Package index • lexsync

Corpora and lexica

Reach a word-frequency corpus in any registered language, and read it in.

list_corpora(): List the corpora known to the registry
fetch_corpus(): Download a CSV-format registered corpus into the cache
lexsync_cache_dir(): Per-user cache directory for fetched corpora
load_lexicon(): Load a lexicon from a CSV file
load_items(): Load a paradigm item table (prime-target pairs, sentences, ...)
load_pool(): Load a supplied candidate pool of words and give it the matcher's dimensions
merge_norms(): Left-join a norm table (e.g. concreteness, age of acquisition, valence)

Derive the dimensions that stimuli are later matched or controlled on.

add_neighbourhood(): Compute orthographic-neighbourhood dimensions (Coltheart's N and OLD20)
add_bigram_frequency(): Mean bigram probability (type-based, non-positional), a phonotactic-probability proxy
add_pair_overlap(): Orthographic overlap between the two members of each pair
count_syllables(): Orthographic syllable estimate: the number of maximal vowel runs

Filter a lexicon to a candidate pool, then match conditions across dimensions in parallel.

build_pool(): Build an experimental candidate pool by filtering a lexicon
match_stimuli(): Match stimuli across conditions on several lexical dimensions
resample_stimuli(): Produce several disjoint matched item sets (items as a random factor)

Span a predictor instead of dichotomising it, holding the control dimensions constant.

select_continuous_stimuli(): Select a set spanning a continuous predictor, holding controls constant
match_report_continuous(): Realised-control report for a continuous design

Deterministic non-words, and the lexical-decision sets built from them.

generate_pseudowords(): A length-matched pseudoword for each base word (byte-order processing)
make_pseudoword(): The most bigram-plausible legal non-word at the smallest edit distance
build_lexdec_stimuli(): Assemble a word-vs-pseudoword lexical-decision set from a candidate pool

The declarative trial model, and the fields a design must supply to it.

PARADIGMS: The paradigm registry: default event sequences and required fields
resolve_events(): The design's trial event list: its own events, else its paradigm's
resolve_trial_timing(): Realise per-trial event durations onto the stimuli table
required_fields(): Trial fields a design needs present in its items (paradigm + events)

Rotate items across lists and assign participants to them.

counterbalance(): Assign stimuli to lists and a randomised, reproducible trial order
balance_lists(): Assign item sets to counterbalancing lists so the lists match on the item dimensions
participant_table(): Build a participant counterbalancing table

Report the realised control, testing for equivalence rather than for a null result.

match_report(): Build the full match-quality report
describe_stimuli(): Per-group descriptive statistics for several dimensions
balance_check(): Check that the levels of given columns occur equally often
variance_ratio(): Variance ratio: a distributional balance check
cohens_d(): Cohen's d (pooled-SD standardised mean difference)
cohens_d_ci(): Cohen's d with a confidence interval, complementing the TOST verdict
tost_equiv(): Two one-sided tests (TOST) of equivalence on a Cohen's d bound

Render the events into runnable experiments, with EEG triggers on the laboratory targets.

export_experiments(): Export all presentation targets (PsychoPy, OpenSesame, jsPsych)
export_psychopy(): Export a runnable PsychoPy script that interprets the event sequence
export_opensesame(): Export a complete plain-text OpenSesame experiment
export_jspsych(): Export a browser-runnable jsPsych experiment
assign_triggers(): Assign EEG trigger codes to stimuli

The provenance, checksums and realised control that travel with a stimulus set.

build_datasheet(): Assemble the materials datasheet for one design
write_datasheet(): Write a datasheet to a JSON record and a Markdown rendering
methods_paragraph(): A ready-to-adapt methods paragraph rendered from a datasheet

Run a design end to end, and log every step and artefact as it is written.

run_pipeline(): Run the lexsync pipeline for one design
run_all(): Run the lexsync pipeline for every design configuration
new_run_log(): Start a new run log
log_step(): Append a step to a run log
log_artefact(): Record a written artefact (path, rows, fingerprint) in the log
write_run_log(): Write the run log to Markdown (and optionally JSON Lines)