{"id":13665766,"url":"https://github.com/nppackages/scpi","last_synced_at":"2025-04-06T06:09:04.724Z","repository":{"id":56810910,"uuid":"390752624","full_name":"nppackages/scpi","owner":"nppackages","description":"Prediction and inference procedures for synthetic control methods with multiple treated units and staggered adoption.","archived":false,"fork":false,"pushed_at":"2025-02-01T16:38:39.000Z","size":12807,"stargazers_count":32,"open_issues_count":0,"forks_count":10,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-30T05:05:14.021Z","etag":null,"topics":["prediction-intervals","python","r","stata","synthetic-control"],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":false,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/nppackages.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-07-29T14:33:43.000Z","updated_at":"2025-02-04T20:44:05.000Z","dependencies_parsed_at":"2023-01-30T19:31:02.293Z","dependency_job_id":"25216b96-3262-4f2e-ad68-9fc838faac97","html_url":"https://github.com/nppackages/scpi","commit_stats":{"total_commits":66,"total_committers":2,"mean_commits":33.0,"dds":"0.030303030303030276","last_synced_commit":"ae291327e9eb7982bf8896b1038b1f254aa5632a"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nppackages%2Fscpi","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nppackages%2Fscpi/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nppackages%2Fscpi/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nppackages%2Fscpi/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nppackages","download_url":"https://codeload.github.com/nppackages/scpi/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247441052,"owners_count":20939239,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["prediction-intervals","python","r","stata","synthetic-control"],"created_at":"2024-08-02T06:00:50.219Z","updated_at":"2025-04-06T06:09:04.707Z","avatar_url":"https://github.com/nppackages.png","language":"R","funding_links":[],"categories":["R","Synthetic Control"],"sub_categories":[],"readme":"# SCPI\n\nThe `scpi` package provides Python, R and Stata implementations of estimation and inference procedures for synthetic control methods.\n\nThis work was supported by the National Science Foundation through grants [SES-1947805](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1947805), [SES-2019432](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2019432), and [SES-2241575](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2241575), and by the National Institutes of Health through grant [R01 GM072611-16](https://reporter.nih.gov/project-details/10093056).\n\n## Queries and Requests\n\nPlease email: [scpi_pkg@googlegroups.com](mailto:scpi_pkg@googlegroups.com)\n\n## Python Implementation\n\nTo install/update in Python type:\n```\npip install scpi_pkg\n```\n\n- Help: [PyPI repository](https://pypi.org/project/scpi_pkg/).\n\n- Replication: [py-script](Python/scpi_illustration.py), [plot illustration](Python/scpi_illustration_plot.py), [data](Python/scpi_germany.csv).\n\n- Illustration Staggered Adoption: [py-script](Python/scpi_illustration-multi.py), [plot illustration](Python/scpi_illustration_plot-multi.py).\n\n## R Implementation\n\nTo install/update in R from CRAN type:\n```\ninstall.packages('scpi')\n````\n\n- Help: [R Manual](https://cran.r-project.org/web/packages/scpi/scpi.pdf), [CRAN repository](https://cran.r-project.org/package=scpi).\n\n- Replication: [R-script](R/scpi_illustration.R), [plot illustration](R/scpi_illustration_plot.R), [data](R/scpi_germany.csv).\n\n- Illustration Staggered Adoption: [R-script](R/scpi_illustration-multi.R), [plot illustration](R/scpi_illustration_plot-multi.R).\n\n## Stata Implementation\n\nThe Stata implementation relies on Python, which needs to be available in the system.\n\n### How to install Python\nThere are at least two ways to install Python:\n1. Download and install Python directly from [https://realpython.com/installing-python/](https://realpython.com/installing-python/).\n2. Download and install Anaconda for [Windows](https://docs.anaconda.com/anaconda/install/windows/), [macOS](https://docs.anaconda.com/anaconda/install/mac-os/), or [Linux](https://docs.anaconda.com/anaconda/install/linux/).\n\nAfter Python is installed, please run the following two commands via the Python command line:\n\n```\npip install luddite\npip install scpi_pkg\n```\n\n### How to link Stata and Python\nStata (16.0 or newer) and Python (\u003e=3.8) can be linked following the [official tutorial](https://blog.stata.com/2020/08/18/stata-python-integration-part-1-setting-up-stata-to-use-python/) on the Stata blog.\n\n### To install/update in Stata type:\n```\nnet install grc1leg, from(\"http://www.stata.com/users/vwiggins/\") replace force\nnet install scpi, from(https://raw.githubusercontent.com/nppackages/scpi/master/stata) replace force\n```\n\n- Help: [scdata](stata/scdata.pdf), [scest](/stata/scest.pdf), [scpi](stata/scpi.pdf), [scplot](stata/scplot.pdf).\n\n- Replication files: [do-file](stata/scpi_illustration.do), [plot illustration](stata/scpi_illustration_plot.do), [data](stata/scpi_germany.dta).\n\n- Illustration Staggered Adoption: [do-file](stata/scpi_illustration-multi.do), [plot illustration](stata/scpi_illustration_plot-multi.do).\n\n\n## References\n\n### Software and Implementation\n\n- Cattaneo, Feng, Palomba and Titiunik (2025): [scpi: Uncertainty Quantification for Synthetic Control Methods](https://nppackages.github.io/references/Cattaneo-Feng-Palomba-Titiunik_2025_JSS.pdf).\u003cbr\u003e\n_Journal of Statistical Software_, forthcoming.\n\n### Technical and Methodological\n\n- Cattaneo, Feng, Palomba and Titiunik (2025): [Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption](https://nppackages.github.io/references/Cattaneo-Feng-Palomba-Titiunik_2025_RESTAT.pdf).\u003cbr\u003e\n_Review of Economics and Statistics_, revise and resubmit.\u003cbr\u003e\n[Supplemental](https://nppackages.github.io/references/Cattaneo-Feng-Palomba-Titiunik_2025_RESTAT--Supplement.pdf)\u003cbr\u003e\n\n- Cattaneo, Feng and Titiunik (2021): [Prediction Intervals for Synthetic Control Methods](https://nppackages.github.io/references/Cattaneo-Feng-Titiunik_2021_JASA.pdf).\u003cbr\u003e\n_Journal of the American Statistical Association_ 116(536): 1865-1880.\u003cbr\u003e\n[Supplemental](https://nppackages.github.io/references/Cattaneo-Feng-Titiunik_2021_JASA--Supplement.pdf)\u003cbr\u003e\n\n\u003cbr\u003e\u003cbr\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnppackages%2Fscpi","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnppackages%2Fscpi","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnppackages%2Fscpi/lists"}