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  "Package": "lingamr",
  "Title": "LiNGAM Algorithms for Causal Discovery",
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  "Authors@R": "c(\nperson(\"Osamu\", \"Morimoto\", , \"galactic.supermarket@gmail.com\", role = c(\"aut\", \"cre\", \"cph\")),\nperson(\"T.\", \"Ikeuchi\", role = \"cph\"),\nperson(\"G.\", \"Haraoka\", role = \"cph\"),\nperson(\"M.\", \"Ide\", role = \"cph\"),\nperson(\"W.\", \"Kurebayashi\", role = \"cph\"),\nperson(\"S.\", \"Shimizu\", role = \"cph\"))",
  "Description": "R implementation of LiNGAM (Linear Non-Gaussian Acyclic\nModel) algorithms for causal discovery, following Shimizu et\nal. (2011) <https://www.jmlr.org/papers/v12/shimizu11a.html>.\nBased on the 'Python' implementation by Ikeuchi et al. (2023)\n<https://github.com/cdt15/lingam>. The VAR-LiNGAM residual\ndiagnostics are inspired by the 'VARLiNGAM' R code of Moneta et\nal.\n<https://sites.google.com/site/dorisentner/publications/VARLiNGAM>.",
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  "Date/Publication": "2026-08-02 12:24:37 UTC",
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    "author": "Osamu Morimoto <galactic.supermarket@gmail.com>",
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    "message": "Parallelize imputation bootstrap and relax exact-rounding constraints\n\n- Add parallel/n_cores to bootstrap_with_imputation(), following the\n  PSOCK + clusterSetRNGStream pattern of the other bootstrap functions;\n  sequential runs remain bit-identical for a given seed (~2x with 4\n  cores at n_sampling=40)\n- Solve HighDimDirectLiNGAM's conditional regressions via Cholesky\n  (chol2inv) with a pinv() fallback for singular Gram submatrices\n- Score LiM local-search candidates with glm.fit()/lm.fit() instead of\n  the formula interface (verified bit-identical)\n- Compute lim_h()'s matrix power by binary exponentiation for d > 16;\n  d <= 16 keeps the sequential product bit-identical to the Python source\n- Regenerate roxygen docs, including Rd files for the internal helpers\n  added by the previous commit\n",
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  "_exports": [
    "as_bootstrap_result",
    "bootstrap_with_imputation",
    "check_var_stationarity",
    "check_varma_stationarity",
    "estimate_all_total_effects",
    "estimate_total_effect",
    "estimate_total_effect_parce",
    "estimate_total_effect_rcd",
    "estimate_var_total_effect",
    "estimate_varma_total_effect",
    "evaluate_model_fit",
    "generate_camuv_sample",
    "generate_lim_sample",
    "generate_lingam_hard_sample",
    "generate_lingam_large_sample",
    "generate_lingam_paradox_data",
    "generate_lingam_sample_10",
    "generate_lingam_sample_6",
    "generate_multi_group_sample",
    "generate_parce_sample",
    "generate_rcd_sample",
    "generate_resit_sample",
    "generate_varlingam_sample",
    "generate_varmalingam_sample",
    "get_adjacency_matrix_summary",
    "get_causal_direction_counts",
    "get_causal_order_stability",
    "get_directed_acyclic_graph_counts",
    "get_error_independence_p_values",
    "get_error_independence_p_values_parce",
    "get_error_independence_p_values_rcd",
    "get_group_result",
    "get_paths",
    "get_probabilities",
    "get_total_causal_effects",
    "get_var_paths",
    "get_var_probabilities",
    "get_varma_paths",
    "get_varma_probabilities",
    "glance",
    "lingam_camuv",
    "lingam_direct",
    "lingam_direct_bootstrap",
    "lingam_high_dim",
    "lingam_lim",
    "lingam_multi_group",
    "lingam_multi_group_bootstrap",
    "lingam_parce",
    "lingam_parce_bootstrap",
    "lingam_rcd",
    "lingam_rcd_bootstrap",
    "lingam_resit",
    "lingam_resit_bootstrap",
    "lingam_var",
    "lingam_var_bootstrap",
    "lingam_varma",
    "lingam_varma_bootstrap",
    "make_prior_knowledge",
    "plot_adjacency",
    "plot_bootstrap_probabilities",
    "plot_residual_qq",
    "plot_varlingam_residual_qq",
    "plot_varmalingam_residual_qq",
    "summary_lingam",
    "test_residual_normality",
    "test_varlingam_residual_normality",
    "test_varlingam_residual_normality_all",
    "test_varmalingam_residual_normality",
    "test_varmalingam_residual_normality_all",
    "tidy"
  ],
  "_help": [
    {
      "page": "as_bootstrap_result",
      "title": "Collapse an ImputationBootstrapResult into a BootstrapResult",
      "topics": [
        "as_bootstrap_result"
      ]
    },
    {
      "page": "autoplot.CAMUVResult",
      "title": "Plot the causal graph of a CAMUVResult with ggplot2",
      "topics": [
        "autoplot.CAMUVResult"
      ]
    },
    {
      "page": "autoplot.LiMResult",
      "title": "Plot the causal graph of a LiMResult with ggplot2",
      "topics": [
        "autoplot.LiMResult"
      ]
    },
