## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
set.seed(2026)

## ----eval = FALSE-------------------------------------------------------------
# install.packages("remotes")
# remotes::install_github("AurelienNicosiaULaval/GLBFP")

## -----------------------------------------------------------------------------
library(GLBFP)

## -----------------------------------------------------------------------------
x <- matrix(rnorm(300), ncol = 1)
b <- compute_bi_optim(x, m = 1)

fit <- GLBFP(x = 0, data = x, b = b, m = 1)
fit

## -----------------------------------------------------------------------------
fit_alias <- glbfp(x = 0, data = x, b = b, m = 1)
identical(fit$estimation, fit_alias$estimation)

## -----------------------------------------------------------------------------
names(fit)
summary(fit)
predict(fit)

## -----------------------------------------------------------------------------
grid_fit <- GLBFP_estimate(data = x, b = b, m = 1, grid_size = 80)
head(cbind(grid_fit$grid, density = grid_fit$densities))
head(as.data.frame(grid_fit))

## -----------------------------------------------------------------------------
plot(grid_fit)

## -----------------------------------------------------------------------------
data("ashua")

river_data <- ashua[, c("flow", "level")]
b2 <- c(8, 0.4)
x0 <- c(mean(river_data$flow), mean(river_data$level))

fit2 <- GLBFP(x = x0, data = river_data, b = b2, m = c(1, 1))
fit2

grid_fit2 <- GLBFP_estimate(
  data = river_data,
  b = b2,
  m = c(1, 1),
  grid_size = 15
)

plot(grid_fit2, contour = TRUE)