{ "_id": "6a1054dfacfb0bcc41ca22a6", "Package": "sensitivityCalibration", "Type": "Package", "Title": "A Calibrated Sensitivity Analysis for Matched Observational\nStudies", "Version": "0.0.1", "Author": "Bo Zhang", "Maintainer": "Bo Zhang ", "Description": "Implements the calibrated sensitivity analysis approach\nfor matched observational studies. Our sensitivity analysis\nframework views matched sets as drawn from a super-population.\nThe unmeasured confounder is modeled as a random variable. We\ncombine matching and model-based covariate-adjustment methods\nto estimate the treatment effect. The hypothesized unmeasured\nconfounder enters the picture as a missing covariate. We adopt\na state-of-art Expectation Maximization (EM) algorithm to\nhandle this missing covariate problem in generalized linear\nmodels (GLMs). As our method also estimates the effect of each\nobserved covariate on the outcome and treatment assignment, we\nare able to calibrate the unmeasured confounder to observed\ncovariates. Zhang, B., Small, D. S. (2018). .", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "LazyData": "true", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-09 08:17:30 UTC", "User": "root" }, "Repository": "https://cran.r-universe.dev", "Date/Publication": "2018-12-18 10:20:06 UTC", "RemoteUrl": "https://github.com/cran/sensitivityCalibration", "RemoteRef": "HEAD", "RemoteSha": "ddf0339c8b7af97cad748121ecafe25bcae47267", "MD5sum": "f2975587c29f30071eaebbd3a8f4262a", "_user": "cran", "_type": "src", "_file": "sensitivityCalibration_0.0.1.tar.gz", "_fileid": "7509996daf7a0d98ad631205ec4b99c94915e4928b9e515c285faec665e8fb35", "_filesize": 191799, "_sha256": "7509996daf7a0d98ad631205ec4b99c94915e4928b9e515c285faec665e8fb35", "_created": "2026-05-09T08:17:30.000Z", "_published": "2026-05-22T13:06:39.960Z", "_distro": "noble", "_jobs": [ { "job": 77385403363, "time": 162, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "6894269304" }, { "job": 77385403278, "time": 166, "config": 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