Package index
-
scm_fit() - Fit a Synthetic Control Method Model
-
pred() - Predictor Specification for SCM
-
scm_design() - Experimental Synthetic Control Design
Inference
Permutation, bootstrap, jackknife, parametric, and conformal inference for the supported estimators.
-
conformal_inference() - Conformal Inference for Synthetic Control Estimators
-
mspe_ratio_pval() - Permutation Inference via MSPE Ratio for SCM
-
sdid_inference() - Inference for Synthetic Difference-in-Differences
-
gsc_boot() - Parametric Bootstrap Inference for GSC (Xu 2017 S.3)
-
gsc_inference() - Non-parametric Inference for GSC (Xu 2017)
-
si_inference() - Non-parametric Inference for SI (Agarwal et al. 2025)
Diagnostics & robustness checks
Validation exercises recommended by Abadie, Diamond & Hainmueller (2015).
-
placebo_in_time() - In-Time Placebo (Backdating) Test for SCM
-
loo_donors() - Leave-One-Out Donor Robustness for SCM
-
augment_scm() - Augmented Synthetic Control Method (Ridge ASCM)
-
tidy(<coresynth_inference>) - Tidy an inference result
-
glance(<coresynth_inference>) - Glance at an inference result
-
plot(<coresynth>) - Plot a coresynth model
-
plot(<scm_design>) - Plot an scm_design object
-
export_json() - Export coresynth Results to JSON
-
coresynthcoresynth-package - coresynth: Fast and Unified Synthetic Control Methods
Low-level C++ routines
Exported RcppArmadillo workhorses called internally by the wrappers. Most users do not need these directly.
-
scm_weights_cpp() - SCM Outer Weights (Joint Optimization of W and V)
-
scm_inner_weights_cpp() - SCM Inner Weights (QP Given V)
-
scm_placebo_cpp() - Fast Leave-One-Out Placebo Test for SCM (Abadie et al. 2010)
-
sdid_unit_weights_cpp() - Calculate SDID Unit Weights (omega)
-
sdid_time_weights_cpp() - Calculate SDID Time Weights (lambda)
-
sdid_estimate_cpp() - Calculate SDID Estimate (tau_sdid)
-
sdid_placebo_cpp() - Fast Placebo Test for SDID
-
gsc_ife_cpp() - Fast Interactive Fixed Effects (IFE) for Generalized Synthetic Control
-
si_pcr_cpp() - SI-PCR: Synthetic Interventions via Principal Component Regression
-
tensor_unfold_cpp() - Tensor Unfolding (Matricization) for Synthetic Interventions
-
soft_impute_cpp() - Fast Matrix Completion using Soft-Impute Algorithm
-
kalman_smoother_cpp() - Kalman Filter and RTS Smoother (TASC)