    {
      "page": "autoplot.LingamResult",
      "title": "Plot the causal graph of a LingamResult with ggplot2",
      "topics": [
        "autoplot.LingamResult"
      ]
    },
    {
      "page": "autoplot.MultiGroupLingamResult",
      "title": "Plot one group of a MultiGroupLingamResult with ggplot2",
      "topics": [
        "autoplot.MultiGroupLingamResult"
      ]
    },
    {
      "page": "autoplot.ParceLingamResult",
      "title": "Plot the causal graph of a ParceLingamResult with ggplot2",
      "topics": [
        "autoplot.ParceLingamResult"
      ]
    },
    {
      "page": "autoplot.RCDResult",
      "title": "Plot the causal graph of an RCDResult with ggplot2",
      "topics": [
        "autoplot.RCDResult"
      ]
    },
    {
      "page": "autoplot.ResitResult",
      "title": "Plot the causal graph of a ResitResult with ggplot2",
      "topics": [
        "autoplot.ResitResult"
      ]
    },
    {
      "page": "bootstrap_with_imputation",
      "title": "Bootstrap with Multiple Imputation for Direct LiNGAM",
      "topics": [
        "bootstrap_with_imputation"
      ]
    },
    {
      "page": "check_var_stationarity",
      "title": "Check the stationarity of a fitted VAR-LiNGAM model",
      "topics": [
        "check_var_stationarity"
      ]
    },
    {
      "page": "check_varma_stationarity",
      "title": "Check the stationarity and invertibility of a fitted VARMA-LiNGAM model",
      "topics": [
        "check_varma_stationarity"
      ]
    },
    {
      "page": "estimate_all_total_effects",
      "title": "Estimate the total causal effects between all variables at once",
      "topics": [
        "estimate_all_total_effects"
      ]
    },
    {
      "page": "estimate_total_effect",
      "title": "Estimate the total causal effect between two specified variables",
      "topics": [
        "estimate_total_effect"
      ]
    },
    {
      "page": "estimate_total_effect_parce",
      "title": "Estimate the total causal effect between two variables (ParceLiNGAM)",
      "topics": [
        "estimate_total_effect_parce"
      ]
    },
    {
      "page": "estimate_total_effect_rcd",
      "title": "Estimate the total causal effect between two variables (RCD)",
      "topics": [
        "estimate_total_effect_rcd"
      ]
    },
    {
      "page": "estimate_var_total_effect",
      "title": "Estimate a total causal effect in a VAR-LiNGAM model",
      "topics": [
        "estimate_var_total_effect"
      ]
    },
    {
      "page": "estimate_varma_total_effect",
      "title": "Estimate a total causal effect in a VARMA-LiNGAM model",
      "topics": [
        "estimate_varma_total_effect"
      ]
    },
    {
      "page": "evaluate_model_fit",
      "title": "Evaluate model fit of an estimated causal graph",
      "topics": [
        "evaluate_model_fit"
      ]
    },
    {
      "page": "generate_camuv_sample",
      "title": "Generate sample data with unobserved variables (for CAM-UV)",
      "topics": [
        "generate_camuv_sample"
      ]
    },
    {
      "page": "generate_lim_sample",
      "title": "Generate sample data for LiM (3 mixed variables)",
      "topics": [
        "generate_lim_sample"
      ]
    },
    {
      "page": "generate_lingam_hard_sample",
      "title": "Generate a challenging sample data for Direct LiNGAM",
      "topics": [
        "generate_lingam_hard_sample"
      ]
    },
    {
      "page": "generate_lingam_large_sample",
      "title": "Generate large-scale sample data to benchmark Direct LiNGAM scalability",
      "topics": [
        "generate_lingam_large_sample"
      ]
    },
    {
      "page": "generate_lingam_paradox_data",
      "title": "Generate Paradoxical Data Where DirectLiNGAM Struggles",
      "topics": [
        "generate_lingam_paradox_data"
      ]
    },
    {
      "page": "generate_lingam_sample_10",
      "title": "Generate 10-variable sample data for Direct LiNGAM",
      "topics": [
        "generate_lingam_sample_10"
      ]
    },
    {
      "page": "generate_lingam_sample_6",
      "title": "Generate sample data for Direct LiNGAM (6 variables)",
      "topics": [
        "generate_lingam_sample_6"
      ]
    },
    {
      "page": "generate_multi_group_sample",
      "title": "Generate sample data for Multi-Group Direct LiNGAM (2 groups, 6 variables)",
      "topics": [
        "generate_multi_group_sample"
      ]
    },
    {
      "page": "generate_parce_sample",
      "title": "Generate sample data with a latent confounder (for BottomUpParceLiNGAM)",
      "topics": [
        "generate_parce_sample"
      ]
    },
    {
      "page": "generate_rcd_sample",
      "title": "Generate sample data with a latent confounder (for RCD)",
      "topics": [
        "generate_rcd_sample"
      ]
    },
    {
      "page": "generate_resit_sample",
