Package: ndl 0.2.18

ndl: Naive Discriminative Learning

Naive discriminative learning implements learning and classification models based on the Rescorla-Wagner equations and their equilibrium equations.

Authors:Antti Arppe [aut], Peter Hendrix [aut], Petar Milin [aut], R. Harald Baayen [aut], Tino Sering [aut, cre], Cyrus Shaoul [aut]

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NEWS

# Install 'ndl' in R:
install.packages('ndl', repos = c('https://dernarr.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • danks - Example data from Danks (2003), after Spellman (1996).
  • dative - Dative Alternation
  • lexample - Lexical example data illustrating the Rescorla-Wagner equations
  • numbers - Example data illustrating the Rescorla-Wagner equations as applied to numerical cognition by Ramscar et al. (2011).
  • plurals - Artificial data set used to illustrate the Rescorla-Wagner equations and naive discriminative learning.
  • serbian - Serbian case inflected nouns.
  • serbianLex - Serbian lexicon with 1187 prime-target pairs.
  • serbianUniCyr - Serbian case inflected nouns (in Cyrillic Unicode).
  • serbianUniLat - Serbian case inflected nouns (in Latin-alphabet Unicode).
  • think - Finnish 'think' verbs.

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.00 score 1 stars 66 scripts 202 downloads 3 mentions 14 exports 66 dependencies

Last updated 6 years agofrom:52291ac2f0. Checks:OK: 3 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 06 2024
R-4.5-win-x86_64OKNov 06 2024
R-4.5-linux-x86_64OKNov 06 2024
R-4.4-win-x86_64NOTENov 06 2024
R-4.4-mac-x86_64NOTENov 06 2024
R-4.4-mac-aarch64NOTENov 06 2024
R-4.3-win-x86_64NOTENov 06 2024
R-4.3-mac-x86_64NOTENov 06 2024
R-4.3-mac-aarch64NOTENov 06 2024

Exports:acts2probscrosstableStatisticscueCodingestimateActivationsestimateWeightsestimateWeightsCompactmodelStatisticsndlClassifyndlCrossvalidatendlCuesOutcomesndlStatisticsndlVarimporthoCodingRescorlaWagner

Dependencies:backportsbase64encbslibcachemcheckmatecliclustercolorspacedata.tabledigestevaluatefansifarverfastmapfontawesomeforeignFormulafsggplot2gluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobandjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmennetpillarpkgconfigR6rappdirsRColorBrewerRcpprlangrmarkdownrpartrstudioapisassscalesstringistringrtibbletinytexutf8vctrsviridisviridisLitewithrxfunyaml

Readme and manuals

Help Manual

Help pageTopics
Naive Discriminative Learningndl-package ndl
Calculate probability matrix from activation matrix, as well as predicted valuesacts2probs
Analysis of Model Fit for Naive Discriminatory Reader Modelsanova.ndlClassify anova.ndlClassifylist
Calculate statistics for a contingency tablecrosstableStatistics
code a vector of cues as n-gramscueCoding
Example data from Danks (2003), after Spellman (1996).danks
Dative Alternationdative
Estimation of the activations of outcomes (meanings)estimateActivations
Estimation of the association weights using the equilibrium equations of Danks (2003) for the Rescorla-Wagner equations.estimateWeights
Estimation of the association weights using the equilibrium equations of Danks (2003) for the Rescorla-Wagner equations using a compact binary event file.estimateWeightsCompact
Count cue-outcome co-occurences needed to run the Danks equations.learn
Count cue-outcome co-occurrences needed to run the Danks equations.learnLegacy
Lexical example data illustrating the Rescorla-Wagner equationslexample
Calculate a range of goodness of fit measures for an object fitted with some multivariate statistical method that yields probability estimates for outcomes.modelStatistics
Classification using naive discriminative learning.ndlClassify print.ndlClassify
Crossvalidation of a Naive Discriminative Learning model.ndlCrossvalidate
Creation of dataframe for Naive Discriminative Learning from formula specificationndlCuesOutcomes
Calculate goodness of fit statistics for a naive discriminative learning model.ndlStatistics
Permutation variable importance for classification using naive discriminative learning.ndlVarimp
Example data illustrating the Rescorla-Wagner equations as applied to numerical cognition by Ramscar et al. (2011).numbers
Code a character string (written word form) as letter n-gramsorthoCoding
Plot function for selected results of 'ndlClassify'.plot.ndlClassify plot.ndlProbabilities plot.ndlWeights
Plot function for the output of 'RescorlaWagner'.plot.RescorlaWagner
Artificial data set used to illustrate the Rescorla-Wagner equations and naive discriminative learning.plurals
Predict method for ndlClassify objectspredict.ndlClassify
Calculate an approximation of the pseudoinverse of a matrix.random.pseudoinverse
Implementation of the Rescorla-Wagner equations.RescorlaWagner
Serbian case inflected nouns.serbian
Serbian lexicon with 1187 prime-target pairs.serbianLex
Serbian case inflected nouns (in Cyrillic Unicode).serbianUniCyr
Serbian case inflected nouns (in Latin-alphabet Unicode).serbianUniLat
A summary of a Naive Discriminatory Learning Modelprint.summary.ndlClassify summary.ndlClassify
A summary of a crossvalidation of a Naive Discriminatory Reader Modelprint.summary.ndlCrossvalidate summary.ndlCrossvalidate
Finnish 'think' verbs.think