statistic = "post_mspe" adds the alternative of Abadie (2021, S.3.5): rank the post-treatment MSPE itself, rather than its ratio to the pre-treatment MSPE, once the ill-fitting runs are gone. On a panel carrying five donors no synthetic control can track, that statistic gives p = 0.150 unpruned – the unfittable donors sit at the top of the distribution on size alone – and p = 0.029 at mspe_prune = 5. The ratio statistic reaches p = 0.025 without any cutoff, which is the advantage ADH claim for it in their Figure 8.

  plot() and plot_data() on the result now default to whichever cutoff the test used, so the figure and the p-value describe the same set of placebo runs. This holds for type = "ratios" as well as type = "gaps": the subtitle reports the number of runs the p-value rests on, so a figure drawing the discarded ones contradicts its own caption. Pass mspe_prune = Inf to draw them all regardless.

  plot(type = "ratios") also draws whichever statistic the test ranked, so a statistic = "post_mspe" result gives the post-treatment MSPE rather than its ratio, with the axis and title to match. plot_data(type = "ratios") returns both statistics, in ratio and post_mspe.

• pool_cohorts = FALSE fits one synthetic control per treated unit. Staggered SCM pools units sharing an adoption date into a cohort, which is the Appendix A.2 hierarchy of Ben-Michael, Feller & Rothstein (2022). Their S.4 formulation instead gives every treated unit its own weight vector, and that is what pool_cohorts = FALSE fits; cohort_fits then names the unit behind each block, which is what scm_rank_pval() needs. The default stays with the appendix, where S.4.1 shows there is no across-unit variation to exploit inside a cohort. (augsynth defaults the other way.)

  Those blocks are labelled by unit rather than by adoption date, because several of them share one: cohort_estimates carries a unit column, tidy() returns unit_<id> terms, and augment() keys .unit on it. Labelling them by cohort would have given two treated units the same key, which merges their series wherever it is grouped or drawn on. A fit whose blocks are cohorts keeps the labels it had, and the unit column names the unit of a one-unit cohort and is NA for a pooled one. Estimators without the notion carry no such column.

• weights sets how much each treated unit counts towards the ATT. Abadie (2021, S.8) notes that with regions or countries a population- or GDP-weighted average effect is often the quantity of interest. A staggered fit multiplies the supplied mass by each block’s T_post; a sharp fit makes the pooled target a weighted average of the treated series. Left NULL the units count equally, as before.

• scm_balance_frontier() and plot() trace the pooling trade-off. Ben-Michael, Feller & Rothstein call this plot “an important tool for using partially pooled SCM in practice” (S.4.2). It refits across a grid of nu and reports the imbalance of the treated average, which the aggregate ATT rests on, against the root mean square of the per-block imbalances, which the block-level effects rest on. The curve is strongly convex in practice, so it shows where more pooling stops paying for itself: on a test panel, moving nu from 0 to 0.5 cut the pooled imbalance by 21% for a 6% rise in per-block imbalance, while the last quarter cost 39% for a further 8%.

  This is worth looking at because the nu heuristic does not always pick well. Where treatment is assigned without selection on the unit effects, every block already fits to the noise floor and the block-level gaps are near-independent, so their average shrinks by about 1/sqrt(J) and the heuristic returns roughly that – a number driven by noise averaging rather than by imbalance pooling could remove. Separating the two would need an estimate of the noise floor, which is neither in the paper nor in augsynth, so the default is unchanged and the frontier is offered instead.

• print() and summary() describe staggered fits as staggered. Both previously used the sharp output, which reports T_pre and T_post from the panel-level split — the earliest adoption date, correct for one cohort and wrong for the rest — with nothing to say the fit was staggered at all. print() now labels the method (staggered) and gives the cohort count in place of that split, and summary() prints the per-cohort table of estimates, weights, and treatment windows. Multi-arm SI fits show the cohort-arm table on the same terms.

