# ------------------------------------------------
# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# ------------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "SCMs" in publications use:'
type: software
license: GPL-3.0-only
title: 'SCMs: Specification Curve Analysis for Synthetic Control Methods'
version: 0.1.1
doi: 10.32614/CRAN.package.SCMs
abstract: 'Estimates synthetic control models while exposing the implementation choices
  that define the synthetic counterfactual: how matching variables are constructed,
  how predictor weights are set, which constraints the donor weights satisfy (simplex,
  lasso, ridge, penalized), whether an intercept is included, and which outcome model
  corrects for bias (OLS, ridge, lasso, augmented synthetic control). Runs specification-curve
  analysis over a grid of these choices (Simonsohn, Simmons and Nelson (2020) <https://doi.org/10.1038/s41562-020-0912-z>),
  with placebo-in-space and bootstrap inference for single specifications and placebo
  inference for the curve as a whole, and uses ''xgboost'' models with SHAP values
  (Lundberg and Lee (2017) <https://doi.org/10.48550/arXiv.1705.07874>) to show which
  choices drive the estimates. Methods follow Abadie, Diamond and Hainmueller (2010)
  <https://doi.org/10.1198/jasa.2009.ap08746>, Abadie (2021) <https://doi.org/10.1257/jel.20191450>,
  Ben-Michael, Feller and Rothstein (2021) <https://doi.org/10.1080/01621459.2021.1929245>
  and Chen and Guestrin (2016) <https://doi.org/10.1145/2939672.2939785>.'
authors:
- family-names: Jelveh
  given-names: Zubin
  email: zjelveh@umd.edu
repository: https://zjelveh.r-universe.dev
repository-code: https://github.com/zjelveh/SCMs
commit: 0b61758f4f907498b8dbe4c9bc4b27c4491f0b3a
url: https://github.com/zjelveh/SCMs
date-released: '2026-10-22'
contact:
- family-names: Jelveh
  given-names: Zubin
  email: zjelveh@umd.edu