---
title: "References"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{References}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = FALSE,
  comment = "",
  R.options = list(
    cli.num_colors = 1,
    cli.hyperlink = FALSE,
    crayon.enabled = FALSE,
    width = 80
  )
)
# Console colour carries no meaning on a rendered page. pkgdown turns it on for
# its own build, and the escape sequences then reach the reader as literal text,
# so colour is switched off here for a plain vignette render and a site build
# alike. The fixed width keeps printed output inside the documentation column.
```

The guides and the function reference pages cite published work throughout, and
this page carries the full entries so that every citation on the site resolves.
Each guide also lists the works it cites at its own foot, and this page gathers
those lists together with the works cited only in the function documentation and
in the design configurations that ship with the package. Every entry carries a
DOI wherever the work has one, and any work cited on both sites reads the same
here as on the [Python twin's reference
page](https://pablobernabeu.github.io/lexsync/python/references/). The
lexical corpora that lexsync reads are attributed separately, in
[`corpora/ATTRIBUTION.md`](https://github.com/pablobernabeu/lexsync/blob/main/corpora/ATTRIBUTION.md)
and in the corpus registry, since their licences travel with the derived data
rather than with the software.

::: {.references}
Andrews, S. (1989). Frequency and neighborhood effects on lexical access:
Activation or search? *Journal of Experimental Psychology: Learning, Memory, and
Cognition*, *15*(5), 802–814. https://doi.org/10.1037/0278-7393.15.5.802

Armstrong, B. C., Watson, C. E., & Plaut, D. C. (2012). SOS! An algorithm and
software for the stochastic optimization of stimuli. *Behavior Research
Methods*, *44*(3), 675–705. https://doi.org/10.3758/s13428-011-0182-9

Austin, P. C. (2009). Balance diagnostics for comparing the distribution of
baseline covariates between treatment groups in propensity-score matched
samples. *Statistics in Medicine*, *28*(25), 3083–3107.
https://doi.org/10.1002/sim.3697

Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling
with crossed random effects for subjects and items. *Journal of Memory and
Language*, *59*(4), 390–412. https://doi.org/10.1016/j.jml.2007.12.005

Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects
structure for confirmatory hypothesis testing: Keep it maximal. *Journal of
Memory and Language*, *68*(3), 255–278.
https://doi.org/10.1016/j.jml.2012.11.001

Bochynska, A., Keeble, L., Halfacre, C., Casillas, J. V., Champagne, I.-A., Chen,
K., Röthlisberger, M., Buchanan, E. M., & Roettger, T. B. (2023). Reproducible
research practices and transparency across linguistics. *Glossa
Psycholinguistics*, *2*(1). https://doi.org/10.5070/G6011239

Clark, H. H. (1973). The language-as-fixed-effect fallacy: A critique of language
statistics in psychological research. *Journal of Verbal Learning and Verbal
Behavior*, *12*(4), 335–359. https://doi.org/10.1016/S0022-5371(73)80014-3

Coltheart, M., Davelaar, E., Jonasson, J. T., & Besner, D. (1977). Access to the
internal lexicon. In S. Dornic (Ed.), *Attention and Performance VI*
(pp. 535–555). Erlbaum.

Forster, K. I. (2000). The potential for experimenter bias effects in word
recognition experiments. *Memory & Cognition*, *28*(7), 1109–1115.
https://doi.org/10.3758/BF03211812

González Alonso, J., Bernabeu, P., Silva, G., DeLuca, V., Poch, C., Ivanova, I., &
Rothman, J. (2025). Starting from the very beginning: Unraveling third language
(L3) development with longitudinal data from artificial language learning and
EEG. *International Journal of Multilingualism*, *22*(1), 119–142.
https://doi.org/10.1080/14790718.2024.2415993

Gu, X. S., & Rosenbaum, P. R. (1993). Comparison of multivariate matching
methods: Structures, distances, and algorithms. *Journal of Computational and
Graphical Statistics*, *2*(4), 405–420.
https://doi.org/10.1080/10618600.1993.10474623

Hansen, B. B., & Klopfer, S. O. (2006). Optimal full matching and related designs
via network flows. *Journal of Computational and Graphical Statistics*, *15*(3),
609–627. https://doi.org/10.1198/106186006X137047

Keuleers, E., & Brysbaert, M. (2010). Wuggy: A multilingual pseudoword generator.
