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  "Package": "netOP",
  "Title": "Network Data Operations and Overlapping Partitions Based Methods\nfor Large Networks",
  "Version": "0.1.2",
  "Authors@R": "person(\"Sayan\", \"Chakrabarty\", , \"sayanc@umich.edu\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-2372-2076\"))",
  "Description": "Implements methods for generating, embedding, and\nclustering random networks and for estimating and selecting\nstatistical network models. Provides SONNET (Subsampling ON\nNETwork), a scalable subsampling-based divide-and-conquer\nmethod for community detection described by Chakrabarty,\nSengupta and Chen (2025) <doi:10.5705/ss.202022.0108>, and\nNETCROP (NETwork CRoss-validation using Overlapping\nPartitions), an overlapping-partition framework for network\ncross-validation, model selection, and regularization tuning\ndescribed by Chakrabarty, Sengupta and Chen (2026)\n<doi:10.48550/arXiv.2504.06903>. Also includes spectral and\nlatent-space methods, loss functions, and helper functions for\nstatistical analysis of network data.",
  "License": "GPL (>= 2)",
  "URL": "https://github.com/sayan-ch/netOP, https://sayan-ch.github.io",
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  "Copyright": "2026 Sayan Chakrabarty",
  "Repository": "https://sayan-ch.r-universe.dev",
  "Date/Publication": "2026-09-16 14:52:51 UTC",
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  "Author": "Sayan Chakrabarty [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-2372-2076>)",
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    "colSums",
    "connected_components",
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    "dkest_tune_regularizer",
    "ecv_stability_blockmodel",
    "ecv_stability_rdpg",
    "eig_decomp",
    "estimate_dcbm",
    "estimate_dcbm_P_hat",
    "estimate_sbm",
    "estimate_sbm_P_hat",
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    "generate_community_labels",
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    "generate_degree_parameters",
    "generate_er",
    "generate_inverse_beta_degree_parameters",
    "generate_latent_positions",
    "generate_lsm",
    "generate_lsm_alpha",
    "generate_lsm_positions",
    "generate_rdpg",
    "generate_sbm",
    "generate_truncated_normal",
    "get_generator_parameters",
    "graph_laplacian",
    "hardmax",
    "hardmin",
    "label_match_brute_force",
    "label_match_greedy",
    "largest_connected_component",
    "lsm_pgd",
    "mae",
    "matrix_density",
    "mean",
    "measure_peak_ram",
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    "mse",
    "mult_reg_sonnet",
    "mult_reg_spectral_cluster",
    "ncv_stability_blockmodel",
    "netcrop_blockmodel",
    "netcrop_lsm",
    "netcrop_param_select",
    "netcrop_rdpg",
    "netcrop_tune_regularizer",
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    "normalize_lsm_positions",
    "oracle_plotter",
    "outer_add",
    "pair_hamming_loss",
    "pair_nmi_loss",
    "procrustes",
    "read_network",
    "rowMeans",
    "rowSums",
    "run_simulations",
    "sae",
    "scale_lsm_to_average_degree",
    "scale_to_average_degree",
    "scale_to_average_degree_naive",
    "shortest_path_distances",
    "sigmoid",
    "singular_decomp",
    "softmax",
    "softmin",
    "softplus",
    "sonnet",
    "sonnet_independent_overlap",
    "sonnet_param_select",
    "sonnet_shared_overlap",
    "spectral_cluster",
    "sse",
    "sum",
    "truncated_svd_reconstruct",
    "uni_mclapply",
    "usvt",
    "which_hardmax",
    "which_hardmin",
    "which_softmax",
    "which_softmin",
    "write_network"
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  "_help": [
    {
      "page": "absolute_error_losses",
      "title": "Compute absolute-error losses",
      "topics": [
        "absolute_error_losses",
        "mae",
        "sae"
      ]
    },
    {
      "page": "adjacency_neighbors",
      "title": "Find adjacent nodes",
      "topics": [
        "adjacency_neighbors",
        "adjacency_weighted_neighbors"
      ]
    },
    {
      "page": "apply_average_degree_scaling",
      "title": "Apply the selected average-degree scaling method",
      "topics": [
        "apply_average_degree_scaling"
      ]
    },
    {
      "page": "ase",
      "title": "Compute an adjacency spectral embedding",
      "topics": [
        "ase"
