{ "_id": "6acb6abdfe5cc25786e8fb20", "Package": "mhpfilter", "Type": "Package", "Title": "Modified Hodrick-Prescott Filter with Optimal Smoothing\nParameter Selection", "Version": "0.1.0", "Authors@R": "c(\nperson(\"Muhammad\", \"Yaseen\",\nemail = \"myaseen208@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-5923-1714\")),\nperson(\"Javed\", \"Iqbal\",\nemail = \"Javed.iqbal6@sbp.org.pk\",\nrole = \"ctb\",\ncomment = \"Original methodology author\"),\nperson(\"M. Nadim\", \"Hanif\",\nemail = \"Nadeem.hanif@sbp.org.pk\",\nrole = \"ctb\",\ncomment = \"Original methodology author\")\n)", "Maintainer": "Muhammad Yaseen ", "Description": "High-performance implementation of the Modified\nHodrick-Prescott (HP) Filter for decomposing macroeconomic time\nseries into trend and cyclical components. Based on the\nmethodology of Choudhary, Hanif and Iqbal (2014)\n \"On smoothing macroeconomic\ntime series using the modified HP filter\", which uses\ngeneralized cross-validation (GCV) to automatically select the\noptimal smoothing parameter lambda, following McDermott (1997)\n\"An automatic method for choosing the smoothing parameter in\nthe HP filter\" (as described in Coe and McDermott (1997)\n). Unlike the standard HP filter that uses\nfixed lambda values (1600 for quarterly, 100 for annual data),\nthis package estimates series-specific lambda values that\nminimize the GCV criterion. Implements efficient C++ routines\nvia 'RcppArmadillo' for fast computation, supports batch\nprocessing of multiple series, and provides comprehensive\nvisualization tools using 'ggplot2'. 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