centrality_data_harmony |
Example data for plotting a Semantic Centrality Plot. |
DP_projections_HILS_SWLS_100 |
Data for plotting a Dot Product Projection Plot. |
embeddings_from_huggingface2 |
Word embeddings from textEmbedLayersOutput function |
Language_based_assessment_data_3_100 |
Example text and numeric data. |
Language_based_assessment_data_8 |
Text and numeric data for 10 participants. |
PC_projections_satisfactionwords_40 |
Example data for plotting a Principle Component Projection Plot. |
textCentrality |
Compute cosine semantic similarity score between single words' word embeddings and the aggregated word embedding of all words. |
textCentralityPlot |
Plot words according to cosine semantic similarity to the aggregated word embedding. |
textEmbed |
Extract layers and aggregate them to word embeddings, for all character variables in a given dataframe. |
textEmbedLayerAggregation |
Select and aggregate layers of hidden states to form a word embeddings. |
textEmbedLayersOutput |
Extract layers of hidden states (word embeddings) for all character variables in a given dataframe. |
textEmbedStatic |
Applies word embeddings from a given decontextualized static space (such as from Latent Semantic Analyses) to all character variables |
textPCA |
Compute 2 PCA dimensions of the word embeddings for individual words. |
textPCAPlot |
Plot words according to 2-D plot from 2 PCA components. |
textPlot |
Plot words from textProjection() or textWordPrediction(). |
textPredict |
Predict scores or classification from, e.g., textTrain. |
textPredictTest |
Significance testing correlations If only y1 is provided a t-test is computed, between the absolute error from yhat1-y1 and yhat2-y1. |
textProjection |
Compute Supervised Dimension Projection and related variables for plotting words. |
textProjectionPlot |
Plot words according to Supervised Dimension Projection. |
textrpp_initialize |
Initialize text required python packages |
textrpp_install |
Install text required python packages in conda or virtualenv environment |
textrpp_install_virtualenv |
Install text required python packages in conda or virtualenv environment |
textrpp_uninstall |
Uninstall textrpp conda environment |
textSimilarity |
Compute the cosine semantic similarity between two text variables. |
textSimilarityNorm |
Compute the semantic similarity between a text variable and a word norm (i.e., a text represented by one word embedding that represent a construct). |
textSimilarityTest |
Test whether there is a significant difference in meaning between two sets of texts (i.e., between their word embeddings). |
textTrain |
Train word embeddings to a numeric (ridge regression) or categorical (random forest) variable. |
textTrainLists |
Individually trains word embeddings from several text variables to several numeric or categorical variables. It is possible to have word embeddings from one text variable and several numeric/categprical variables; or vice verse, word embeddings from several text variables to one numeric/categorical variable. It is not possible to mix numeric and categorical variables. |
textTrainRandomForest |
Train word embeddings to a categorical variable using random forrest. |
textTrainRegression |
Train word embeddings to a numeric variable. |
textWordPrediction |
Compute predictions based on single words for plotting words. The word embeddings of single words are trained to predict the mean value associated with that word. P-values does NOT work yet. |
word_embeddings_4 |
Word embeddings for 4 text variables for 40 participants |