Changes in version 0.1.0                        

First release of sdid, which builds on synthdid 0.0.9 by Arkhangelsky,
Athey, Hirshberg, Imbens, and Wager. BSD 3-Clause License.

Interface

  - sdid() estimates the ATT, following the Stata command sdid. It takes
    a long format panel, detects staggered adoption, and fits SDiD, SC,
    or DiD via method ("sdid", "sc", "did").
  - Inference must be chosen with vce: "bootstrap", "jackknife",
    "placebo", or "noinference". reps defaults to 50. seed makes
    replications reproducible without changing the session's RNG state.
  - sdid() prints a header, replication progress, and the ATT with its z
    statistic, p-value, and confidence interval at level. graph = FALSE
    skips the plot; quietly = TRUE prints nothing.
  - alternative sets a two-sided (default) or one-sided ("greater",
    "less") test. One-sided tests give one-sided p-values and intervals,
    labeled P>z or P<z.
  - Results have print, summary, coef, vcov, confint, nobs, weights, and
    plot methods, documented in ?sdid and ?sdid-methods.
  - tidy() and glance() methods work with modelsummary and broom.
    tidy(cohorts = TRUE) adds a row per adoption period, and labels
    names the rows.
  - Two vignettes, getting started and plotting, are rewritten around
    sdid().

Staggered adoption

  - Cohort effects are averaged with weights proportional to treated
    units times post-treatment periods (Clarke et al. 2024,
    Algorithm 1).
  - Bootstrap, jackknife, and placebo resample the whole panel jointly
    (Algorithms 2–4) and report cohort and overall standard errors.
  - Each cohort's z statistic, p-value, and interval are printed and
    stored in cohorts.
  - Reproduces the cohort estimates, overall effects, and jackknife SE
    of Clarke et al. (2024) on their gender quota data.

Covariates

  - covariates takes time-varying covariates. covariate.method is
    "optimized" (default; coefficients estimated jointly with the
    weights) or "projected" (two-way fixed effects regression on
    untreated observations; Kranz 2022).
  - projection.sample sets the projected regression's sample:
    never-treated units (default, as in Stata) or all untreated
    observations.
  - The optimized method standardizes covariates by default (standardize
    = TRUE), warns on reaching max.iter (class sdid_max_iter_warning),
    and errors if coefficients diverge.
  - beta is named by covariate. With the optimized method and staggered
    adoption, it is a covariate × cohort matrix.
  - Standard errors re-estimate the coefficients in every replication.

Plots

  - sdid_plot(), sdid_units_plot(), sdid_placebo(), and
    sdid_placebo_plot() replace their synthdid_* versions and take
    sdid() results. Staggered designs get one panel per cohort.
  - sdid_plot() styles: "minimal" (default) and "effects", which adds a
    simplified DiD diagram.
  - Axes get about five ticks covering the data. Whole-number time axes
    get whole-number ticks only. x.breaks and y.breaks set ticks
    manually.
  - sdid_plot() and sdid_units_plot() accept a named list of
    single-cohort results, one panel each. facet and facet.vertical are
    removed; lambda.comparable defaults to FALSE.
  - Stacked panels have open frames, their own time-weight axes, and
    cohort labels at the adoption period. A red dashed line marks
    treatment.
  - sdid_units_plot() is redesigned: type = "scatter" (default) plots
    each control's weight against its own estimate; type = "bars" shows
    weighted controls ordered by weight. New arguments: label.units,
    cohort, weighted.only. Uses ggrepel when installed.
    negligible.threshold, negligible.alpha, and units are removed.
  - sdid_placebo() and sdid_placebo_plot() gain placebo.time to choose
    when the in-time placebo starts. Single-adoption designs only.
  - Plotting no longer attaches ggplot2 and requires ggplot2 >= 3.4.0.

Performance

  - Unit and time weights are estimated in C++. Bootstrap, jackknife,
    and placebo replications run in parallel, with identical results for
    any thread count under the same seed. Set threads with
    sdid.num.threads or num.threads.
  - Long weight solves switch to a Gram matrix gradient, much faster on
    large panels. Controlled by sdid.solver and sdid.gram.max.mb.
  - Replications can be interrupted with Ctrl-C.

Bug fixes relative to synthdid 0.0.9

  - Tibbles and data.tables are accepted. They previously failed with
    "There is no variation in treatment status".
  - Fixed a line-search edge case that could produce NaN weights.
  - The in-time placebo for SC no longer fails with a duplicated
    omega.intercept argument.
  - Replications use the fitted estimator's sparsify settings, not the
    defaults.
  - A treatment column stored as doubles no longer warns.
  - The PENN documentation gives the correct number of rows.

Removed

  - synthdid_estimate(), sc_estimate(), did_estimate(),
    panel.matrices(), synthdid_se(), synthdid_effect_curve(), and
    timesteps() are no longer exported.
  - synthdid_rmse_plot(), synthdid_controls(), simulate_dgp(),
    estimate_dgp(), and lindsey_density_estimate() are removed.
  - Underlying estimates (in estimates and from sdid_placebo()) have
    class sdid_estimate and print on one line without computing an SE.
    Their summary() and format() methods are removed, and the methods
    dependency is dropped.
  - The original plot style, standard errors in plots, and several
    estimates in one shared panel are removed.
  - Options are renamed with the sdid. prefix. The verbosity option is
    removed; use quietly.