{ "_id": "6a1068c9acfb0bcc41ca974b", "Package": "itsadug", "Version": "2.5", "Date": "2025-12-22", "Title": "Interpreting Time Series and Autocorrelated Data Using GAMMs", "Authors@R": "c( person(\"Jacolien\", \"van Rij\", email=\"j.c.van.rij@rug.nl\", role = c(\"aut\", \"cre\")),\nperson(\"Martijn\",\"Wieling\", role = \"aut\"),\nperson(\"R. Harald\",\"Baayen\", role = \"aut\"),\nperson(\"Hedderik\", \"van Rijn\", role = \"ctb\"))", "Author": "Jacolien van Rij [aut, cre], Martijn Wieling [aut], R. Harald\nBaayen [aut], Hedderik van Rijn [ctb]", "Maintainer": "Jacolien van Rij ", "Description": "GAMM (Generalized Additive Mixed Modeling; Lin & Zhang,\n1999) as implemented in the R package 'mgcv' (Wood, S.N., 2006;\n2011) is a nonlinear regression analysis which is particularly\nuseful for time course data such as EEG, pupil dilation, gaze\ndata (eye tracking), and articulography recordings, but also\nfor behavioral data such as reaction times and response data.\nAs time course measures are sensitive to autocorrelation\nproblems, GAMMs implements methods to reduce the\nautocorrelation problems. This package includes functions for\nthe evaluation of GAMM models (e.g., model comparisons,\ndetermining regions of significance, inspection of\nautocorrelational structure in residuals) and interpreting of\nGAMMs (e.g., visualization of complex interactions, and\ncontrasts).", "License": "GPL (>= 2)", "LazyData": "true", "VignetteBuilder": "knitr", "RoxygenNote": "7.3.3", "Encoding": "UTF-8", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-08 04:59:36 UTC", "User": "root" }, "Repository": "https://jacolien.r-universe.dev", "Date/Publication": "2026-01-08 02:29:57 UTC", "RemoteUrl": "https://github.com/cran/itsadug", "RemoteRef": "HEAD", "RemoteSha": "6f9574d4422f9072450d77114949639502618d37", "MD5sum": "830ef7923c1475b8cccbd3cb70d04576", "_user": "jacolien", "_type": "src", "_file": "itsadug_2.5.tar.gz", "_fileid": "49faf7f20c4ff8a457efe99b7ff9bbc87ab8e2f79d35cc5488c1c3d4b8d440a7", "_filesize": 3849955, "_sha256": 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factor.", "concept": [ "Utility functions" ], "topics": [ "refLevels" ] }, { "page": "report_stats", "title": "Returns a description of the statistics of the smooth terms for reporting.", "concept": [ "Testing for significance" ], "topics": [ "report_stats" ] }, { "page": "res_df", "title": "Retrieve the residual degrees of freedom from the model.", "concept": [ "Utility functions" ], "topics": [ "res_df" ] }, { "page": "resid_gam", "title": "Extract model residuals and remove the autocorrelation accounted for.", "concept": [ "functions for model criticism" ], "topics": [ "resid.gam", "resid_gam" ] }, { "page": "rug_model", "title": "Add rug to plot, based on model.", "concept": [ "Functions for plotting" ], "topics": [ "rug_model" ] }, { "page": "simdat", "title": "Simulated time series data.", "topics": [ "simdat" ] }, { "page": "start_event", "title": "Determine the starting point for each time series.", "concept": [ "functions for model criticism" ], "topics": [ "start_event" ] }, { "page": "start_value_rho", "title": "Extract the Lag 1 value from the ACF of the residuals of a gam, bam, lm, lmer model, ...", "concept": [ "functions for model criticism" ], "topics": [ "start_value_rho" ] }, { "page": "summary_data", "title": "Print a descriptive summary of a data frame.", "concept": [ "Utility functions" ], "topics": [ "summary_data" ] }, { "page": "timeBins", "title": "Label timestamps as timebins of a given binsize.", "concept": [ "Utility functions" ], "topics": [ "timeBins" ] }, { "page": "wald_gam", "title": "Function for post-hoc comparison of the contrasts in a single GAMM model.", "concept": [ "Testing for significance" ], "topics": [ "wald_gam" ] } ], "_rundeps": [ "lattice", "Matrix", "mgcv", "nlme", "plotfunctions" ], "_vignettes": [ { "source": "acf.Rmd", "filename": "acf.html", "title": "ACF: checking & handling autocorrelation", "author": "Jacolien van Rij", "engine": "knitr::rmarkdown", "headings": [ "How to include an AR1 model", "Example", "Mark 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