.init_builtin_models    Initialize Built-in Vision Models
.vision_model_registry
                        Vision Model Registry for transforEmotion
                        Package
MASS_mvrnorm            Multivariate Normal (Gaussian) Distribution
add_vision_model        User-Friendly Vision Model Management Functions
as_rag_table            Convert RAG JSON to a table
calculate_moving_average
                        Calculate the moving average for a time series
check_findingemo_quality
                        Check FindingEmo Dataset Quality
check_nvidia_gpu        Install Necessary Python Modules
delete_transformer      Delete a Transformer Model
dlo_dynamics            Dynamics function of the DLO model
download_findingemo_data
                        Download FindingEmo-Light Dataset
emotions                Emotions Data
emoxicon_scores         Emoxicon Scores
emphasize               Generate and emphasize sudden jumps in emotion
                        scores
evaluate_emotions       Evaluate Emotion Classification Performance
generate_observables    Generate observable emotion scores data from
                        latent variables
generate_q              Generate a matrix of Dynamic Error values for
                        the DLO simulation
get_vision_model_config
                        Get Vision Model Configuration
image_scores            Calculate image scores using a Hugging Face
                        CLIP model
image_scores_dir        Calculate image scores for all images in a
                        directory (fast batch)
is_vision_model_registered
                        Check if Vision Model is Registered
list_vision_models      List Available Vision Models
load_findingemo_annotations
                        Load FindingEmo-Light Annotations
map_discrete_to_vad     Map Discrete Emotions to VAD
                        (Valence-Arousal-Dominance) Framework
map_to_emo8             Map FindingEmo Emotions to Emo8 Labels
neo_ipip_extraversion   NEO-PI-R IPIP Extraversion Item Descriptions
nlp_scores              Natural Language Processing Scores
parse_rag_json          Parse RAG JSON
plot.emotion_evaluation
                        Plot Evaluation Results
plot_sim_emotions       Plot the latent or the observable emotion
                        scores.
prepare_findingemo_evaluation
                        Prepare FindingEmo Data for Evaluation
print.emotion_evaluation
                        Print method for emotion evaluation results
punctuate               Punctuation Removal for Text
rag                     Retrieval-augmented Generation (RAG)
rag_json_utils          RAG JSON utilities
rag_sentemo             Structured Emotion/Sentiment via RAG (Small
                        LLMs)
register_retriever      Register a custom retriever
register_vision_model   Register a Vision Model
remove_vision_model     Remove a Vision Model
sentence_similarity     Sentiment Analysis Scores
setup_gpu_modules       Install GPU Python Modules
setup_miniconda         Deprecated: Miniconda setup (use uv instead)
setup_modules           Setup Required Python Modules
setup_popular_models    Quick Setup for Popular Models
show_vision_models      Show Available Vision Models
simulate_video          Simulate latent and observed emotion scores for
                        a single "video"
stop_words              Stop Words from the _tm_ Package
summary.emotion_evaluation
                        Summary method for emotion evaluation results
te_cleanup_default_venv
                        Remove reticulate's default virtualenv
                        (r-reticulate)
tinytrolls              Russian Trolls Data - Small Version
transforEmotion-package
                        transforEmotion-package
transformer_scores      Sentiment Analysis Scores
vad_scores              Direct VAD (Valence-Arousal-Dominance)
                        Prediction
validate_rag_json       Validate a RAG JSON structure
validate_rag_predictions
                        Validate RAG Emotion/Sentiment Predictions
video_scores            Run FER on a YouTube video using a Hugging Face
                        CLIP model
