# Install dependencies
install.packages(c("Rcpp", "RcppEigen", "testthat"))
# Build and install
remotes::install_github("alrobles/maxentcpp")

source(system.file("examples", "quickstart.R", package = "maxentcpp"))

library(maxentcpp)
library(terra)
# --- 1. Load raster data from the package -----------------------------------
stack_path      <- system.file("extdata", "stack_1_12_crop.rds",
                               package = "maxentcpp")
example_rasters <- terra::unwrap(readRDS(stack_path))
g_bio1  <- maxent_grid_from_terra(example_rasters[[1]])
g_bio12 <- maxent_grid_from_terra(example_rasters[[2]])
# --- 2. Occurrence records ---------------------------------------------------
data(example_occ_df)   # columns: species, long, lat
info <- maxent_grid_info(g_bio1)
dim  <- maxent_dimension(info$nrows, info$ncols,
                         info$xll, info$yll, info$cellsize)
occ  <- maxent_read_occurrences(example_occ_df, dim,
                                lon_col = "long", lat_col = "lat")
# --- 3. Background points ----------------------------------------------------
bg <- maxent_background_indices(g_bio1, n = 10000, seed = 42)
# --- 4. Features -------------------------------------------------------------
all_rows <- c(bg$rows, occ$rows)
all_cols <- c(bg$cols, occ$cols)
n_total  <- length(all_rows)
sample_indices <- seq(length(bg$rows), n_total - 1L)
bio1_vals  <- sapply(seq_along(all_rows), function(i)
    grid_get_value(g_bio1,  all_rows[i], all_cols[i]))
bio12_vals <- sapply(seq_along(all_rows), function(i)
    grid_get_value(g_bio12, all_rows[i], all_cols[i]))
features <- maxent_generate_features(
    list(bio1 = bio1_vals, bio12 = bio12_vals),
    types = c("linear", "quadratic", "hinge"), n_hinges = 15)
# --- 5. Train ----------------------------------------------------------------
fs     <- maxent_featured_space(n_total, as.integer(sample_indices), features)
result <- maxent_fit(fs, max_iter = 500, convergence = 1e-5)
cat("AUC:", maxent_evaluate(
    maxent_extract_predictions(fs, list(g_bio1, g_bio12),
                               c("bio1", "bio12"), occ$rows, occ$cols),
    maxent_extract_predictions(fs, list(g_bio1, g_bio12),
                               c("bio1", "bio12"), bg$rows,  bg$cols))$auc, "\n")
# --- 6. Project and visualise ------------------------------------------------
pred_raster <- maxent_grid_to_terra(
    maxent_project_cloglog(fs, list(g_bio1, g_bio12), c("bio1", "bio12")))
terra::plot(pred_raster,
            main = "Predicted Habitat Suitability (cloglog)",
            col  = hcl.colors(50, "YlOrRd", rev = TRUE))

maxent_grid_from_terra()        --- Load raster layers (terra SpatRaster)
maxent_read_occurrences()       --- Ingest presence records (data frame / CSV)
maxent_background_indices()     --- Sample background points
maxent_generate_features()      --- Build feature set
maxent_featured_space()         --- Create model object
maxent_fit()                    --- Train the MaxEnt model
maxent_save_lambdas()           --- Persist model to disk
maxent_project_cloglog()        --- Spatial prediction (cloglog output)
maxent_grid_to_terra()          --- Convert output to terra SpatRaster
maxent_evaluate()               --- Compute AUC + metrics
maxent_response_curve()         --- Variable response plots
maxent_permutation_importance() --- Variable ranking
maxent_mess()                   --- Environmental novelty (MESS)
maxent_clamp()                  --- Safe extrapolation
--- Output layer (report / file artifacts) ---
maxent_color_ramp()             --- Canonical Java-compatible colour ramp
maxent_write_prediction_png()   --- Prediction map PNG with legend + dots
maxent_plot_response_curves()   --- Response curve PNGs (full + thumbnail)
maxent_plot_variable_importance()--- Variable importance bar chart PNG
maxent_write_omission_csv()     --- Omission/threshold CSV (9 thresholds)
maxent_write_sample_predictions()--- Sample predictions CSV
maxent_print_results()          --- Print dismo-style performance metrics to console
maxent_append_results_csv()     --- Append row to maxentResults.csv
maxent_run()                    --- One-click full pipeline wrapper