Package index • tripletTools

All functions

align.embeddings(): Align embeddings across participants
assign_sample_alg(): Label each trial with its sampling algorithm
assign_sample_sets(): Assign trials to train or test sets
estimate_dimensionality(): Estimate the latent dimensionality of a triplet dataset
filter_failed_catch(): Exclude participants who fail too many catch trials
filter_fast_responders(): Exclude participants with suspiciously fast mean reaction times
filter_incomplete(): Exclude participants with too few trials
get.combined(): Get combined data
get.group.list.mean(): Get group list mean
get.hoacc(): Get hold-out prediction accuracy
get.nearest.k(): Get nearest k
get.participant.summary(): Get participant summary
get.prediction.matrix(): Get prediction matrix
get.raster.from.png(): Get raster from PNG
get.rep.dist(): Get representational distances
get.tip.coords(): Get tip coordinates
icon_emb_group: Group embedding data for 32 icon images of faces and buildings
icon_emb_ind: Individual embedding data for 32 icon images of faces and buildings
icon_pics: Face and Place icon pictures
icon_triplets: Triplet data for 32 icons of faces and places
make.tripnames(): Make triplet names
make.vmat(): Make validation matrix
model.strength(): Get model strength
pacc.by.cluster(): Prediction accuracy by cluster
plot_cis(): Plot column means and confidence intervals
plot_pics(): Plot pictures in a scatterplot
process_choices(): Clean raw choice strings from jsPsych CSV output
read_legacy(): Standardise a legacy triadic comparison dataset
read_raw_data(): Clean raw jsPsych triplet experiment data
run_embeddings(): Run the full embedding pipeline for all workers
run_embeddings_from_list(): Run the embedding pipeline from a triplet data list
run_group_embedding_from_list(): Compute a group-level embedding from a triplet data list
setup_python_env(): Set up the Python environment for triplet embeddings
strsplit1(): Split a string
test.model(): Test embedding model predictions.
train_embedding(): Train a single triplet embedding model
write_embedding_list(): Write a list of individual embeddings to a CSV file
z.pred.mat(): Z-score prediction matrix