• cov_method = "balance" fits the auxiliary-covariate form of staggered SCM (Ben-Michael, Feller & Rothstein 2022, S.5.2, Equation 11). Every SCM paper handles covariates by balancing them – Abadie, Diamond & Hainmueller put them in the V-weighted predictor distance, and BFR note in S.4 that “where Abadie et al. (2010, 2015) balance auxiliary covariates, we focus exclusively on lagged outcomes” before reintroducing them this way. Partialling out, which is what covariates has always done here, is the device of Clarke et al. (2024, S.2.2) that SDID needs because it has nothing to balance with; staggered SCM borrowed it only because predictors is unavailable on that path.

  Each covariate’s pre-treatment average now enters the partially pooled objective as a second imbalance measure, pooled and per-block like the lagged outcomes and carrying the same cohort masses, with xi weighting the two sides. xi defaults to the paper’s rule, the sample variance of the donor units’ outcomes before the last adoption date; both normalisation constants are the combined measure at the nu = 0 solution, as their footnote 7 defines. fit$pooling gains q_cov_sep, q_cov_pool and n_covariates, and scm_balance_frontier() traces the objective including them and returns them as columns of its own. Read its relative columns accordingly: they divide by the combined constant, so they are not 1 at nu = 0, and the two outcome imbalances can both fall as nu rises because the covariate side is the third quantity being traded off.

  It needs the partially pooled solver, so it is refused alongside nu = "separate", donor_mspe_threshold, lambda_pen and v_selection = "oos" rather than falling back silently. xi is refused on the same grounds wherever the balance term is not in the objective – a sharp fit, or cov_method = "partial_out" – instead of being accepted and ignored. "partial_out" remains the default, so no existing fit changes.

  As xi falls to zero the fit collapses onto the outcomes-only one at a rate linear in xi, which is the check that the covariate side reduces exactly to the existing path; raising it trades outcome balance for covariate balance monotonically, which is the trade-off the equation describes.

Bug fixes

• Outcomes-only SCM is fast again. Since 0.4.1 the default fit and its in-space placebo test had run about 3 times slower on panels with many pre-treatment periods, and 1.0.0’s Wolfe default had made it 10 to 14 times slower. Two causes, both fixed without changing any result (weights, estimates and placebo statistics agree with the previous build to 1e-13): the exact inner solve that seeds the coordinate sweep started the active set from all donors at once, paying a face solve per donor it dropped, and now reaches the same optimum from the support a gradient presolve finds; and the sweep over a panel with at least half as many periods as donors now carries the donor Gram matrix with rank-1 updates, where the predictor-oriented factor form rebuilt each face’s Gram from scratch. On a 100-donor, 96-period panel a fit takes 0.013 s and its placebo test 0.4 s, against 0.19 s and 5.6 s before.

• The partially pooled staggered SCM solver no longer decomposes an N-by-N matrix per block update. Each block’s QP matrix is the Gram of a factor with one row per lag, whose small Gram gives the same Lipschitz constant; solve_simplex_qp() now accepts it. Estimates are unchanged (to 1e-14); a 200-unit fit takes 0.08 s instead of 0.13 s, and its jackknife 3 s instead of 27 s.

• A staggered fit with covariates reports its treated series on the scale it was fitted on. The covariates are partialled out first, and every other series the fit carries – the per-cohort targets, Y_all/Y_mat, and the sharp path’s own Y_treat – is on that residualised scale, but the staggered Y_treat was the raw outcome. treated_outcomes() therefore returned one series while augment() returned another. Affects "scm" and "sdid"; without covariates the two scales coincide and nothing changes.

• mspe_ratio_pval() names mspe_post_placebo by donor. Its companion mspe_pre_placebo was named and it was not, so the two could not be lined up by name although the documentation presents them as a pair.

• loo_donors() re-solves with the inner solver the fit used. It always called the active-set method, so on a degenerate predictor spec – where the optimal set is a face rather than a point and the two solvers pick different points of it – dropping a donor also changed which optimum was reported, mixing the two effects the exercise is meant to separate.