      ]
    },
    {
      "page": "auc_losses",
      "title": "Compute AUC scores and losses",
      "topics": [
        "auc",
        "auc_as_loss",
        "auc_losses"
      ]
    },
    {
      "page": "binary_deviance_losses",
      "title": "Compute binary deviance losses",
      "topics": [
        "binary_deviance_losses",
        "bin_dev",
        "bin_dev_mean"
      ]
    },
    {
      "page": "clip_probabilities",
      "title": "Clip probability values safely",
      "topics": [
        "clip_probabilities"
      ]
    },
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      "page": "clip_values",
      "title": "Clip numeric values to an interval",
      "topics": [
        "clip_values"
      ]
    },
    {
      "page": "connected_components",
      "title": "Find connected components",
      "topics": [
        "connected_components",
        "largest_connected_component"
      ]
    },
    {
      "page": "dkest_tune_regularizer",
      "title": "Tune a degree-regularized spectral model",
      "topics": [
        "dkest_tune_regularizer"
      ]
    },
    {
      "page": "ecv_stability",
      "title": "Edge cross-validation stability selection",
      "topics": [
        "ecv_stability"
      ]
    },
    {
      "page": "ecv_stability_blockmodel",
      "title": "Select block-model size with edge cross-validation",
      "topics": [
        "ecv_stability_blockmodel"
      ]
    },
    {
      "page": "ecv_stability_rdpg",
      "title": "Select RDPG dimension with edge cross-validation",
      "topics": [
        "ecv_stability_rdpg"
      ]
    },
    {
      "page": "eig_decomp",
      "title": "Compute an eigendecomposition",
      "topics": [
        "eig_decomp"
      ]
    },
    {
      "page": "embedding_estimating",
      "title": "Network embedding, clustering, estimation, and label alignment",
      "topics": [
        "embedding_estimating"
      ]
    },
    {
      "page": "estimate_blockmodel",
      "title": "Estimate stochastic block models",
      "topics": [
        "estimate_dcbm",
        "estimate_sbm"
      ]
    },
    {
      "page": "estimate_blockmodel_P_hat",
      "title": "Reconstruct block-model probability matrices",
      "topics": [
        "estimate_dcbm_P_hat",
        "estimate_sbm_P_hat"
      ]
    },
    {
      "page": "extrema_selectors",
      "title": "Compute hard and soft extrema",
      "topics": [
        "extrema_selectors",
        "hardmax",
        "hardmin",
        "softmax",
        "softmin",
        "which_hardmax",
        "which_hardmin",
        "which_softmax",
        "which_softmin"
      ]
    },
    {
      "page": "generate_adjacency",
      "title": "Sample a network from edge probabilities",
      "topics": [
        "generate_adjacency"
      ]
    },
    {
      "page": "generate_community_labels",
      "title": "Generate or validate community labels",
      "topics": [
        "generate_community_labels"
      ]
    },
    {
      "page": "generate_blockmodel",
      "title": "Generate block-model networks",
      "topics": [
        "generate_dcbm",
        "generate_sbm"
      ]
    },
    {
      "page": "generate_degree_parameters",
      "title": "Generate or validate DCBM degree parameters",
      "topics": [
        "generate_degree_parameters"
      ]
    },
    {
      "page": "generate_er",
      "title": "Generate an Erdos-Renyi network",
      "topics": [
        "generate_er"
      ]
    },
    {
      "page": "generate_inverse_beta_degree_parameters",
      "title": "Generate inverse-beta degree parameters",
      "topics": [
        "generate_inverse_beta_degree_parameters"
      ]
    },
    {
      "page": "generate_latent_positions",
      "title": "Generate or validate latent positions",
      "topics": [
        "generate_latent_positions"
      ]
    },
    {
      "page": "generate_lsm",
      "title": "Generate a latent-space-model network",
      "topics": [
        "generate_lsm"
      ]
    },
    {
      "page": "generate_lsm_alpha",
      "title": "Generate latent-space-model intercepts",
      "topics": [
        "generate_lsm_alpha"
      ]
    },
    {
      "page": "generate_lsm_positions",
      "title": "Generate latent-space-model positions",
      "topics": [
        "generate_lsm_positions"
      ]
    },
    {
      "page": "generate_rdpg",
      "title": "Generate a random dot product graph",
