rfcd: Random Forest Change Point Detection

#' Create an object of class hdcd_control to supply parameters to \link{hdcd}
#' 
#' @export
hdcd_control <- function(cv_inner = FALSE,
                         cv_inner_lambda = NULL,
                         cv_inner_randomize_folds = FALSE,
                         cv_inner_n_lambda = 10,
                         cv_inner_n_folds = 10,
                         cv_inner_nrep_em = 5,
                         cv_inner_search_lambda = F,
                         cv_inner_min_grid_ratio = 0.01,
                         cv_inner_lambda_step = (0.1) ^ (1/10),
                         cv_inner_stop_early = FALSE,
                         glasso_standardize = TRUE,
                         glasso_penalize_diagonal = FALSE,
                         glasso_threshold = 1e-4,
                         glasso_NA_method = 'loh_wainwright_bias_correction',
                         nodewise_regression_node = 1,
                         elastic_net_alpha = 1,
                         elastic_net_family = 'gaussian',
                         verbose = F,
                         max_depth = Inf,
                         section_search_min_points = 5,
                         section_search_stepsize = 0.5,
                         section_search_k_sigma = 0,
                         section_search_tolerance = 0,
                         segment_loss_min_points = 2,
                         random_forest_n_tree = 600,
                         random_forest_mtry = NULL,
                         random_forest_sample_fraction = 1,
                         u_statistic_n_MC = 250,
                         u_statistic_n_Z = 25,
                         u_statistic_alpha = 1,
                         two_step_search_find_best_split = 'shift_in_mean_and_variance',
                         wbs_n_segments = 100
){
  structure(list(
    cv_inner = cv_inner,
    cv_inner_randomize_folds = cv_inner_randomize_folds,
    cv_inner_lambda = cv_inner_lambda,
    cv_inner_n_lambda = cv_inner_n_lambda,
    cv_inner_n_folds = cv_inner_n_folds,
    cv_inner_nrep_em = cv_inner_nrep_em,
    cv_inner_search_lambda = cv_inner_search_lambda,
    cv_inner_min_grid_ratio = cv_inner_min_grid_ratio,
    cv_inner_lambda_step = cv_inner_lambda_step,
    cv_inner_stop_early = cv_inner_stop_early,
    glasso_standardize = glasso_standardize,
    glasso_penalize_diagonal = glasso_penalize_diagonal,
    glasso_threshold = glasso_threshold,
    glasso_NA_method = glasso_NA_method,
    nodewise_regression_node = nodewise_regression_node,
    elastic_net_alpha = elastic_net_alpha,
    elastic_net_family = elastic_net_family,
    verbose = verbose,
    max_depth = max_depth,
    section_search_min_points = section_search_min_points,
    section_search_stepsize = section_search_stepsize,
    section_search_k_sigma = section_search_k_sigma,
    section_search_tolerance = section_search_tolerance,
    segment_loss_min_points = segment_loss_min_points,
    random_forest_n_tree = random_forest_n_tree,
    random_forest_mtry = random_forest_mtry,
    random_forest_sample_fraction = random_forest_sample_fraction,
    u_statistic_n_MC = u_statistic_n_MC,
    u_statistic_n_Z = u_statistic_n_Z,
    u_statistic_alpha = u_statistic_alpha,
    two_step_search_find_best_split = two_step_search_find_best_split,
    wbs_n_segments = wbs_n_segments
  ), class = "hdcd_control")
}