{ "_id": "6a101dbcacfb0bcc41c8a66b", "Package": "RCTS", "Title": "Clustering Time Series While Resisting Outliers", "Version": "0.2.4", "Authors@R": "person(given = \"Ewoud\",\nfamily = \"Heyndels\",\nrole = c(\"aut\", \"cre\"),\nemail = \"ewoud.heyndels@vub.be\",\ncomment = c(ORCID = \"0000-0003-4540-8571\"))", "Description": "Robust Clustering of Time Series (RCTS) has the\nfunctionality to cluster time series using both the classical\nand the robust interactive fixed effects framework. The\nclassical framework is developed in Ando & Bai (2017)\n. The implementation within\nthis package excludes the SCAD-penalty on the estimations of\nbeta. This robust framework is developed in Boudt & Heyndels\n(2022) and is made robust\nagainst different kinds of outliers. The algorithm iteratively\nupdates beta (the coefficients of the observable variables),\ngroup membership, and the latent factors (which can be common\nand/or group-specific) along with their loadings. The number of\ngroups and factors can be estimated if they are unknown.", "License": "GPL (>= 2)", "Encoding": "UTF-8", "LazyData": "true", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.2.3", "RdMacros": "Rdpack", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://eh-in-r.r-universe.dev", "Date/Publication": "2023-05-18 13:31:53 UTC", "RemoteUrl": "https://github.com/eh-in-r/rcts", "RemoteRef": "HEAD", "RemoteSha": "f6a1ba4cdddd14d5faae291c9e6120247752aac9", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-14 07:44:32 UTC", "User": "root" }, "Author": "Ewoud Heyndels [aut, cre] (ORCID:\n)", "Maintainer": "Ewoud Heyndels ", "MD5sum": "43a3df7e7bc4c47e5fb37caedf469e14", "_user": "eh-in-r", "_type": "src", "_file": "RCTS_0.2.4.tar.gz", "_fileid": "d241e614425248a47b8df3f17088679ed4d57c54d8a9be90d0194018514e21ed", "_filesize": 701372, "_sha256": "d241e614425248a47b8df3f17088679ed4d57c54d8a9be90d0194018514e21ed", "_created": "2026-05-14T07:44:32.000Z", "_published": 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"4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/RCTS.html", "manual.pdf" ], "_homeurl": "https://github.com/eh-in-r/rcts", "_realowner": "eh-in-r", "_cranurl": true, "_releases": [ { "version": "0.2.2", "date": "2022-06-20" }, { "version": "0.2.3", "date": "2022-09-14" }, { "version": "0.2.4", "date": "2023-05-18" } ], "_exports": [ "adapt_pic_with_sigma2maxmodel", "add_configuration", "add_metrics", "add_pic", "calculate_best_config", "calculate_error_term", "calculate_lambda_group", "calculate_PIC", "calculate_sigma2", "calculate_VCsquared", "check_stopping_rules", "create_data_dgp2", "define_C_candidates", "define_configurations", "define_kg_candidates", "define_number_subsets", "estimate_algorithm", "estimate_beta", "estimate_factor_group", "fill_rc", "fill_rcj", "get_best_configuration", "get_convergence_speed", "get_final_estimation", "initialise_beta", "initialise_clustering", "initialise_commonfactorstructure_macropca", "initialise_df_pic", "initialise_df_results", "initialise_rc", "initialise_rcj", "iterate", "make_subsamples", "parallel_algorithm", "plot_VCsquared", "update_g" ], "_datasets": [ { "name": "df_results_example", "title": "An example for df_results. This dataframe contains the estimators for each configuration.", "object": "df_results_example", "class": [ "data.frame" ], "fields": [ "S", "k_common", "k1", "k2", "k3", "g", "beta_est", "factor_group", "lambda_group", "comfactor", "lambda" ], "rows": 4, "table": false, "tojson": true }, { "name": "factor_group_true_dgp3", "title": "factor_group_true_dgp3 contains the values of the true group factors on which Y_dgp3 is based", "object": "factor_group_true_dgp3", "class": [ "list" ], "fields": [], "table": true, "tojson": true }, { "name": "g_true_dgp3", "title": "g_true_dgp3 contains the true group memberships of the elements