{ "_id": "6ad9e34fdb07788da70aa81d", "Package": "LatencyA", "Type": "Package", "Title": "Latency Analysis in Epidemiological Studies Using Weighted\nCumulative Exposures", "Version": "1.0.2", "Authors@R": "c(person(given = \"Fuyu\",family = \"Guo\",email = \"fuyuguo@hsph.harvard.edu\", role = c(\"aut\", \"cre\")),\nperson(given = \"Zihan\", family = \"Qian\", email = \"zqian2@mgh.harvard.edu\", role = \"aut\"),\nperson(given = \"Molin\", family = \"Wang\", email = \"stmow@channing.harvard.edu\", role = \"aut\"))", "Maintainer": "Fuyu Guo ", "Description": "Implements weighted cumulative exposure Cox proportional\nhazards models for latency analysis with repeatedly measured,\ntime-varying exposures. Instead of entering every lagged\nexposure as a separate and highly correlated covariate, the\nlag-specific log hazard ratios are constrained to follow a\nsmooth function of the time since exposure, parameterized by\nnatural cubic splines or polynomials, so that the susceptible\nwindow of an exposure can be identified from the estimated\nlag-risk curve. Functions are provided to fit models with a\nprespecified number of knots or polynomial degree, to select\nthe number of knots by a grid search or the spline terms by a\nstepwise search using the Akaike or Bayesian information\ncriterion, and to extract lag-specific log hazard ratios with\ntheir variances. For the data-driven model selection\nprocedures, inference is based on a cluster bootstrap that\nresamples subjects, which can be run sequentially or in\nparallel. The methods are described in Guo, Qian, Wang, Wang,\nRosner and Wang (2026) \"Taking Into Account the Susceptible\nWindow for Exposures in Cox Proportional Hazards Model: A Way\nUsing Splines to Model the Lag-Risk Relationship\"\n.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "LazyData": "true", "Config/roxygen2/version": "8.0.0", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-22 10:14:33 UTC", "User": "root" }, "Author": "Fuyu Guo [aut, cre], Zihan Qian [aut], Molin Wang [aut]", "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev", "Repository": "https://fyguo.r-universe.dev", "Date/Publication": "2026-10-21 14:10:02 UTC", "RemoteUrl": "https://github.com/cran/LatencyA", "RemoteRef": "HEAD", "RemoteSha": "814f6605f43c2f66d9042ce2fd8201c0c24ff292", "_user": "fyguo", "_type": "src", "_file": "LatencyA_1.0.2.tar.gz", "_fileid": "https://r2.ropensci.org/732ed0311322b9e0372a050159d2c6c77fc6336175fbc93b15dcea9c13ed68aa", "_filesize": 1730821, "_sha256": 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This is GreadSearching by knots", "topics": [ "CoxKnotsearch" ] }, { "page": "CoxKnotsearch_boot", "title": "This function conduct bootstraps for @CoxKnotsearch", "topics": [ "CoxKnotsearch_boot" ] }, { "page": "CoxNCSpline", "title": "This function implments natural cubic splines in Cox regression for latency analysis", "topics": [ "CoxNCSpline" ] }, { "page": "CoxPoly", "title": "This is a function to implement polynomial Cox regression for latency analysis", "topics": [ "CoxPoly" ] }, { "page": "CoxTermsearch", "title": "This function implments Stepwise model selection by terms of natural cubic splines in Cox regression for latency analysis This is GreadSearching by knots", "topics": [ "CoxTermsearch" ] }, { "page": "CoxTermsearch_boot", "title": "This function conduct bootstraps for @CoxTermsearch", "topics": [ "CoxTermsearch_boot" ] }, { "page": "extract_CoxKnotsearch", "title": "This is a function to extract log HR based on the resuls from CoxKnotsearch", "topics": [ 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