Package: XSLmodels 0.4.0

XSLmodels: Run and Optimize Cross-Situational Word Learning Models

A collection of cognitive models of cross-situational word learning, together with methods and datasets for fitting and cross-validating them against experimental and corpus data, and for plotting the results.

Authors:George Kachergis [aut, cre], Mika Braginsky [aut], Kevin Liang [aut]

XSLmodels_0.4.0.tar.gz
XSLmodels_0.4.0.zip(r-4.7-any)XSLmodels_0.4.0.zip(r-4.6-any)XSLmodels_0.4.0.zip(r-4.5-any)
XSLmodels_0.4.0.tgz(r-4.6-any)XSLmodels_0.4.0.tgz(r-4.5-any)
XSLmodels_0.4.0.tar.gz(r-4.7-any)XSLmodels_0.4.0.tar.gz(r-4.6-any)
XSLmodels_0.4.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
XSLmodels/json (API)

# Install 'XSLmodels' in R:

install.packages('XSLmodels', repos = c('https://kachergis.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kachergis/xslmodels/issues

Pkgdown/docs site:https://www.kachergis.com

Datasets:

benitez2020 - Benitez et al. (2020) temporal-structure-of-naming experiment data
fm_corpus - Frank, Tenenbaum & Fernald naturalistic word-learning corpus
gangwani2011_category - Gangwani, Kachergis & Yu simultaneous category + object name learning
kachergis_initial_accuracy - Kachergis, Grimmick, & Gureckis initial accuracy experiment data
kachergis2012_highlighting - Kachergis (2012) highlighting experiment data
koehne2013 - Koehne, Trueswell, & Gleitman (2013) multiple-meaning word learning
rollins_corpus - CHILDES/Rollins naturalistic word-learning corpus
suanda2014 - Suanda, Mugwanya, & Namy (2014) cross-situational learning in children
vlach_debrock2017 - Vlach & DeBrock (2017) spaced/massed repetition experiment data
xsl_datasets - Cross-situational word learning experiment data
xsl_holdout_datasets - Registry of datasets held out from xsl_datasets

On CRAN:

5.18 score 17 scripts 56 exports 30 dependencies

Last updated from:b44b9fa9f2. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	233
source / vignettes	OK	247
linux-release	OK	187
macos-release	OK	257
macos-oldrel	OK	239
windows-devel	OK	205
windows-release	OK	150
windows-oldrel	OK	209
wasm-release	OK	116

Exports:baseline bayesian_decay create_cooc_matrix cross_validated_group_fits decay fazly fgt2009 fgt2009_rsa fgt2009_sweep_alpha get_crossvalidated_model_fit get_example_ambiguous_condition get_example_unambiguous_condition get_fscore get_group_model_fit get_perf get_roc get_roc_max get_tp get_train_test_split guess_and_test kalman_filter mafc_test minerva2 multi_sampling new_xslControl new_xslData new_xslFit new_xslMod plot_roc plot_training_trials predict_referent propose_but_verify pursuit rem rescorla_wagner rsa_listener rsa_speaker show_datasets show_models softmax_rl tilles todam uncfam uncfam_attention uncfam_elimination uncfam_gamma uncfam_general uncfam_predictive uncfam_sampling update_params xsl_fit xsl_run xslControl xslData xslFit xslMod

Dependencies:cli cpp11 DEoptim dplyr farver generics ggplot2 glue gtable isoband labeling lifecycle magrittr pillar pkgconfig purrr R6 RColorBrewer rlang S7 scales stringi stringr tibble tidyr tidyselect utf8 vctrs viridisLite withr

Overview of XSLmodels: Importing cross-situational datasets and fitting models

Defining and structure of cross-situational learning (XSL) datasets. | 0. Importing a Dataset | 1. Structure of an experimental condition | 2. Viewing an experiment's structure | 3. Included experimental conditions | Running, fitting, and defining XSL models. | 4. Run model with given parameters through a single dataset and pull SSE. | 5. Run a model with given parameters through multiple datasets and pull SSE. | 6. Evaluating model performance | 7. Using the helper functions | 8. Model fitting | 9. Available models | 10. Prior learning: training trials or a start matrix | 11. Testing generalization on held-out datasets