of Y_dgp3", "object": "g_true_dgp3", "class": [ "numeric" ], "fields": [], "table": false, "tojson": true }, { "name": "lambda_group_true_dgp3", "title": "lambda_group_true_dgp3 contains the values of the loadings to the group factors on which Y_dgp3 is based", "object": "lambda_group_true_dgp3", "class": [ "data.frame" ], "fields": [ "X1", "X2", "X3", "group", "id" ], "rows": 300, "table": true, "tojson": true }, { "name": "X_dgp3", "title": "The dataset X_dgp3 contains the values of the 3 observable variables on which Y_dgp3 is based.", "object": "X_dgp3", "class": [ "array" ], "fields": [], "table": false, "tojson": true }, { "name": "Y_dgp3", "title": "Y_dgp3 contains a simulated dataset for DGP 3.", "object": "Y_dgp3", "class": [ "matrix", "array" ], "fields": {}, "rows": 300, "table": true, "tojson": true } ], "_help": [ { "page": "adapt_pic_with_sigma2maxmodel", "title": "Adapts the object that contains PIC for all candidate C's and all subsamples with sigma2_max_model.", "topics": [ "adapt_pic_with_sigma2maxmodel" ] }, { "page": "adapt_X_estimating_less_variables", "title": "When running the algorithm with a different number of observable variables then the number that is available, reformat X. (Mainly used for testing)", "topics": [ "adapt_X_estimating_less_variables" ] }, { "page": "add_configuration", "title": "Adds the current configuration (number of groups and factors) to df_results.", "topics": [ "add_configuration" ] }, { "page": "add_metrics", "title": "Adds several metrics to df_results.", "topics": [ "add_metrics" ] }, { "page": "add_pic", "title": "Fills in df_pic: adds a row with the calculated PIC for the current configuration.", "topics": [ "add_pic" ] }, { "page": "add_pic_parallel", "title": "Calculates the PIC for the current configuration.", "topics": [ "add_pic_parallel" ] }, { "page": "beta_true_heterogroups", "title": "Helpfunction in create_true_beta() for the option beta_true_heterogeneous_groups. (This is the default option.)", "topics": [ "beta_true_heterogroups" ] }, { "page": "calculate_best_config", "title": "Function that returns for each candidate C the best number of groups and factors, based on the PIC.", "topics": [ "calculate_best_config" ] }, { "page": "calculate_error_term", "title": "Calculates the error term Y - X*beta_est - LF - LgFg.", "topics": [ "calculate_error_term" ] }, { "page": "calculate_errors_virtual_groups", "title": "Helpfunction for update_g(). Calculates the errors for one of the possible groups time series can be placed in.", "topics": [ "calculate_errors_virtual_groups" ] }, { "page": "calculate_FL_group_estimated", "title": "Returns the estimated groupfactorstructure.", "topics": [ "calculate_FL_group_estimated" ] }, { "page": "calculate_FL_group_true", "title": "Calculate the true groupfactorstructure.", "topics": [ "calculate_FL_group_true" ] }, { "page": "calculate_lambda", "title": "calculates factor loadings of common factors", "topics": [ "calculate_lambda" ] }, { "page": "calculate_lambda_group", "title": "calculates factor loadings of groupfactors", "topics": [ "calculate_lambda_group" ] }, { "page": "calculate_lgfg", "title": "Calculates the group factor structure: the matrix product of the group factors and their loadings.", "topics": [ "calculate_lgfg" ] }, { "page": "calculate_obj_for_g", "title": "Calculates objective function for individual i and group k in order to estimate group membership.", "topics": [ "calculate_obj_for_g" ] }, { "page": "calculate_PIC", "title": "Function to determine PIC (panel information criterium)", "topics": [ "calculate_PIC" ] }, { "page": "calculate_PIC_term1", "title": "Function to calculate the first term of PIC (panel information criterium)", "topics": [ "calculate_PIC_term1" ] }, { "page": "calculate_sigma2", "title": "Calculates sum of squared errors, divided by