{ "_id": "6a1d371c1d7bb097a0a3d6a5", "Package": "fdapace", "Type": "Package", "Title": "Functional Data Analysis and Empirical Dynamics", "URL": "https://github.com/functionaldata/tPACE", "BugReports": "https://github.com/functionaldata/tPACE/issues", "Version": "0.6.0", "Encoding": "UTF-8", "Date": "2024-07-02", "Language": "en-US", "Authors@R": "c(person(\"Yidong\", \"Zhou\", email=\"ydzhou@ucdavis.edu\", role=c(\"cre\",\"aut\"), comment=c(ORCID=\"0000-0003-1423-1857\")),\nperson(\"Han\", \"Chen\", role=c(\"aut\")),\nperson(\"Su I\", \"Iao\", role=c(\"aut\")),\nperson(\"Poorbita\", \"Kundu\", role=c(\"aut\")),\nperson(\"Hang\", \"Zhou\", role=c(\"aut\")),\nperson(\"Satarupa\",\"Bhattacharjee\", role=c(\"aut\")),\nperson(\"Cody\",\"Carroll\", role=c(\"aut\"), comment=c(ORCID=\"0000-0003-3525-8653\")),\nperson(\"Yaqing\",\"Chen\", role=c(\"aut\")),\nperson(\"Xiongtao\",\"Dai\", role=c(\"aut\")),\nperson(\"Jianing\",\"Fan\", role=c(\"aut\")),\nperson(\"Alvaro\",\"Gajardo\", role=c(\"aut\")),\nperson(\"Pantelis Z.\",\"Hadjipantelis\", role=c(\"aut\")),\nperson(\"Kyunghee\",\"Han\", role=\"aut\"),\nperson(\"Hao\",\"Ji\", role=c(\"aut\")),\nperson(\"Changbo\",\"Zhu\", role=c(\"aut\")),\nperson(\"Paromita\", \"Dubey\", role=\"ctb\"),\nperson(\"Shu-Chin\", \"Lin\", role=\"ctb\"),\nperson(\"Hans-Georg\", \"Müller\", role=c(\"cph\",\"ths\",\"aut\")),\nperson(\"Jane-Ling\", \"Wang\", role=c(\"cph\",\"ths\",\"aut\")))", "Maintainer": "Yidong Zhou ", "Description": "A versatile package that provides implementation of\nvarious methods of Functional Data Analysis (FDA) and Empirical\nDynamics. The core of this package is Functional Principal\nComponent Analysis (FPCA), a key technique for functional data\nanalysis, for sparsely or densely sampled random trajectories\nand time courses, via the Principal Analysis by Conditional\nEstimation (PACE) algorithm. This core algorithm yields\ncovariance and mean functions, eigenfunctions and principal\ncomponent (scores), for both functional data and derivatives,\nfor both dense (functional) and sparse (longitudinal) sampling\ndesigns. For sparse designs, it provides fitted continuous\ntrajectories with confidence bands, even for subjects with very\nfew longitudinal observations. PACE is a viable and flexible\nalternative to random effects modeling of longitudinal data.\nThere is also a Matlab version (PACE) that contains some\nmethods not available on fdapace and vice versa. Updates to\nfdapace were supported by grants from NIH Echo and NSF\nDMS-1712864 and DMS-2014626. Please cite our package if you use\nit (You may run the command citation(\"fdapace\") to get the\ncitation format and bibtex entry). References: Wang, J.L.,\nChiou, J., Müller, H.G. (2016)\n; Chen, K.,\nZhang, X., Petersen, A., Müller, H.G. (2017)\n.", "License": "BSD_3_clause + file LICENSE", "LazyData": "false", "NeedsCompilation": "yes", "RoxygenNote": "7.3.1", "VignetteBuilder": "knitr", "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev", "Repository": "https://functionaldata.r-universe.dev", "Date/Publication": "2024-07-03 05:37:09 UTC", "RemoteUrl": "https://github.com/functionaldata/tpace", "RemoteRef": "HEAD", "RemoteSha": "e778562f5f253ae4146d6cfae5df35514d596a75", "Packaged": { "Date": "2026-06-01 07:15:57 UTC", "User": "root" }, "Author": "Yidong Zhou [cre, aut] (ORCID: ),\nHan Chen [aut],\nSu I Iao [aut],\nPoorbita Kundu [aut],\nHang Zhou [aut],\nSatarupa Bhattacharjee [aut],\nCody Carroll [aut] (ORCID: ),\nYaqing Chen [aut],\nXiongtao Dai [aut],\nJianing Fan [aut],\nAlvaro Gajardo [aut],\nPantelis Z. Hadjipantelis [aut],\nKyunghee Han [aut],\nHao Ji [aut],\nChangbo Zhu [aut],\nParomita Dubey [ctb],\nShu-Chin Lin [ctb],\nHans-Georg Müller [cph, ths, aut],\nJane-Ling Wang [cph, ths, aut]", "MD5sum": "9eda8d5a35e9d5182f4719b3a4e19890", "_user": "functionaldata", "_type": "src", "_file": "fdapace_0.6.0.tar.gz", "_fileid": "3845f28bf4f08b913fe6adef01318c2ccd29ac11330c1376229dfdba1dbca6f2", "_filesize": 2166538, "_sha256": "3845f28bf4f08b913fe6adef01318c2ccd29ac11330c1376229dfdba1dbca6f2", "_created": "2026-06-01T07:15:57.000Z", "_published": "2026-06-01T07:39:08.201Z", "_distro": "noble", "_jobs": [ { "job": 78804168274, "time": 291, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "7325569375" }, { "job": 78804168335, "time": 299, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7325571526" }, { "job": 78804168312, "time": 259, "config": "linux-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7325561162" }, { "job": 78804168244, 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"CreateBWPlot", "title": "Functional Principal Component Analysis Bandwidth Diagnostics plot", "topics": [ "CreateBWPlot" ] }, { "page": "CreateCovPlot", "title": "Creates a correlation surface plot based on the results from FPCA() or FPCder().", "topics": [ "CreateCovPlot" ] }, { "page": "CreateDesignPlot", "title": "Create design plots for functional data. See Yao, F., Müller, H.G., Wang, J.L. (2005). Functional data analysis for sparse longitudinal data. J. American Statistical Association 100, 577-590 for interpretation and usage of these plots. 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"CreateScreePlot" ] }, { "page": "CreateStringingPlot", "title": "Create plots for observed and stringed high dimensional data", "topics": [ "CreateStringingPlot" ] }, { "page": "cumtrapzRcpp", "title": "Cumulative Trapezoid Rule Numerical Integration", "topics": [ "cumtrapzRcpp" ] }, { "page": "Dyn_test", "title": "Bootstrap test of Dynamic Correlation", "topics": [ "Dyn_test" ] }, { "page": "DynCorr", "title": "Dynamical Correlation", "topics": [ "DynCorr" ] }, { "page": "FAM", "title": "Functional Additive Models", "topics": [ "FAM" ] }, { "page": "FCCor", "title": "Calculation of functional correlation between two simultaneously observed processes.", "topics": [ "FCCor" ] }, { "page": "FClust", "title": "Functional clustering and identifying substructures of longitudinal data", "topics": [ "FClust" ] }, { "page": "FCReg", "title": "Functional Concurrent Regression using 2D smoothing", "topics": [ "FCReg" ] }, { "page": "fdapace", "title": "fdapace: Functional Data Analysis and 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