## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
evaluate = FALSE

## ----eval = evaluate, warning=FALSE, message=FALSE, dpi=300-------------------
#  
#  # 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)
#