NT", "topics": [ "calculate_sigma2" ] }, { "page": "calculate_sigma2maxmodel", "title": "Calculates sigma2maxmodel", "topics": [ "calculate_sigma2maxmodel" ] }, { "page": "calculate_TN_factor", "title": "Helpfunction. Calculates part of the 4th term of the PIC.", "topics": [ "calculate_TN_factor" ] }, { "page": "calculate_VCsquared", "title": "Calculates VC², to determine the stability of the found number of groups and factors over the subsamples.", "topics": [ "calculate_VCsquared" ] }, { "page": "calculate_virtual_factor_and_lambda_group", "title": "Helpfunction used in update_g()", "topics": [ "calculate_virtual_factor_and_lambda_group" ] }, { "page": "calculate_W", "title": "Calculates W = Y - X*beta_est. It is used in the initialization step of the algorithm, to initialise the factorstructures.", "topics": [ "calculate_W" ] }, { "page": "calculate_XB_estimated", "title": "Calculates (the estimated value of) the matrix X*beta_est.", "topics": [ "calculate_XB_estimated" ] }, { "page": "calculate_XB_true", "title": "Calculates the product of X*beta_true .", "topics": [ "calculate_XB_true" ] }, { "page": "calculate_Z_common", "title": "Calculates Z = Y - X*beta_est - LgFg. It is used in the estimate of the common factorstructure.", "topics": [ "calculate_Z_common" ] }, { "page": "calculate_Z_group", "title": "Calculates Z = Y - X*beta_est - LF. It is used to estimate the groupfactorstructure.", "topics": [ "calculate_Z_group" ] }, { "page": "check_stopping_rules", "title": "Checks the rules for stopping the algorithm, based on its convergence speed.", "topics": [ "check_stopping_rules" ] }, { "page": "clustering_with_robust_distances", "title": "Function that puts individuals in a separate \"class zero\", when their distance to all possible groups is bigger then a certain threshold.", "topics": [ "clustering_with_robust_distances" ] }, { "page": "create_covMat_crosssectional_dependence", "title": "Function used in generating simulated data with non normal errors.", "topics": [ "create_covMat_crosssectional_dependence" ] }, { "page": "create_data_dgp2", "title": "Creates an instance of DGP 2, as defined in Boudt and Heyndels (2022).", "topics": [ "create_data_dgp2" ] }, { "page": "create_true_beta", "title": "Creates beta_true, which contains the true values of beta (= the coefficients of X)", "topics": [ "create_true_beta" ] }, { "page": "define_C_candidates", "title": "Defines the candidate values for C.", "topics": [ "define_C_candidates" ] }, { "page": "define_configurations", "title": "Constructs dataframe where the rows contains all configurations that are included and for which the estimators will be estimated.", "topics": [ "define_configurations" ] }, { "page": "define_kg_candidates", "title": "Defines the set of combinations of group specific factors.", "topics": [ "define_kg_candidates" ] }, { "page": "define_number_subsets", "title": "Returns a vector with the indices of the subsets. Must start with zero.", "topics": [ "define_number_subsets" ] }, { "page": "define_object_for_initial_clustering_macropca", "title": "Defines the object that will be used to define a initial clustering.", "topics": [ "define_object_for_initial_clustering_macropca" ] }, { "page": "define_rho_parameters", "title": "Determines parameters of rho-function.", "topics": [ "define_rho_parameters" ] }, { "page": "determine_beta", "title": "Helpfunction in estimate_beta() for estimating beta_est.", "topics": [ "determine_beta" ] }, { "page": "determine_robust_lambda", "title": "Help-function for return_robust_lambdaobject().", "topics": [ "determine_robust_lambda" ] }, { "page": "df_results_example", "title": "An example for df_results. This dataframe contains the estimators for each configuration.", "topics": [ "df_results_example" ] }, { "page": "do_we_estimate_common_factors", "title": "Helpfunction to