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RCTS
View on CRAN: Click
here
Download and install RCTS package within the R console
Install from CRAN:
install.packages("RCTS")
Install from Github:
library("remotes")
install_github("cran/RCTS")
Install by package version:
library("remotes")
install_version("RCTS", "0.2.4")
Attach the package and use:
library("RCTS")
Maintained by
Ewoud Heyndels
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-06-20
Latest Update: 2023-05-18
Description:
Robust Clustering of Time Series (RCTS) has the functionality to cluster time series using both the classical and the robust interactive fixed effects framework. The classical framework is developed in Ando & Bai (2017) <doi:10.1080/01621459.2016.1195743>. The implementation within this package excludes the SCAD-penalty on the estimations of beta. This robust framework is developed in Boudt & Heyndels (2022) <doi:10.1016/j.ecosta.2022.01.002> and is made robust against different kinds of outliers. The algorithm iteratively updates beta (the coefficients of the observable variables), group membership, and the latent factors (which can be common and/or group-specific) along with their loadings. The number of groups and factors can be estimated if they are unknown.
How to cite:
Ewoud Heyndels (2022). RCTS: Clustering Time Series While Resisting Outliers. R package version 0.2.4, https://cran.r-project.org/web/packages/RCTS
Other packages that cited RCTS R package
View RCTS citation profile
Other R packages that RCTS depends,
imports, suggests or enhances
Functions, R codes and Examples using
the RCTS R package
Some associated functions: LMROB . OF_vectorized3 . OF_vectorized_helpfunction3 . RCTS . X_dgp3 . Y_dgp3 . adapt_X_estimating_less_variables . adapt_pic_with_sigma2maxmodel . add_configuration . add_metrics . add_pic . add_pic_parallel . beta_true_heterogroups . calculate_FL_group_estimated . calculate_FL_group_true . calculate_PIC . calculate_PIC_term1 . calculate_TN_factor . calculate_VCsquared . calculate_W . calculate_XB_estimated . calculate_XB_true . calculate_Z_common . calculate_Z_group . calculate_best_config . calculate_error_term . calculate_errors_virtual_groups . calculate_lambda . calculate_lambda_group . calculate_lgfg . calculate_mse_beta . calculate_obj_for_g . calculate_sigma2 . calculate_sigma2maxmodel . calculate_virtual_factor_and_lambda_group . check_stopping_rules . clustering_with_robust_distances . create_covMat_crosssectional_dependence . create_data_dgp2 . create_true_beta . define_C_candidates . define_configurations . define_kg_candidates . define_number_subsets . define_object_for_initial_clustering_macropca . define_rho_parameters . determine_beta . determine_robust_lambda . df_results_example . do_we_estimate_common_factors . do_we_estimate_group_factors . estimate_algorithm . estimate_beta . estimate_factor . estimate_factor_group . evade_crashes_macropca . evade_floating_point_errors . factor_group_true_dgp3 . fill_rc . fill_rcj . final_estimations_filter_kg . g_true_dgp3 . generate_Y . generate_grouped_factorstructure . get_best_configuration . get_convergence_speed . get_final_estimation . grid_add_variables . handleNA . handleNA_LG . handle_macropca_errors . initialise_X . initialise_beta . initialise_clustering . initialise_commonfactorstructure_macropca . initialise_df_pic . initialise_df_results . initialise_rc . initialise_rcj . iterate . kg_candidates_expand . lambda_group_true_dgp3 . make_df_pic_parallel . make_df_results_parallel . make_subsamples . matrixnorm . mse_heterogeneous_groups . parallel_algorithm . plot_VCsquared . prepare_for_robpca . reassign_if_empty_groups . restructure_X_to_order_slowN_fastT . return_robust_lambdaobject . robustpca . run_config . scaling_X . solveFG . tabulate_potential_C . update_g .
Some associated R codes: 03_IFE_algorithm_functions.R . 07_IFE_robust_lambda.R . RCTS.R . dataset_X_dgp3.R . dataset_Y_dgp3.R . dataset_df_results_example.R . dataset_factor_group_true_dgp3.R . dataset_g_true_dgp3.R . dataset_lambda_group_true_dgp3.R . functions_cleaning.R . functions_parallel.R . Full RCTS package functions and examples
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