      "topics": [
        "generate_rdpg"
      ]
    },
    {
      "page": "generate_truncated_normal",
      "title": "Generate truncated standard-normal values",
      "topics": [
        "generate_truncated_normal"
      ]
    },
    {
      "page": "get_generator_parameters",
      "title": "Retrieve parameters attached by a network generator",
      "topics": [
        "get_generator_parameters"
      ]
    },
    {
      "page": "graph_laplacian",
      "title": "Construct a graph Laplacian",
      "topics": [
        "graph_laplacian"
      ]
    },
    {
      "page": "is_symmetric_network_matrix",
      "title": "Check whether a network matrix is symmetric",
      "topics": [
        "is_symmetric_network_matrix"
      ]
    },
    {
      "page": "label_match",
      "title": "Match community labels",
      "topics": [
        "label_match_brute_force",
        "label_match_greedy"
      ]
    },
    {
      "page": "lsm_pgd",
      "title": "Fit a latent-space model by projected gradient descent",
      "topics": [
        "lsm_pgd"
      ]
    },
    {
      "page": "matrix_density",
      "title": "Compute matrix density",
      "topics": [
        "matrix_density"
      ]
    },
    {
      "page": "matrix-generics",
      "title": "Matrix summary generics",
      "topics": [
        "colMeans",
        "colSums",
        "diag",
        "matrix-generics",
        "mean",
        "rowMeans",
        "rowSums",
        "sum"
      ]
    },
    {
      "page": "measure_peak_ram",
      "title": "Measure peak memory use",
      "topics": [
        "measure_peak_ram"
      ]
    },
    {
      "page": "modal",
      "title": "Compute the statistical mode",
      "topics": [
        "modal"
      ]
    },
    {
      "page": "mult_reg_sonnet",
      "title": "Fit SONNET over multiple regularizers",
      "topics": [
        "mult_reg_sonnet"
      ]
    },
    {
      "page": "mult_reg_spectral_cluster",
      "title": "Fit spectral clusterings over multiple regularizers",
      "topics": [
        "mult_reg_spectral_cluster"
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    },
    {
      "page": "ncv_stability",
      "title": "Node cross-validation stability selection",
      "topics": [
        "ncv_stability"
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    },
    {
      "page": "ncv_stability_blockmodel",
      "title": "Select block-model size with node cross-validation",
      "topics": [
        "ncv_stability_blockmodel"
      ]
    },
    {
      "page": "netcrop_blockmodel",
      "title": "Select a block model with NETCROP",
      "topics": [
        "netcrop_blockmodel",
        "plot.netcrop_blockmodel",
        "print.netcrop_blockmodel",
        "print.summary.netcrop_blockmodel",
        "summary.netcrop_blockmodel"
      ]
    },
    {
      "page": "netcrop_lsm",
      "title": "Select latent-space dimension with NETCROP",
      "topics": [
        "netcrop_lsm",
        "plot.netcrop_lsm",
        "print.netcrop_lsm",
        "print.summary.netcrop_lsm",
        "summary.netcrop_lsm"
      ]
    },
    {
      "page": "netcrop_param_select",
      "title": "Select NETCROP partition parameters",
      "topics": [
        "netcrop_param_select"
      ]
    },
    {
      "page": "netcrop_rdpg",
      "title": "Select RDPG dimension with NETCROP",
      "topics": [
        "netcrop_rdpg",
        "plot.netcrop_rdpg",
        "print.netcrop_rdpg",
        "print.summary.netcrop_rdpg",
        "summary.netcrop_rdpg"
      ]
    },
    {
      "page": "netcrop_tune_regularizer",
      "title": "Tune a network regularizer with NETCROP",
      "topics": [
        "netcrop_tune_regularizer",
        "plot.netcrop_regularizer",
        "print.netcrop_regularizer",
        "print.summary.netcrop_regularizer",
        "summary.netcrop_regularizer"
      ]
    },
    {
      "page": "network_generators",
      "title": "Network input, generation, and probability calibration",
      "topics": [
        "network_generators"
      ]
    },
    {
      "page": "nmi",
      "title": "Compute normalized mutual information",
      "topics": [
        "nmi"
      ]
    },
    {
      "page": "normalize_lsm_positions",
      "title": "Normalize latent-space positions",
      "topics": [
        "normalize_lsm_positions"
      ]
    },
    {
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