*Behavior Research Methods*, *42*(3), 627–633.
https://doi.org/10.3758/BRM.42.3.627

Kuperman, V. (2015). Virtual experiments in megastudies: A case study of language
and emotion. *Quarterly Journal of Experimental Psychology*, *68*(8), 1693–1710.
https://doi.org/10.1080/17470218.2014.989865

Lakens, D. (2017). Equivalence tests: A practical primer for *t* tests,
correlations, and meta-analyses. *Social Psychological and Personality Science*,
*8*(4), 355–362. https://doi.org/10.1177/1948550617697177

Liben-Nowell, D., Strand, J., Sharp, A., Wexler, T., & Woods, K. (2019). The
danger of testing by selecting controlled subsets, with applications to
spoken-word recognition. *Journal of Cognition*, *2*(1), Article 2.
https://doi.org/10.5334/joc.51

Mathôt, S., Schreij, D., & Theeuwes, J. (2012). OpenSesame: An open-source,
graphical experiment builder for the social sciences. *Behavior Research
Methods*, *44*(2), 314–324. https://doi.org/10.3758/s13428-011-0168-7

Matuschek, H., Kliegl, R., Vasishth, S., Baayen, H., & Bates, D. (2017).
Balancing Type I error and power in linear mixed models. *Journal of Memory and
Language*, *94*, 305–315. https://doi.org/10.1016/j.jml.2017.01.001

Peirce, J., Gray, J. R., Simpson, S., MacAskill, M., Höchenberger, R., Sogo, H.,
Kastman, E., & Lindeløv, J. K. (2019). PsychoPy2: Experiments in behavior made
easy. *Behavior Research Methods*, *51*(1), 195–203.
https://doi.org/10.3758/s13428-018-01193-y

Roettger, T. B. (2019). Researcher degrees of freedom in phonetic research.
*Laboratory Phonology*, *10*(1), Article 1.
https://doi.org/10.5334/labphon.147

Rubin, D. B. (1980). Bias reduction using Mahalanobis-metric matching.
*Biometrics*, *36*(2), 293–298. https://doi.org/10.2307/2529981

Sassenhagen, J., & Alday, P. M. (2016). A common misapplication of statistical
inference: Nuisance control with null-hypothesis significance tests. *Brain and
Language*, *162*, 42–45. https://doi.org/10.1016/j.bandl.2016.08.001

Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology:
Undisclosed flexibility in data collection and analysis allows presenting
anything as significant. *Psychological Science*, *22*(11), 1359–1366.
https://doi.org/10.1177/0956797611417632

Stuart, E. A. (2010). Matching methods for causal inference: A review and a look
forward. *Statistical Science*, *25*(1), 1–21. https://doi.org/10.1214/09-STS313

van Heuven, W. J. B., Mandera, P., Keuleers, E., & Brysbaert, M. (2014).
SUBTLEX-UK: A new and improved word frequency database for British English.
*Quarterly Journal of Experimental Psychology*, *67*(6), 1176–1190.
https://doi.org/10.1080/17470218.2013.850521

Wilkinson, M. D., Dumontier, M., Aalbersberg, IJ. J., Appleton, G., Axton, M.,
Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E.,
Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O.,
Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR Guiding
Principles for scientific data management and stewardship. *Scientific Data*,
*3*, Article 160018. https://doi.org/10.1038/sdata.2016.18

Yarkoni, T. (2022). The generalizability crisis. *Behavioral and Brain Sciences*,
*45*, Article e1. https://doi.org/10.1017/S0140525X20001685

Yarkoni, T., Balota, D., & Yap, M. (2008). Moving beyond Coltheart's N: A new
measure of orthographic similarity. *Psychonomic Bulletin & Review*, *15*(5),
971–979. https://doi.org/10.3758/PBR.15.5.971
:::