DP_projections_HILS_SWLS_100
                        Data for plotting a Dot Product Projection
                        Plot.
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.
centrality_data_harmony
                        Example data for plotting a Semantic Centrality
                        Plot.
raw_embeddings_1        Word embeddings from textEmbedRawLayers
                        function
textCentrality          Compute semantic similarity score between
                        single words' word embeddings and the
                        aggregated word embedding of all words.
textCentralityPlot      Plot words according to semantic similarity to
                        the aggregated word embedding.
textClassify            Predict label and probability of a text using a
                        pretrained classifier language model.
                        (experimental)
textDescriptives        Compute descriptive statistics of character
                        variables.
textDimName             Change the names of the dimensions in the word
                        embeddings.
textDistance            Compute the semantic distance between two text
                        variables.
textDistanceMatrix      Compute semantic distance scores between all
                        combinations in a word embedding
textDistanceNorm        Compute the semantic distance between a text
                        variable and a word norm (i.e., a text
                        represented by one word embedding that
                        represent a construct/concept).
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 embedding.
textEmbedRawLayers      Extract layers of hidden states (word
                        embeddings) for all character variables in a
                        given dataframe.
textEmbedReduce         Pre-trained dimension reduction (experimental)
textEmbedStatic         Applies word embeddings from a given
                        decontextualized static space (such as from
                        Latent Semantic Analyses) to all character
                        variables
textFineTuneDomain      Domain Adapted Pre-Training (EXPERIMENTAL -
                        under development)
textFineTuneTask        Task Adapted Pre-Training (EXPERIMENTAL - under
                        development)
textGeneration          Predicts the words that will follow a specified
                        text prompt. (experimental)
textModelLayers         Get the number of layers in a given model.
textModels              Check downloaded, available models.
textModelsRemove        Delete a specified model and model associated
                        files.
textNER                 Named Entity Recognition. (experimental)
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             Trained models created by e.g., textTrain() or
                        stored on e.g., github can be used to predict
                        new scores or classes from embeddings or text
                        using textPredict.
textPredictAll          Predict from several models, selecting the
                        correct input
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.
textQA                  Question Answering. (experimental)
textSimilarity          Compute the semantic similarity between two
                        text variables.
textSimilarityMatrix    Compute semantic similarity scores between all
                        combinations in a word embedding
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).
textSum                 Summarize texts. (experimental)
textTokenize            Tokenize according to different huggingface
                        transformers
textTopics              This function creates and trains a BERTopic
                        model (based on bertopic python packaged) on a
                        text-variable in a tibble/data.frame.
                        (EXPERIMENTAL)
textTopicsReduce        textTopicsReduce (EXPERIMENTAL)
textTopicsTest          This function tests the relationship between a
                        single topic or all topics and a variable of
                        interest. Available tests include correlation,
                        t-test, linear regression, binary regression,
                        and ridge regression. (EXPERIMENTAL - under
                        development)
textTopicsTree          textTopicsTest (EXPERIMENTAL) to get the
                        hierarchical topic tree
textTopicsWordcloud     This functions plots wordclouds of topics from
                        a Topic Model based on their significance
                        determined by a linear or binary regression
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.
textTrainN              (experimental) Compute cross-validated
                        correlations for different sample-sizes of a
                        data set. The cross-validation process can be
                        repeated several times to enhance the
                        reliability of the evaluation.
textTrainNPlot          (experimental) Plot cross-validated correlation
                        coefficients across different sample-sizes from
                        the object returned by the textTrainN function.
                        If the number of cross-validations exceed one,
                        then error-bars will be included in the plot.
textTrainRandomForest   Train word embeddings to a categorical variable
                        using random forest.
textTrainRegression     Train word embeddings to a numeric variable.
textTranslate           Translation. (experimental)
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 (experimental).
textZeroShot            Zero Shot Classification (Experimental)
textrpp_initialize      Initialize text required python packages
textrpp_install         Install text required python packages in conda
                        or virtualenv environment
textrpp_uninstall       Uninstall textrpp conda environment
word_embeddings_4       Word embeddings for 4 text variables for 40
                        participants
