{ "_id": "6a1292cbacfb0bcc41d0b6a7", "Package": "edl", "Version": "1.1", "Date": "2021-09-15", "Title": "Toolbox for Error-Driven Learning Simulations with Two-Layer\nNetworks", "Authors@R": "c( person(\"Jacolien\", \"van Rij\", email=\"j.c.van.rij@rug.nl\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0001-7445-5647\")), person(\"Dorothée\", \"Hoppe\", email=\"d.b.hoppe@rug.nl\", role = c(\"aut\")))", "Maintainer": "Jacolien van Rij ", "Description": "Error-driven learning (based on the Widrow & Hoff\n(1960)\nlearning rule, and essentially the same as Rescorla-Wagner's\nlearning equations (Rescorla & Wagner, 1972, ISBN: 0390718017),\nwhich are also at the core of Naive Discrimination Learning,\n(Baayen et al, 2011, ) can be used to\nexplain bottom-up human learning (Hoppe et al,\n), but is also at the core of\nartificial neural networks applications in the form of the\nDelta rule. This package provides a set of functions for\nbuilding small-scale simulations to investigate the dynamics of\nerror-driven learning and it's interaction with the structure\nof the input. For modeling error-driven learning using the\nRescorla-Wagner equations the package 'ndl' (Baayen et al,\n2011, ) is available on CRAN at\n. However, the package\ncurrently only allows tracing of a cue-outcome combination,\nrather than returning the learned networks. To fill this gap,\nwe implemented a new package with a few functions that\nfacilitate inspection of the networks for small error driven\nlearning simulations. Note that our functions are not optimized\nfor training large data sets (no parallel processing), as they\nare intended for small scale simulations and course examples.\n(Consider the python implementation 'pyndl'\n for that purpose.)", "License": "GPL (>= 2)", "LazyData": "true", "VignetteBuilder": "knitr", "RoxygenNote": "7.1.0", "Encoding": "UTF-8", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-24 05:52:44 UTC", "User": "root" }, "Author": "Jacolien van Rij [aut, cre]\n(), Dorothée Hoppe [aut]", "Repository": "https://jacolien.r-universe.dev", "Date/Publication": "2021-09-20 06:40:05 UTC", "RemoteUrl": "https://github.com/cran/edl", "RemoteRef": "HEAD", "RemoteSha": "c40160df056e31bfafda4197405bddf89158d4b4", "MD5sum": "951832b635127c91ea90fa0e081d16df", "_user": "jacolien", "_type": "src", "_file": "edl_1.1.tar.gz", "_fileid": "d07b9c74427a4c967145ae6dd2f2463be90b98261671dcc90c2bc95c4ba60bdd", "_filesize": 701744, "_sha256": 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"getUpdate" ] }, { "page": "getValues", "title": "Retrieve all cues from a vector of text strings.", "topics": [ "getValues" ] }, { "page": "getWeightsByCue", "title": "Extract the change of connection weights between a specific cue and all outcomes.", "topics": [ "getWeightsByCue" ] }, { "page": "getWeightsByOutcome", "title": "Extract the change of connection weights between all cues and a specific outcome.", "topics": [ "getWeightsByOutcome" ] }, { "page": "getWM", "title": "Retrieve all cues from a vector of text strings.", "topics": [ "getWM" ] }, { "page": "luceChoice", "title": "Function implementing the Luce choice rule.", "topics": [ "luceChoice" ] }, { "page": "plotActivations", "title": "Visualize the change of connection weights between a specific outcome and all cues.", "topics": [ "plotActivations" ] }, { "page": "plotCueWeights", "title": "Visualize the change of connection weights between a specific cue and all outcomes.", "topics": [ "plotCueWeights" ] }, { "page": 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