## ----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)
#