{
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  "Package": "steinsampling",
  "Title": "Kernel Stein Discrepancy Goodness-of-Fit and Stein Sampling\nTools",
  "Version": "0.1.0",
  "Date": "2026-07-11",
  "Authors@R": "c(\nperson(\"Junhao\", \"Gao\",\nemail = \"jug049@ucsd.edu\",\nrole = c(\"aut\", \"cre\")),\nperson(\"Ery\", \"Arias-Castro\",\nemail = \"eariascastro@ucsd.edu\",\nrole = \"aut\")\n)",
  "Maintainer": "Junhao Gao <jug049@ucsd.edu>",
  "Description": "Provides Stein-discrepancy goodness-of-fit tests and\nStein-method-based sampling tools. The tests include kernel\nStein discrepancy U- and V-statistics following Liu et al.\n(2016) <doi:10.48550/arXiv.1602.03253> and Chwialkowski et al.\n(2016) <doi:10.48550/arXiv.1602.02964>, plus the finite set\nStein discrepancy test of Jitkrittum et al. (2017)\n<doi:10.48550/arXiv.1705.07673>. The sampling tools include\nStein thinning, Stein Points, Stein Point Markov chain Monte\nCarlo, and Stein variational gradient descent following Riabiz\net al. (2022) <doi:10.48550/arXiv.2005.03952>, Chen et al.\n(2018) <doi:10.48550/arXiv.1803.10161>, Chen et al. (2019)\n<doi:10.48550/arXiv.1905.03673>, and Liu and Wang (2016)\n<doi:10.48550/arXiv.1608.04471>. Gaussian mixture utilities are\nincluded for simulation, likelihoods, posterior probabilities,\nscores, and plots.",
  "URL": "https://github.com/junhao7622/steinsampling",
  "BugReports": "https://github.com/junhao7622/steinsampling/issues",
  "License": "GPL (>= 2)",
  "Encoding": "UTF-8",
  "Collate": "'steinsampling-package.R' 'stein_helpers.R' 'kernel_classes.R'\n'bootstrap.R' 'gmm_model.R' 'ksd_u_test.R' 'ksd_v_test.R'\n'fssd_test.R' 'svgd.R' 'stein_thinning.R' 'stein_points.R'\n'stein_point_mcmc.R'",
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  "Config/roxygen2/version": "8.0.0",
  "Packaged": {
    "Date": "2026-07-21 14:51:07 UTC",
    "User": "root"
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  "Author": "Junhao Gao [aut, cre], Ery Arias-Castro [aut]",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-07-21 10:30:02 UTC",
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    "compute_fssd_null_pvalue",
    "compute_fssd_unbiased_stat",
    "compute_tau",
    "cross_kernel",
    "custom_adjusted_gradient",
    "custom_stein_kernel",
    "eval_kernel",
    "find_median_distance",
    "fmin_grid",
    "fmin_mc",
    "fmin_nm",
    "fssd_opt_test",
    "fssd_rand_test",
    "fssd_test",
    "get_score_evaluator",
    "gmm",
    "grad_theta_v_kernel",
    "grad_x_kernel",
    "grw",
    "grwmetrop",
    "ksd_u_bootstrap",
    "ksd_u_statistic",
    "ksd_u_test",
    "ksd_uq_matrix",
    "ksd_v_bootstrap",
    "ksd_v_statistic",
    "ksd_v_test",
    "ksd_vq_matrix",
    "likelihoodgmm",
    "mala",
    "perturbgmm",
    "plotgmm",
    "posteriorgmm",
    "rgmm",
    "rwm",
    "scorefunctiongmm",
    "sp_mcmc",
    "sp_mcmc_criterion",
    "sp_mcmc_eval_candidates",
    "sp_mcmc_select_start",
    "sp_mcmc_state",
    "stein_codescent",
    "stein_kernel",
    "stein_kernel_imq_score",
    "stein_kernel_inverse_log",
    "stein_kernel_matrix",
    "stein_points",
    "stein_thinning",
    "svgd",
    "trace_mixed_kernel",
    "update_svgd"
  ],
  "_help": [
    {
      "page": "steinsampling-package",
      "title": "steinsampling: Stein tests and Stein sampling tools",
      "topics": [
        "steinsampling-package",
        "steinsampling"
      ]
    },
    {
      "page": "compute_fssd_null_pvalue",
      "title": "Simulate the FSSD null distribution",
      "topics": [
        "compute_fssd_null_pvalue"
      ]
    },
    {
      "page": "compute_fssd_unbiased_stat",
      "title": "Estimate squared FSSD from a feature matrix",
      "topics": [
        "compute_fssd_unbiased_stat"
      ]
    },
    {
      "page": "compute_tau",
      "title": "Compute the FSSD feature matrix",
      "topics": [
        "compute_tau"
      ]
    },
    {
      "page": "custom_adjusted_gradient",
      "title": "Apply a custom SVGD direction adjustment",
      "topics": [
        "custom_adjusted_gradient"
      ]
    },
    {
      "page": "custom_stein_kernel",
      "title": "Create a custom Stein kernel",
      "topics": [
        "custom_stein_kernel"
      ]
    },
    {
      "page": "find_median_distance",
      "title": "Median squared distance between sample pairs",
      "topics": [
        "find_median_distance"
      ]
    },
    {
      "page": "fmin_grid",
      "title": "Create the grid search used by Stein Points",
      "topics": [
        "fmin_grid"
      ]
    },
    {
      "page": "fmin_mc",
      "title": "Create the Monte Carlo search used by Stein Points",
      "topics": [
        "fmin_mc"
      ]
    },
    {
      "page": "fmin_nm",
      "title": "Create the multi-start Nelder-Mead search used by Stein Points",
      "topics": [
        "fmin_nm"
      ]
    },
    {
      "page": "fssd_opt_test",
      "title": "FSSD test with optimized test locations",
      "topics": [
        "fssd_opt_test"
      ]
    },
    {
      "page": "fssd_rand_test",
      "title": "FSSD test with random test locations",
      "topics": [
        "fssd_rand_test"
      ]
    },
    {
      "page": "fssd_test",
      "title": "Finite Set Stein Discrepancy goodness-of-fit test",
      "topics": [
        "fssd_test"
      ]
    },
    {
      "page": "get_score_evaluator",
      "title": "Create a score function for a fixed Gaussian mixture model",
      "topics": [
        "get_score_evaluator"
      ]
    },
    {