shorten code: are common factors being estimated.", "topics": [ "do_we_estimate_common_factors" ] }, { "page": "do_we_estimate_group_factors", "title": "Helpfunction to shorten code: are group factors being estimated.", "topics": [ "do_we_estimate_group_factors" ] }, { "page": "estimate_algorithm", "title": "This function is a wrapper around the initialization and the estimation part of the algorithm, for one configuration. It is only used for the serialized algorithm.", "topics": [ "estimate_algorithm" ] }, { "page": "estimate_beta", "title": "Estimates beta.", "topics": [ "estimate_beta" ] }, { "page": "estimate_factor", "title": "Estimates common factor(s) F.", "topics": [ "estimate_factor" ] }, { "page": "estimate_factor_group", "title": "Estimates group factors Fg.", "topics": [ "estimate_factor_group" ] }, { "page": "evade_crashes_macropca", "title": "Solves a very specific issue with MacroPCA.", "topics": [ "evade_crashes_macropca" ] }, { "page": "evade_floating_point_errors", "title": "Function to evade floating point errors.", "topics": [ "evade_floating_point_errors" ] }, { "page": "factor_group_true_dgp3", "title": "factor_group_true_dgp3 contains the values of the true group factors on which Y_dgp3 is based", "topics": [ "factor_group_true_dgp3" ] }, { "page": "fill_rc", "title": "Fills in the optimized number of common factors for each C.", "topics": [ "fill_rc" ] }, { "page": "fill_rcj", "title": "Fills in the optimized number of groups and group specific factors for each C.", "topics": [ "fill_rcj" ] }, { "page": "final_estimations_filter_kg", "title": "Filters dataframe on the requested group specific factors configuration.", "topics": [ "final_estimations_filter_kg" ] }, { "page": "g_true_dgp3", "title": "g_true_dgp3 contains the true group memberships of the elements of Y_dgp3", "topics": [ "g_true_dgp3" ] }, { "page": "generate_grouped_factorstructure", "title": "Generates the true groupfactorstructure, to use in simulations.", "topics": [ "generate_grouped_factorstructure" ] }, { "page": "generate_Y", "title": "Generate panel data Y for simulations.", "topics": [ "generate_Y" ] }, { "page": "get_best_configuration", "title": "Finds the first stable interval after the first unstable point. It then defines the value for C for the begin, middle and end of this interval.", "topics": [ "get_best_configuration" ] }, { "page": "get_convergence_speed", "title": "Defines the convergence speed.", "topics": [ "get_convergence_speed" ] }, { "page": "get_final_estimation", "title": "Function that returns the final clustering, based on the estimated number of groups and common and group specific factors.", "topics": [ "get_final_estimation" ] }, { "page": "grid_add_variables", "title": "Function which is used to have a dataframe (called \"grid\") with data (individualindex, timeindex, XT and LF) available.", "topics": [ "grid_add_variables" ] }, { "page": "handle_macropca_errors", "title": "Helpfunction in robustpca().", "topics": [ "handle_macropca_errors" ] }, { "page": "handleNA", "title": "Function with as input a dataframe. (this will be \"Y\" or \"to_divide\") It filters out rows with NA.", "topics": [ "handleNA" ] }, { "page": "handleNA_LG", "title": "Removes NA's in LG (in function calculate_virtual_factor_and_lambda_group() )", "topics": [ "handleNA_LG" ] }, { "page": "initialise_beta", "title": "Initialisation of estimation of beta (the coefficients with the observable variables)", "topics": [ "initialise_beta" ] }, { "page": "initialise_clustering", "title": "Function that clusters time series in a dataframe with kmeans.", "topics": [ "initialise_clustering" ] }, { "page": "initialise_commonfactorstructure_macropca", "title": "Initialises the estimation of the common factors and their loadings.", "topics": [ "initialise_commonfactorstructure_macropca" ] }, { "page": "initialise_df_pic", "title": "Initialises