Other packages > Find by keyword >

RCTS  

Clustering Time Series While Resisting Outliers
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]
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) . The implementation within this package excludes the SCAD-penalty on the estimations of beta. This robust framework is developed in Boudt & Heyndels (2022) 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. Accessed 25 Jun. 2026.
Previous versions and publish date:
0.2.2 (2022-06-20 18:20), 0.2.3 (2022-09-14 09:40)
Other packages that cited RCTS R package
View RCTS citation profile
Other R packages that RCTS depends, imports, suggests or enhances
Complete documentation for RCTS
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
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  
edeaR  
Exploratory and Descriptive Event-Based Data Analysis
Exploratory and descriptive analysis of event based data. Provides methods for describing and select ...
Download / Learn more Package Citations See dependency  
airGRiwrm  
'airGR' Integrated Water Resource Management
Semi-distributed Precipitation-Runoff Modelling based on 'airGR' package models integrating human i ...
Download / Learn more Package Citations See dependency  
foster  
Forest Structure Extrapolation with R
Set of tools to streamline the modeling of the relationship betweensatellite imagery time series or ...
Download / Learn more Package Citations See dependency  
sitmo  
Parallel Pseudo Random Number Generator (PPRNG) 'sitmo' Header Files
Provided within are two high quality and fast PPRNGs that may be used in an 'OpenMP' parallel enviro ...
Download / Learn more Package Citations See dependency  

27,535

R Packages

236,180

Dependencies

73,223

Author Associations

27,536

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA