{
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  "Package": "kernR",
  "Title": "Kernel-Based Causal Distributional Testing",
  "Version": "0.7.0.9000",
  "Authors@R": "c(\nperson(\"Max\", \"Moldovan\", , \"max.moldovan@adelaide.edu.au\",\nrole = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0001-9680-8474\"))\n)",
  "Description": "Kernel-based hypothesis tests for causal inference and\ndistributional treatment effects. Implements backdoor-adjusted\nHSIC (bd-HSIC) for testing causal association, and doubly\nrobust kernel statistics (DR-DATE, DR-DETT) for testing\ndistributional treatment effects beyond mean shifts. Supports\nbinary, continuous, and mixed treatments, hierarchical/nested\ndata structures, and scales to large datasets via Nystrom\napproximation and random Fourier features. This package depends\non 'PESTO' (GPL (>= 3)); kernR's own sources are released under\nthe MIT licence, but the installed combination with 'PESTO' is\na combined work subject to the terms of the GPL (>= 3).",
  "License": "MIT + file LICENSE",
  "URL": "https://github.com/max578/kernR, https://max578.github.io/kernR",
  "BugReports": "https://github.com/max578/kernR/issues",
  "Remotes": [
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  "Roxygen": "list(markdown = TRUE)",
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  "Repository": "https://max578.r-universe.dev",
  "Date/Publication": "2026-06-03 01:02:57 UTC",
  "RemoteUrl": "https://github.com/max578/kernR",
  "RemoteRef": "main",
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  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-06-03 03:22:50 UTC",
    "User": "root"
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  "Author": "Max Moldovan [aut, cre, cph] (ORCID:\n<https://orcid.org/0000-0001-9680-8474>)",
  "Maintainer": "Max Moldovan <max.moldovan@adelaide.edu.au>",
  "MD5sum": "6657c6b6889364d02f8003256617d1dc",
  "_user": "max578",
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  "_created": "2026-06-03T03:22:50.000Z",
  "_published": "2026-06-03T03:29:39.515Z",
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    "author": "Max Moldovan <max.moldovan@gmail.com>",
    "committer": "Max Moldovan <max.moldovan@gmail.com>",
    "message": "chore(ci): resolve PESTO from GitHub via Remotes\n\nkernR's CI resolves PESTO from max578's r-universe, which rebuilds on its\nown cadence; immediately after a PESTO version bump the r-universe copy\nlags, so `PESTO (>= 0.6.0)` is briefly unsatisfiable and setup-r-dependencies\nfails. Point a Remotes entry at the GitHub source-of-truth so CI and dev\ninstalls always get the current PESTO regardless of r-universe rebuild\ntiming. r-universe's own builds are unaffected (it resolves within its\nregistry).\n",
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    "dist_regression",
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    "dr_dett_test",
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    "estimate_propensity",
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    "fit_density_ratio",
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    "hierarchical_test",
    "hsic_identifiability",
    "hsic_sensitivity",
    "hsic_test",
    "hsic_test_nystrom",
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    "kernel_downscale",
    "kernel_matrix",
    "kernel_spec",
    "ksd_test",
    "ksd_test_nystrom",
    "lhs_design",
    "mmd_ppc",
    "mmd_test",
    "numeric_score",
    "nystrom_factor",
    "pesto_ensemble",
    "plot_weights",
    "posterior_sample_aggregate",
    "predict_density_ratio",
    "rff_features",
    "select_bandwidth"
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      "title": "Aggregate-Likelihood Downscaling",
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        "downscaling and embeddings"
      ],
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        "aggregate_downscale"
      ]
    },
    {
      "page": "assess_overlap",
      "title": "Assess Propensity Score Overlap",
      "concept": [
        "density ratio and propensity"
      ],
      "topics": [
        "assess_overlap"
      ]
    },
    {
      "page": "bd_hsic_test",
      "title": "Backdoor-HSIC Test for Causal Association",
      "concept": [
        "causal association tests"
      ],
      "topics": [
        "bd_hsic_test"
      ]
    },
    {
      "page": "concordance_test",
      "title": "Kernel k-sample Concordance Test",
      "concept": [
        "goodness-of-fit tests"
      ],
      "topics": [
        "concordance_test"
      ]
    },
    {
      "page": "concordance_test_nystrom",
      "title": "Accelerated Kernel k-sample Concordance Test (Nystrom / RFF)",
      "concept": [
        "goodness-of-fit tests",
        "low-rank acceleration"
      ],
      "topics": [
        "concordance_test_nystrom"
      ]
    },
    {
      "page": "coverage_test",
      "title": "Coverage / Calibration Diagnostic for a Predictive Ensemble",
      "concept": [
        "goodness-of-fit tests"
      ],
      "topics": [
        "coverage_test",
        "coverage_test.default",
        "coverage_test.pesto_ensemble",
        "coverage_test.pesto_ensemble_manifest"
      ]
    },
    {
      "page": "dist_regression",
      "title": "Kernel Distribution Regression",
      "concept": [
        "downscaling and embeddings"
      ],
      "topics": [
        "dist_regression"
      ]
    },
    {
      "page": "dr_date_scenario",
      "title": "DR-DATE for Two PESTO Ensemble Scenarios",
      "concept": [
        "distributional treatment effects"
      ],
      "topics": [
        "dr_date_scenario"
      ]
    },
    {
      "page": "dr_date_test",
      "title": "Doubly Robust Distributional Average Treatment Effect Test (DR-DATE)",
      "concept": [
        "distributional treatment effects"