a dataframe which will contain the PIC for each configuration and for each value of C.", "topics": [ "initialise_df_pic" ] }, { "page": "initialise_df_results", "title": "Initialises a dataframe that will contain an overview of metrics for each estimated configuration (for example adjusted randindex).", "topics": [ "initialise_df_results" ] }, { "page": "initialise_rc", "title": "Initialises rc.", "topics": [ "initialise_rc" ] }, { "page": "initialise_rcj", "title": "Initialises rcj.", "topics": [ "initialise_rcj" ] }, { "page": "initialise_X", "title": "Creates X (the observable variables) to use in simulations.", "topics": [ "initialise_X" ] }, { "page": "iterate", "title": "Wrapper around estimate_beta(), update_g(), and estimating the factorstructures.", "topics": [ "iterate" ] }, { "page": "kg_candidates_expand", "title": "Function that returns the set of combinations of groupfactors for which the algorithm needs to run.", "topics": [ "kg_candidates_expand" ] }, { "page": "lambda_group_true_dgp3", "title": "lambda_group_true_dgp3 contains the values of the loadings to the group factors on which Y_dgp3 is based", "topics": [ "lambda_group_true_dgp3" ] }, { "page": "LMROB", "title": "Wrapper around lmrob.", "topics": [ "LMROB" ] }, { "page": "make_df_pic_parallel", "title": "Makes a dataframe with the PIC for each configuration and each candidate C.", "topics": [ "make_df_pic_parallel" ] }, { "page": "make_df_results_parallel", "title": "Makes a dataframe with information on each configuration.", "topics": [ "make_df_results_parallel" ] }, { "page": "make_subsamples", "title": "Selects a subsample of the time series, and of the length of the time series. Based on this it returns a list with a subsample of Y, the corresponding subsample of X and of the true group membership and factorstructures if applicable.", "topics": [ "make_subsamples" ] }, { "page": "matrixnorm", "title": "Function to calculate the norm of a matrix.", "topics": [ "matrixnorm" ] }, { "page": "OF_vectorized_helpfunction3", "title": "Helpfunction in OF_vectorized3()", "topics": [ "OF_vectorized_helpfunction3" ] }, { "page": "OF_vectorized3", "title": "Calculates objective function for the classical algorithm: used in iterate() and in local_search.", "topics": [ "OF_vectorized3" ] }, { "page": "parallel_algorithm", "title": "Wrapper of the loop over the subsets which in turn use the parallelised algorithm.", "topics": [ "parallel_algorithm" ] }, { "page": "plot_VCsquared", "title": "Plots expression(VC^2) along with the corresponding number of groups (orange), common factors (darkblue) and group factors of the first group (lightblue).", "topics": [ "plot_VCsquared" ] }, { "page": "prepare_for_robpca", "title": "Helpfunction: prepares object to perform robust PCA on.", "topics": [ "prepare_for_robpca" ] }, { "page": "RCTS", "title": "RCTS", "topics": [ "RCTS" ] }, { "page": "reassign_if_empty_groups", "title": "Randomly reassign individual(s) if there are empty groups. This can happen if the total number of time series is low compared to the number of desired groups.", "topics": [ "reassign_if_empty_groups" ] }, { "page": "restructure_X_to_order_slowN_fastT", "title": "Restructures X (which is an 3D-array of dimensions (N,T,p) to a 2D-matrix of dimension (NxT,p).", "topics": [ "restructure_X_to_order_slowN_fastT" ] }, { "page": "return_robust_lambdaobject", "title": "Calculates robust loadings", "topics": [ "return_robust_lambdaobject" ] }, { "page": "robustpca", "title": "Function that uses robust PCA and estimates robust factors and loadings.", "topics": [ "robustpca" ] }, { "page": "run_config", "title": "Wrapper around the non-parallel algorithm, to estimate beta, group membership and the factorstructures.", "topics": [ "run_config" ] }, { "page": "scaling_X", "title": "Scaling