# L-BAM Tutorial

    # Install the text package (only needed the first time)
    # install.packages("text")
    library(text)
    # textrpp_install()
    # textrpp_initialize()

    # Get the LBAM as a data frame and filter for models starting with “Dep”
    lbam <- text::textLBAM()

    subset(
      lbam,
      substr(Construct_Concept_Behaviours, 1, 3) == "dep",
      select = c(Construct_Concept_Behaviours, Name)
    )

    # Example text to access
    text_to_assess = c(
      "I feel down and blue all the time.",
      "I feel great and have no worries that bothers me.")

    # Produce depression severity scores using a text-trained model
    # This command downloads the model, creates word embeddings, and applies the model to the embeddings.
    depression_scores <- text::textPredict(
      model_info = "depression_text_phq9_roberta23_gu2024",
      texts = text_to_assess,
      dim_name = FALSE)

    # You can find information about a text-trained model in R.
    model_Gu2024 <- readRDS("depressiontext_robertaL23_phq9_Gu2024.rds")
    model_Gu2024

    # Assess the harmony in life of the same text as above
    # The function now uses the same word embeddings as above (i.e., it does not produce new ones).
    harmony_in_life_scores <- textAssess(
      model_info = "harmony_text_roberta23_kjell2022",
      texts = text_to_assess,
      dim_name = FALSE)

    # Assign implicit motives labels using fine-tuned models
    implicit_motive <- text::textClassify(
      model_info = "implicitpower_roberta_ft_nilsson2024",
      texts = text_to_assess)