{ "_id": "6a1033e4acfb0bcc41c98b92", "Package": "jmotif", "Version": "1.2.1", "Encoding": "UTF-8", "Title": "Time Series Analysis Toolkit Based on Symbolic Aggregate\nDiscretization, i.e. SAX", "Description": "Implements time series z-normalization, SAX, HOT-SAX, VSM,\nSAX-VSM, RePair, and RRA algorithms facilitating time series\nmotif (i.e., recurrent pattern), discord (i.e., anomaly), and\ncharacteristic pattern discovery along with interpretable time\nseries classification.", "Authors@R": "person(\"Pavel\", \"Senin\", , \"seninp@gmail.com\", c(\"aut\", \"cre\"))", "URL": "https://github.com/jMotif/jmotif-R", "BugReports": "https://github.com/jMotif/jmotif-R/issues", "LazyData": "true", "License": "GPL-2", "RoxygenNote": "7.3.3", "Repository": "https://jmotif.r-universe.dev", "Date/Publication": "2025-12-22 10:50:33 UTC", "RemoteUrl": "https://github.com/jmotif/jmotif-r", "RemoteRef": "HEAD", "RemoteSha": "a6bd5ddaff9da2ceaf105abffba51e43aa3fb18a", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-12 07:59:59 UTC", "User": "root" }, "Author": "Pavel Senin [aut, cre]", "Maintainer": "Pavel Senin ", "MD5sum": "13068bec6fcf53873f7fab480915508e", "_user": "jmotif", "_type": "src", "_file": "jmotif_1.2.1.tar.gz", "_fileid": "681403db609dd4c264325ee5908841ae8a6c3ec96995251448afacc6a1b76333", "_filesize": 2248542, "_sha256": "681403db609dd4c264325ee5908841ae8a6c3ec96995251448afacc6a1b76333", "_created": "2026-05-12T07:59:59.000Z", "_published": "2026-05-22T10:45:56.230Z", "_distro": "noble", "_jobs": [ { "job": 77364199082, "time": 162, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "6938785407" }, { "job": 77364198979, "time": 184, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "6938794066" }, { "job": 77364199178, "time": 147, "config": "linux-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "6938781792" }, { "job": 77364199183, "time": 142, "config": "linux-release-x86_64", "r": "4.6.0", 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3 } ], "_tags": [], "_topics": [ "anomalydiscovery", "discord", "discretization", "kdd", "sax", "sax-vsm", "timeseries", "cpp" ], "_stars": 57, "_contributors": [ { "user": "seninp", "count": 512, "uuid": 1729978 } ], "_userbio": { "uuid": 8937265, "type": "organization", "name": "jMotif", "description": "SAX discretization-based stack for time series analysis" }, "_downloads": { "count": 548, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/jmotif" }, "_devurl": "https://github.com/jmotif/jmotif-r", "_searchresults": 47, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/jmotif.html", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_homeurl": "https://github.com/jmotif/jmotif-r", "_realowner": "jmotif", "_cranurl": true, "_releases": [ { "version": "1.0.1", "date": "2015-10-19" }, { "version": "1.0.2", "date": "2015-12-08" }, { "version": "1.0.2.900", "date": 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"A PHYSIONET dataset", "object": "ECG0606", "class": [ "numeric" ], "fields": [], "table": false, "tojson": true }, { "name": "Gun_Point", "title": "A standard UCR Gun Point dataset from http://www.cs.ucr.edu/~eamonn/time_series_data", "object": "Gun_Point", "class": [ "list" ], "fields": [], "table": false, "tojson": true } ], "_help": [ { "page": "alphabet_to_cuts", "title": "Translates an alphabet size into the array of corresponding SAX cut-lines built using the Normal distribution.", "topics": [ "alphabet_to_cuts" ] }, { "page": "bags_to_tfidf", "title": "Computes a TF-IDF weight vectors for a set of word bags.", "topics": [ "bags_to_tfidf" ] }, { "page": "CBF", "title": "A standard UCR Cylinder-Bell-Funnel dataset from http://www.cs.ucr.edu/~eamonn/time_series_data", "topics": [ "CBF" ] }, { "page": "cosine_dist", "title": "Computes the cosine similarity between numeric vectors", "topics": [ "cosine_dist" ] }, { "page": "cosine_sim", "title": "Computes the cosine distance value between a bag of words and a set of TF-IDF weight vectors.", "topics": [ "cosine_sim" ] }, { "page": "early_abandoned_dist", "title": "Finds the Euclidean distance between points, if distance is above the threshold, abandons the computation and returns NAN.", "topics": [ "early_abandoned_dist" ] }, { "page": "ecg0606", "title": "A PHYSIONET dataset", "topics": [ "ecg0606" ] }, { "page": "euclidean_dist", "title": "Finds the Euclidean distance between points.", "topics": [ "euclidean_dist" ] }, { "page": "find_discords_brute_force", "title": "Finds a discord using brute force algorithm.", "topics": [ "find_discords_brute_force" ] }, { "page": "find_discords_hotsax", "title": "Finds a discord (i.e. time series anomaly) with HOT-SAX. Usually works the best with lower sizes of discretization parameters: PAA and Alphabet.", "topics": [ "find_discords_hotsax" ] }, { "page": "find_discords_rra", "title": "Finds a discord with RRA (Rare Rule Anomaly) algorithm. Usually works the best with higher than that for HOT-SAX sizes of discretization parameters (i.e., PAA and Alphabet sizes).", "topics": [ "find_discords_rra" ] }, { "page": "Gun_Point", "title": "A standard UCR Gun Point dataset from http://www.cs.ucr.edu/~eamonn/time_series_data", "topics": [ "Gun_Point" ] }, { "page": "idx_to_letter", "title": "Get the ASCII letter by an index.", "topics": [ "idx_to_letter" ] }, { "page": "is_equal_mindist", "title": "Compares two strings using mindist.", "topics": [ "is_equal_mindist" ] }, { "page": "is_equal_str", "title": "Compares two strings using natural letter ordering.", "topics": [ "is_equal_str" ] }, { "page": "letter_to_idx", "title": "Get the index for an ASCII letter.", "topics": [ "letter_to_idx" ] }, { "page": "letters_to_idx", "title": "Get an ASCII indexes sequence for a given character array.", "topics": [ "letters_to_idx" ] }, { "page": "manyseries_to_wordbag", "title": "Converts a set of time-series into a single bag of words.", "topics": [ "manyseries_to_wordbag" ] }, { "page": "min_dist", "title": "Computes the mindist value for two strings", "topics": [ "min_dist" ] }, { "page": "paa", "title": "Computes a Piecewise Aggregate Approximation (PAA) for a time series.", "topics": [ "paa" ] }, { "page": "sax_by_chunking", "title": "Discretize a time series with SAX using chunking (no sliding window).", "topics": [ "sax_by_chunking" ] }, { "page": "sax_distance_matrix", "title": "Generates a SAX MinDist distance matrix (i.e. the \"lookup table\") for a given alphabet size.", "topics": [ "sax_distance_matrix" ] }, { "page": "sax_via_window", "title": "Discretizes a time series with SAX via sliding window.", "topics": [ "sax_via_window" ] }, { "page": "series_to_chars", "title": "Transforms a time series into the char array using SAX and the normal alphabet.", "topics": [ "series_to_chars" ] }, { "page": "series_to_string", "title": "Transforms a time series into the string.", "topics": [ "series_to_string" ] }, { 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