of X.", "topics": [ "scaling_X" ] }, { "page": "solveFG", "title": "Helpfunction in update_g(), to calculate solve(FG x t(FG)) x FG", "topics": [ "solveFG" ] }, { "page": "tabulate_potential_C", "title": "Shows the configurations for potential C's of the first stable interval (beginpoint, middlepoint and endpoint)", "topics": [ "tabulate_potential_C" ] }, { "page": "update_g", "title": "Function that estimates group membership.", "topics": [ "update_g" ] }, { "page": "X_dgp3", "title": "The dataset X_dgp3 contains the values of the 3 observable variables on which Y_dgp3 is based.", "topics": [ "X_dgp3" ] }, { "page": "Y_dgp3", "title": "Y_dgp3 contains a simulated dataset for DGP 3.", "topics": [ "Y_dgp3" ] } ], "_rundeps": [ "cellWise", "cli", "cpp11", "DEoptimR", "dplyr", "farver", "generics", "ggplot2", "glue", "gridExtra", "gtable", "isoband", "labeling", "lattice", "lifecycle", "magrittr", "matrixStats", "mvtnorm", "ncvreg", "pcaPP", "pillar", "pkgconfig", "plyr", "purrr", "R6", "rbibutils", "RColorBrewer", "Rcpp", "RcppArmadillo", "Rdpack", "reshape2", "rlang", "robustbase", "rrcov", "S7", "scales", "shape", "stringi", "stringr", "svd", "tibble", "tidyr", "tidyselect", "utf8", "vctrs", "viridisLite", "withr" ], "_score": 2, "_indexed": true, "_nocasepkg": "rcts", "_universes": [ "eh-in-r" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.2.4", "date": "2026-05-14T07:46:55.000Z", "distro": "noble", "commit": "f6a1ba4cdddd14d5faae291c9e6120247752aac9", "fileid": "d0d643b099e693955506dfb156cdf8a5e0019c9c95511342db50b0ff9d6ba822", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/eh-in-r/actions/runs/25848254548" }, { "r": "4.6.0", "os": "linux", "version": "0.2.4", "date": "2026-05-14T07:46:51.000Z", "distro": "noble", "commit": "f6a1ba4cdddd14d5faae291c9e6120247752aac9", "fileid": "d2ad18573cdec6ad389c5a264a5b84c676b6698eef3522965d93a14aac96f9fc", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/eh-in-r/actions/runs/25848254548" }, { "r": "4.5.3", "os": "mac", "version": "0.2.4", "date": "2026-05-14T07:46:51.000Z", "commit": "f6a1ba4cdddd14d5faae291c9e6120247752aac9", "fileid": "be06b006d6538789d9917f055b7b3af143f36e348007444fcb54b80e8197a5ed", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/eh-in-r/actions/runs/25848254548" }, { "r": "4.6.0", "os": "mac", "version": "0.2.4", "date": "2026-05-14T07:47:44.000Z", "commit": "f6a1ba4cdddd14d5faae291c9e6120247752aac9", "fileid": "3a97ca8a3246712fd512a55613f7bcd77da6b50762e913047f42971e46bc0610", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/eh-in-r/actions/runs/25848254548" }, { "r": "4.7.0", "os": "win", "version": "0.2.4", "date": "2026-05-14T07:45:47.000Z", "commit": "f6a1ba4cdddd14d5faae291c9e6120247752aac9", "fileid": "7a4d8de86c388774ed732a5cfa22775f9cb435f343ce90767daf71ce495bd46c", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/eh-in-r/actions/runs/25848254548" }, { "r": "4.5.3", "os": "win", "version": "0.2.4", "date": "2026-05-14T07:45:53.000Z", "commit": "f6a1ba4cdddd14d5faae291c9e6120247752aac9", "fileid": "8d534022a45f76761c55f39803cd28e99531b46b154a7864feb0df8521b20adc", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/eh-in-r/actions/runs/25848254548" }, { "r": "4.6.0", "os": "win", "version": "0.2.4", "date": "2026-05-14T07:45:54.000Z", "commit": "f6a1ba4cdddd14d5faae291c9e6120247752aac9", "fileid": "6fe4108181d1497b459b8de236b53b0300d7861a3262c9ccd1ba883b6d701f6c", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/eh-in-r/actions/runs/25848254548" }, { "r": "4.6.0", "os": "wasm", "version": "0.2.4", "date": "2026-05-22T09:11:08.000Z", "commit": "f6a1ba4cdddd14d5faae291c9e6120247752aac9", "fileid": "08041c4b1baffc5dc6d509085abbf005a7138fba6139696d04175e2f0d8322f0", "status": "success", "buildurl": "https://github.com/r-universe/eh-in-r/actions/runs/25848254548" } ] }