      "title": "Generate sample data from a nonlinear additive noise model (for RESIT)",
      "topics": [
        "generate_resit_sample"
      ]
    },
    {
      "page": "generate_varlingam_sample",
      "title": "Generate sample data from a VAR-LiNGAM model",
      "topics": [
        "generate_varlingam_sample"
      ]
    },
    {
      "page": "generate_varmalingam_sample",
      "title": "Generate sample data from a VARMA-LiNGAM model",
      "topics": [
        "generate_varmalingam_sample"
      ]
    },
    {
      "page": "get_adjacency_matrix_summary",
      "title": "Create an adjacency matrix of representative causal-effect values from bootstrap results",
      "topics": [
        "get_adjacency_matrix_summary"
      ]
    },
    {
      "page": "get_causal_direction_counts",
      "title": "Get counts, proportions, and causal effects of causal directions",
      "topics": [
        "get_causal_direction_counts"
      ]
    },
    {
      "page": "get_causal_order_stability",
      "title": "Evaluate the stability of the causal order from bootstrap",
      "topics": [
        "get_causal_order_stability"
      ]
    },
    {
      "page": "get_directed_acyclic_graph_counts",
      "title": "Get DAG counts",
      "topics": [
        "get_directed_acyclic_graph_counts"
      ]
    },
    {
      "page": "get_error_independence_p_values",
      "title": "Compute p-values for the independence test of the errors",
      "topics": [
        "get_error_independence_p_values"
      ]
    },
    {
      "page": "get_error_independence_p_values_parce",
      "title": "Compute p-values for the independence of ParceLiNGAM residuals (HSIC-based)",
      "topics": [
        "get_error_independence_p_values_parce"
      ]
    },
    {
      "page": "get_error_independence_p_values_rcd",
      "title": "Compute p-values for the independence of RCD residuals (HSIC-based)",
      "topics": [
        "get_error_independence_p_values_rcd"
      ]
    },
    {
      "page": "get_group_result",
      "title": "Extract a single group's result from a MultiGroupLingamResult",
      "topics": [
        "get_group_result"
      ]
    },
    {
      "page": "get_paths",
      "title": "Get all paths between two specified variables and their bootstrap probabilities",
      "topics": [
        "get_paths"
      ]
    },
    {
      "page": "get_probabilities",
      "title": "Get bootstrap probabilities",
      "topics": [
        "get_probabilities"
      ]
    },
    {
      "page": "get_total_causal_effects",
      "title": "Get a list of total causal effects",
      "topics": [
        "get_total_causal_effects"
      ]
    },
    {
      "page": "get_var_paths",
      "title": "Enumerate bootstrap paths between two variables in a VAR-LiNGAM model",
      "topics": [
        "get_var_paths"
      ]
    },
    {
      "page": "get_var_probabilities",
      "title": "Bootstrap probabilities for a VAR-LiNGAM model",
      "topics": [
        "get_var_probabilities"
      ]
    },
    {
      "page": "get_varma_paths",
      "title": "Enumerate bootstrap paths between two variables in a VARMA-LiNGAM model",
      "topics": [
        "get_varma_paths"
      ]
    },
    {
      "page": "get_varma_probabilities",
      "title": "Bootstrap probabilities for a VARMA-LiNGAM model",
      "topics": [
        "get_varma_probabilities"
      ]
    },
    {
      "page": "glance.CAMUVResult",
      "title": "Get a one-row summary of a CAMUVResult",
      "topics": [
        "glance.CAMUVResult"
      ]
    },
    {
      "page": "glance.LiMResult",
      "title": "Get a one-row summary of a LiMResult",
      "topics": [
        "glance.LiMResult"
      ]
    },
    {
      "page": "glance.LingamResult",
      "title": "Get a one-row summary of a LingamResult",
      "topics": [
        "glance.LingamResult"
      ]
    },
    {
      "page": "glance.MultiGroupLingamResult",
      "title": "Get a one-row summary of a MultiGroupLingamResult",
      "topics": [
        "glance.MultiGroupLingamResult"
      ]
    },
    {
      "page": "glance.ParceLingamResult",
      "title": "Get a one-row summary of a ParceLingamResult",
      "topics": [
        "glance.ParceLingamResult"
      ]
    },
    {
      "page": "glance.RCDResult",
      "title": "Get a one-row summary of an RCDResult",
      "topics": [
        "glance.RCDResult"
      ]
    },
    {
      "page": "glance.ResitResult",
      "title": "Get a one-row summary of a ResitResult",
      "topics": [
        "glance.ResitResult"
      ]
    },
    {
      "page": "lingam_camuv",
      "title": "CAM-UV (Causal Additive Models with Unobserved Variables)",
      "topics": [
        "lingam_camuv"
      ]
    },
    {
      "page": "lingam_direct",
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