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

## ----minimal example duration, warning = FALSE, message = FALSE---------------
library(bistablehistory)
data(br_singleblock)
gamma_fit <- fit_cumhist(br_singleblock,
                         state="State",
                         duration="Duration",
                         refresh=0)

## ----eval=FALSE---------------------------------------------------------------
# gamma_fit <- fit_cumhist(br_singleblock,
#                         state="State",
#                         onset="Time")

## -----------------------------------------------------------------------------
history_tau(gamma_fit)

## -----------------------------------------------------------------------------
historyef(gamma_fit)

## ----eval=FALSE---------------------------------------------------------------
# gamma_fit <- fit_cumhist(br_singleblock,
#                         state="State",
#                         duration="Duration",
#                         control=list(max_treedepth = 15,
#                                      adapt_delta = 0.99))

## ----eval=FALSE---------------------------------------------------------------
# gamma_fit <- fit_cumhist(br_single_subject,
#                         state="State",
#                         onset="Time",
#                         run="Block")

## ----eval=FALSE---------------------------------------------------------------
# gamma_fit <-  fit_cumhist(kde_two_observers,
#                           state="State",
#                           duration="Duration",
#                           random_effect="Observer",
#                           run="Block")

## ----eval=FALSE---------------------------------------------------------------
# predictions <- predict(gam_fit, summary = TRUE, probs = c((1-0.89)/2, 1 - (1-0.89)/2))

## ----eval=FALSE---------------------------------------------------------------
# prediction_samples <- predict(gam_fit, summary = FALSE, full_length = TRUE)

## ----eval=FALSE---------------------------------------------------------------
# prediction_samples <- predict(gam_fit, summary = FALSE, full_length = FALSE)

## ----eval=FALSE---------------------------------------------------------------
# H <- predict_history(gam_fit, "difference")

## ----eval=FALSE---------------------------------------------------------------
# df <- compute_history(br_singleblock,
#                       state="State",
#                       duration="Duration",
#                       tau=1,
#                       mixed_state=0.5,
#                       history_init=0)