• conformal_inference() no longer substitutes a retired rule for a tuning parameter a fit does not carry. The refit has to impose the model the fit used, but r, k and lambda each fell back to the fixed heuristic that was this release’s default before it was replaced – r = 2, k = floor(sqrt(min(T_pre, N_co))), 1% of the spectral norm – which would score the null against a differently specified counterfactual. Every current fit records its own, so the fallback was unreachable; it is now an error naming the parameter. The fields are also read exactly rather than with $, which would partial-match r to r_cv, or lambda to lambda_cv, if the exact name were ever absent.

coresynth 0.5.0

CRAN release: 2026-08-25

New features

• linetypes in plot.coresynth() and plot.scm_placebo() makes the line type of each series settable, the third member of the colors/labels family and keyed the same way. Line types were previously hard-coded, so the treated series was always solid and the synthetic control always dashed; drawing both solid – the usual choice when the two paths are already distinguished by color, or when the figure is going into a document that separates series by color alone – meant rebuilding the plot from plot_data().

  For type = "trend" it takes a named vector over "treated", "synthetic", and (with show_donors > 0) "donors", e.g. linetypes = c(synthetic = "solid"); series not mentioned keep their default. For type = "gap" it takes a single line type for the gap line, mirroring how colors takes a single color there. For plot.scm_placebo(type = "gaps") the keys are "treated" and "placebo". Values are ggplot2 line type names ("solid", "dashed", "dotted", …) or the equivalent integer codes 0:6; an unknown key errors with the list of valid keys, as for colors and labels. Bar charts ("weights", "pred_weights") and the point chart ("ratios") ignore the argument.

  All defaults reproduce the previous appearance exactly. The argument sits after labels in both signatures, so the arguments that follow it (vline, vline_offset, hline, …) shift by one position for anyone passing them positionally rather than by name.

Changed

• A near miss on the 0.4.1 outcomes-only routing is now reported. Whether a per-period outcome spec is the outcomes-only fit depends on the pre-treatment periods data carries, not on the span of the pred() list, so the same predictors argument switches paths when data reaches further back – widening the panel for a plot is enough. Sharp SCM fits whose predictors are all single-period outcome lags but do not cover the pre-treatment window exactly now say so, report the coverage, and point at v_window and at subsetting data as the two ways to move the window the fit is judged on. Both fits were already correct for the model each one states; what was missing was any sign that the model had changed.

• pred() time windows are checked against the panel. A window naming times the panel does not carry silently aggregated the overlap alone while keeping its full label, and a window reaching past the treatment date silently aggregated values the treatment had already moved. Both now warn, naming the offending times. A window with no overlap at all keeps the existing hard error. scm_design() gained the same non-finite predictor guard scm_fit() already had – such a window previously surfaced as “no feasible treatment set found”, which named neither the predictor nor the window.

coresynth 0.4.1

CRAN release: 2026-07-28

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.

New features

• scm_fit(qp_solver = "wolfe") selects a Caratheodory-sparse inner solver. X0 w traces the convex hull of the donor columns in the k-dimensional predictor space as w ranges over the simplex, so the inner QP is a projection onto a polytope in R^k, and by Caratheodory’s theorem an optimum supported on at most k + 1 donors always exists. The Wolfe

  1976. min-norm-point method returns such a solution: it grows a small affinely independent corral one donor at a time instead of shedding coordinates from the full pool, terminates finitely, and is KKT-verified, so it is an exact solver and not an approximation.

  Use it when the predictor specification has fewer predictors than donors in the support. There the inner QP’s optimal set is a face rather than a point, the default active-set solver breaks the tie on round-off (so its weights are not stable across arithmetic), and the weights it returns can spread over most of the donor pool – 221 of 240 donors in one measured case – which is neither interpretable nor reproducible. Where the QP is not degenerate the two solvers agree: on Proposition 99 an outcomes-only fit returns the same six donors and the same pre-treatment loss to 1e-15 under either.

  Opt-in for now, since it changes reported weights for degenerate specs; it is intended to become the default in a future major release. Not available with v_selection = "oos", lambda_pen, v_optim = "bfgs", or staggered adoption, which solve their own QP outside the sharp inner solver; those combinations error rather than silently ignoring the argument. mspe_ratio_pval() mirrors the fit’s choice so the permutation test stays symmetric.