Rendered fromXSLmodels.Rmdusingknitr::rmarkdown

Last update: 2026-10-07
Started: 2026-07-12

Running and fitting models on naturalistic corpora

Rendered fromcorpora.Rmdusingknitr::rmarkdown

Last update: 2026-10-07
Started: 2026-09-04

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Baseline model	baseline
Bayesian decay model	bayesian_decay
Benitez et al. (2020) temporal-structure-of-naming experiment data	benitez2020
Creates co-occurrence matrix from training trials	create_cooc_matrix
Perform Cross-Validation on Group Fits of a Model	cross_validated_group_fits
Decay model	decay
Fazly et al. 2010 probablistic associative model	fazly
Frank, Goodman & Tenenbaum (2009) intentional Bayesian word-learning model	fgt2009
Frank, Goodman & Tenenbaum word-learning model with pragmatic (RSA) reasoning	fgt2009_rsa
Sweep the lexicon-size prior alpha for an FGT model	fgt2009_sweep_alpha
Frank, Tenenbaum & Fernald naturalistic word-learning corpus	fm_corpus
Gangwani, Kachergis & Yu simultaneous category + object name learning	gangwani2011_category
Get cross-validated model fit	get_crossvalidated_model_fit
Get example ambiguous condition	get_example_ambiguous_condition
Get example unambiguous condition	get_example_unambiguous_condition
Calculate F-score, precision, recall, and specificity for a knowledge matrix at a given threshold	get_fscore
Get group model fit for a specific model	get_group_model_fit
Calculate Luce choice (proportion correct) for each item in a model knowledge matrix	get_perf
Calculate receiver operating characteristic (ROC) scores for a model association Matrix	get_roc get_roc_max plot_roc
Get true positives (TP), given a knowledge matrix and a gold-standard lexicon	get_tp
Split Data into Training and Testing Sets Based on Indices	get_train_test_split
Guess and test model	guess_and_test
Kachergis, Grimmick, & Gureckis initial accuracy experiment data	kachergis_initial_accuracy
Kachergis (2012) highlighting experiment data	kachergis2012_highlighting
Kalman filter associative model	kalman_filter
Koehne, Trueswell, & Gleitman (2013) multiple-meaning word learning	koehne2013
Evaluate m-alternative forced choice test	mafc_test
MINERVA2 episodic memory model	minerva2
Multi-sampling associative model	multi_sampling
Constructor for xsl_model S3 class	new_xslFit new_xslMod xslMod xslMod-class
Create Animated Plot of Co-Occurrence Matrix from Training Trials	plot_training_trials
Predict referent selection for a single trial from an association matrix	predict_referent
Trueswell et al. 2013 propose-but-verify model	propose_but_verify
Stevens et al. 2014 pursuit model	pursuit
REM (Retrieving Effectively from Memory) model	rem
Rescorla-Wagner (1972) error-driven associative model	rescorla_wagner
CHILDES/Rollins naturalistic word-learning corpus (Frank et al. 2009)	rollins_corpus
Rational Speech Act pragmatic listener and speaker	rsa_listener rsa_speaker
Show available datasets in the package	show_datasets
Show available models in the package	show_models
Softmax reinforcement learning model	softmax_rl
Suanda, Mugwanya, & Namy (2014) cross-situational learning in children	suanda2014
Tilles and Fontanari (2013) reinforcement model	tilles
TODAM holographic reduced representation model	todam
Kachergis 2012	uncfam
Biased associative model with attention scaled to trial uncertainty	uncfam_attention
Biased associative model with learning by elimination	uncfam_elimination
Kachergis 2012 with a free familiarity exponent	uncfam_gamma
General biased associative model (nests the uncfam family)	uncfam_general
Predictive biased associative model (item-level prediction error)	uncfam_predictive
Kachergis 2012	uncfam_sampling
Update parameters of xslMod	update_params
Vlach & DeBrock (2017) spaced/massed repetition experiment data	vlach_debrock2017
Cross-situational word learning experiment data	xsl_datasets
Fit XSL model using differential evolution	xsl_fit
Registry of datasets held out from xsl_datasets	xsl_holdout_datasets
Run XSL model	xsl_run
xslControl S3 class	new_xslControl xslControl xslControl-class
xslData S3 class	new_xslData xslData xslData-class
xslFit S3 class	xslFit xslFit-class