{ "_id": "6a13f903acfb0bcc41d348b0", "Package": "BINCOR", "Type": "Package", "Title": "Estimate the Correlation Between Two Irregular Time Series", "Version": "0.2.1", "Authors@R": "c(person(given = \"Josué M.\", family = \"Polanco-Martínez\", role = c(\"aut\", \"cph\", \"cre\"), email = \"josue.m.polanco@gmail.com\", comment = c(ORCID = \"0000-0001-7164-0185\")), person(given = \"Mikko\", family = \"Korpela\", role = c(\"ctb\", \"trl\"), email = \"mvkorpel@iki.fi\"), person(given = \"Manfred\", family = \"Mudelsee\", role = c(\"ctb\"), email = \"mudelsee@climate-risk-analysis.com\", comment = c(ORCID = \"0000-0002-2364-9561\")))", "Copyright": "Authors and file inst/COPYRIGHTS", "Maintainer": "Josué M. Polanco-Martínez ", "Description": "Estimate the correlation between two irregular time series\nthat are not necessarily sampled on identical time points. This\nprogram is also applicable to the situation of two evenly\nspaced time series that are not on the same time grid. 'BINCOR'\nis based on a novel estimation approach proposed by Mudelsee\n(2010, 2014) to estimate the correlation between two climate\ntime series with different timescales. The idea is that\nautocorrelation (AR1 process) allows to correlate values\nobtained on different time points. 'BINCOR' contains four\nfunctions: bin_cor() (the main function to build the binned\ntime series), plot_ts() (to plot and compare the irregular and\nbinned time series, cor_ts() (to estimate the correlation\nbetween the binned time series) and ccf_ts() (to estimate the\ncross-correlation between the binned time series). A\ndescription of the method and package is provided in\nPolanco-Martínez et al. (2019), .", "License": "GPL (>= 2)", "Encoding": "UTF-8", "LazyData": "true", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-25 07:21:35 UTC", "User": "root" }, "Author": "Josué M. 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