Bug fixes

• The inner SCM QP now returns a KKT-exact optimum. scm_inner_weights_cpp() – which supplies the reported weights for v_selection = "oos", v_optim = "bfgs", loo_donors(), conformal_inference(), scm_design(), and the coordinate sweep’s starting candidate – ran accelerated projected gradient to a 1e-6 step tolerance with no optimality gate. On rank-deficient problems that left it visibly short of the optimum: measured KKT residuals ran to 1e-2 relative, meaning the returned weights did not solve the problem they were documented to solve. It now runs the same warm-started active-set solver the main path uses, with the gradient method only as a fallback; residuals across the regression suite dropped from 1e-5..1e-2 to below 1e-11.

  This moves reported weights for specifications whose inner QP is degenerate. Outcomes-only fits improve slightly (Proposition 99 pre-treatment loss 7.220088 -> 7.220081). Specifications where the treated unit’s predictor vector lies strictly inside the donor hull – so the predictor distance is zero for a continuum of weights, e.g. two or three aggregated predictor windows over a large donor pool – can move substantially, because which zero-distance solution is returned is arbitrary and the outcome path attached to it is arbitrary with it. That is a property of such specifications, not of the solver; qp_solver = "wolfe" above makes it visible and stable.

Performance

• Predictor-based SCM and its in-space placebo test are 100-10,000x faster on large donor pools. The inner simplex QP no longer forms the N_co x N_co Gram Q = X0' V X0: it carries the k x N_co factor B = diag(sqrt(V)) X0 instead. Q has rank at most k, so with few predictors and many donors it is maximally rank deficient – the regime where forming it costs the most and says the least. The active-set face solve now runs in the null space of the sum constraint inside the factor, at O(k^2 m) instead of O(m^3) (same minimiser, same rank truncation: pinv(B_A P) = Z pinv(B_A Z) for any orthonormal basis Z of {z : 1'z = 0}), the dual-feasibility gradient costs O(k N_co) instead of O(N_co^2), and the Lipschitz bound comes from the k x k Gram instead of an N_co x N_co eigendecomposition. The NNLS rescue no longer needs an eigendecomposition at all, since the factor is the least-squares form it wants. At N_co = 240, T_pre = 20: a single fit with one predictor drops from 3.78 s to under 0.01 s and its 240-donor placebo test from 315 s to 0.03 s; with three predictors, from 5.79 s to 0.05 s and 463 s to 2.1 s. Outcomes-only placebo runs improve more modestly (1.9 s to 1.1 s), as that path was never the rank-deficient one.

• v_optim = "multistart" and the coordinate sweep short-circuit when there is a single predictor. With k = 1 the normalised V is the single point V = 1: every start in the multi-start set is the same point and every grid candidate rescales the inner QP by a positive constant, leaving its argmin unchanged. The search is therefore vacuous and now reduces to one exact inner solve, which is also strictly more accurate than the old path’s cold FISTA solution.

• The outcomes-only bordered-KKT face solve now falls back to the exact null-space solve instead of to FISTA. That saddle system is singular once the face exceeds the rank of its Gram, and the previous code then gave up and let the caller fall back to FISTA, which stops at its 1e-6 tolerance – so the outer V search read an inexact objective. It now falls through to the exact routes. This makes v_selection = "oos" measurably better fitting (3.5% lower validation RMSPE on Proposition 99).

  Note on reproducibility: when the inner QP is rank deficient (fewer predictors than donors in the support) its solution set is a face, not a point, and the active set’s entering-variable choice breaks ties on round-off. Weights from predictor-based fits can therefore differ from 0.4.0 while remaining exact optima of the same problem; the pre-treatment fit itself is unchanged to round-off for outcomes-only specs and moves by at most a few times 1e-5 for in-sample predictor specs under the default v_optim. Outcomes-only fits, staggered SCM, SDID, GSC, MC, TASC, SI, scm_design(), and conformal_inference() are bit-identical.

• 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 V 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. 2024, 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,
  20000. 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.