      "page": "gmm",
      "title": "Create a Gaussian mixture model",
      "topics": [
        "gmm"
      ]
    },
    {
      "page": "grw",
      "title": "Run the Gaussian random-walk transition used by SP-MCMC",
      "topics": [
        "grw"
      ]
    },
    {
      "page": "grwmetrop",
      "title": "Run a Gaussian random-walk Metropolis chain",
      "topics": [
        "grwmetrop"
      ]
    },
    {
      "page": "kernel_generics",
      "title": "Building blocks for Stein kernels",
      "topics": [
        "cross_kernel",
        "eval_kernel",
        "grad_theta_v_kernel",
        "grad_x_kernel",
        "kernel_generics",
        "trace_mixed_kernel"
      ]
    },
    {
      "page": "ksd_u_bootstrap",
      "title": "Bootstrap the KSD U-statistic from a Stein-kernel matrix",
      "topics": [
        "ksd_u_bootstrap"
      ]
    },
    {
      "page": "ksd_u_statistic",
      "title": "Compute the KSD U-statistic from its Stein-kernel matrix",
      "topics": [
        "ksd_u_statistic"
      ]
    },
    {
      "page": "ksd_u_test",
      "title": "KSD goodness-of-fit test using the Liu et al. U-statistic",
      "topics": [
        "ksd_u_test"
      ]
    },
    {
      "page": "ksd_uq_matrix",
      "title": "Build the Stein-kernel matrix for the KSD U-statistic",
      "topics": [
        "ksd_uq_matrix"
      ]
    },
    {
      "page": "ksd_v_bootstrap",
      "title": "Wild-bootstrap the KSD V-statistic",
      "topics": [
        "ksd_v_bootstrap"
      ]
    },
    {
      "page": "ksd_v_statistic",
      "title": "Compute the KSD V-statistic from its Stein-kernel matrix",
      "topics": [
        "ksd_v_statistic"
      ]
    },
    {
      "page": "ksd_v_test",
      "title": "KSD goodness-of-fit test using the Chwialkowski et al. V-statistic",
      "topics": [
        "ksd_v_test"
      ]
    },
    {
      "page": "ksd_vq_matrix",
      "title": "Build the Stein-kernel matrix for the KSD V-statistic",
      "topics": [
        "ksd_vq_matrix"
      ]
    },
    {
      "page": "likelihoodgmm",
      "title": "Compute the mixture density for samples",
      "topics": [
        "likelihoodgmm"
      ]
    },
    {
      "page": "mala",
      "title": "Run a Metropolis-adjusted Langevin chain",
      "topics": [
        "mala"
      ]
    },
    {
      "page": "perturbgmm",
      "title": "Perturb the component means of a Gaussian mixture model",
      "topics": [
        "perturbgmm"
      ]
    },
    {
      "page": "plotgmm",
      "title": "Plot a one-dimensional Gaussian mixture sample",
      "topics": [
        "plotgmm"
      ]
    },
    {
      "page": "posteriorgmm",
      "title": "Compute posterior component probabilities for a Gaussian mixture",
      "topics": [
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      ]
    },
    {
      "page": "rgmm",
      "title": "Sample from a Gaussian mixture model",
      "topics": [
        "rgmm"
      ]
    },
    {
      "page": "rwm",
      "title": "Alias for the Gaussian random-walk transition",
      "topics": [
        "rwm"
      ]
    },
    {
      "page": "scorefunctiongmm",
      "title": "Compute the score of a Gaussian mixture density",
      "topics": [
        "scorefunctiongmm"
      ]
    },
    {
      "page": "sp_mcmc",
      "title": "Generate Stein Points from short MCMC candidate paths",
      "topics": [
        "sp_mcmc"
      ]
    },
    {
      "page": "sp_mcmc_criterion",
      "title": "Create an SP-MCMC start-point rule",
      "topics": [
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      ]
    },
    {
      "page": "sp_mcmc_eval_candidates",
      "title": "Score SP-MCMC candidate points",
      "topics": [
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    },
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      "page": "sp_mcmc_select_start",
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    },
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      "page": "sp_mcmc_state",
      "title": "Store the current SP-MCMC state for a start rule",
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      "page": "stein_codescent",
      "title": "Refine Stein Points by coordinate descent",
      "topics": [
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    },
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      "page": "stein_kernel",
      "title": "Create a built-in Stein kernel",
      "topics": [
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      ]
    },
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      "page": "stein_kernel_imq_score",
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      ]
    },
    {
      "page": "stein_kernel_inverse_log",
      "title": "Create an inverse-log Stein kernel",
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    },
    {
      "page": "stein_kernel_matrix",
      "title": "Compute pairwise Stein kernel values",
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    },
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      "title": "Build a Stein Points sequence by greedy KSD minimization",
      "topics": [
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      ]
    },
    {
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      "title": "Compress existing samples with Stein thinning",
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      ]
    },
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      "title": "Run SVGD particle updates",
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