      ],
      "topics": [
        "dr_date_test"
      ]
    },
    {
      "page": "dr_dett_test",
      "title": "Doubly Robust Distributional Effect on the Treated Test (DR-DETT)",
      "concept": [
        "distributional treatment effects"
      ],
      "topics": [
        "dr_dett_test"
      ]
    },
    {
      "page": "effective_sample_size",
      "title": "Compute Effective Sample Size",
      "concept": [
        "density ratio and propensity"
      ],
      "topics": [
        "effective_sample_size"
      ]
    },
    {
      "page": "estimate_density_ratio",
      "title": "Estimate Density Ratios (backwards-compatible wrapper)",
      "concept": [
        "density ratio and propensity"
      ],
      "topics": [
        "estimate_density_ratio"
      ]
    },
    {
      "page": "estimate_propensity",
      "title": "Estimate Propensity Scores",
      "concept": [
        "density ratio and propensity"
      ],
      "topics": [
        "estimate_propensity"
      ]
    },
    {
      "page": "fit_cme",
      "title": "Estimate Conditional Mean Embedding via Kernel Ridge Regression",
      "concept": [
        "downscaling and embeddings"
      ],
      "topics": [
        "fit_cme"
      ]
    },
    {
      "page": "fit_density_ratio",
      "title": "Fit a Density-Ratio Model",
      "concept": [
        "density ratio and propensity"
      ],
      "topics": [
        "fit_density_ratio"
      ]
    },
    {
      "page": "gaussian_score",
      "title": "Score function for a multivariate normal target",
      "concept": [
        "goodness-of-fit tests"
      ],
      "topics": [
        "gaussian_score"
      ]
    },
    {
      "page": "hierarchical_test",
      "title": "Hierarchical Kernel Causal Test",
      "concept": [
        "causal association tests"
      ],
      "topics": [
        "hierarchical_test"
      ]
    },
    {
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      "title": "HSIC-Based Identifiability Diagnostic",
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        "sensitivity and identifiability"
      ],
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      ]
    },
    {
      "page": "hsic_sensitivity",
      "title": "HSIC-Based Distributional Sensitivity Index",
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        "sensitivity and identifiability"
      ],
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        "hsic_sensitivity"
      ]
    },
    {
      "page": "hsic_test",
      "title": "HSIC Independence Test",
      "concept": [
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      ],
      "topics": [
        "hsic_test"
      ]
    },
    {
      "page": "hsic_test_nystrom",
      "title": "HSIC Independence Test via Low-Rank Factorisation",
      "concept": [
        "low-rank acceleration"
      ],
      "topics": [
        "hsic_test_nystrom"
      ]
    },
    {
      "page": "kernel_causal_test",
      "title": "Unified Kernel Causal Test",
      "concept": [
        "causal association tests"
      ],
      "topics": [
        "kernel_causal_test"
      ]
    },
    {
      "page": "kernel_downscale",
      "title": "Kernel-Based Statistical Downscaling",
      "concept": [
        "downscaling and embeddings"
      ],
      "topics": [
        "kernel_downscale"
      ]
    },
    {
      "page": "kernel_matrix",
      "title": "Compute a Kernel Matrix",
      "concept": [
        "kernel primitives"
      ],
      "topics": [
        "kernel_matrix"
      ]
    },
    {
      "page": "kernel_spec",
      "title": "Create a Kernel Specification",
      "concept": [
        "kernel primitives"
      ],
      "topics": [
        "kernel_spec"
      ]
    },
    {
      "page": "ksd_test",
      "title": "Kernel Stein Discrepancy Goodness-of-Fit Test",
      "concept": [
        "goodness-of-fit tests"
      ],
      "topics": [
        "ksd_test"
      ]
    },
    {
      "page": "ksd_test_nystrom",
      "title": "Accelerated Kernel Stein Discrepancy Goodness-of-Fit Test (Nystrom)",
      "concept": [
        "goodness-of-fit tests",
        "low-rank acceleration"
      ],
      "topics": [
        "ksd_test_nystrom"
      ]
    },
    {
      "page": "lhs_design",
      "title": "Latin-Hypercube Design Over Bounded Parameters",
      "concept": [
        "sensitivity and identifiability"
      ],
      "topics": [
        "lhs_design"
      ]
    },
    {
      "page": "mmd_ppc",
      "title": "MMD Posterior-Predictive Check",
      "concept": [
        "posterior predictive checks"
      ],
      "topics": [
        "mmd_ppc",
        "mmd_ppc.default",
        "mmd_ppc.pesto_ensemble",
        "mmd_ppc.pesto_ensemble_manifest"
      ]
    },
    {
      "page": "mmd_test",
      "title": "MMD Two-Sample Test",
      "concept": [
        "independence and two-sample tests"
      ],
      "topics": [
        "mmd_test"
      ]
    },
    {
      "page": "numeric_score",
      "title": "Finite-difference score from a log-density",
      "concept": [
        "goodness-of-fit tests"
      ],
      "topics": [
        "numeric_score"
      ]
    },
    {
      "page": "nystrom_factor",
      "title": "Nystrom Low-Rank Kernel Factorisation",
      "concept": [
        "low-rank acceleration"
      ],
      "topics": [
        "nystrom_factor"
      ]
    },
    {
      "page": "pesto_ensemble",
      "title": "PESTO Ensemble Manifest (Constructor)",
      "concept": [
        "posterior predictive checks"
      ],
      "topics": [
        "pesto_ensemble"
      ]
    },
    {
      "page": "plot_weights",
      "title": "Plot Weight Diagnostics",
      "concept": [
        "density ratio and propensity"
      ],
      "topics": [
        "plot_weights"
      ]
    },
    {
      "page": "plot.hsic_identifiability",
      "title": "Plot an HSIC Identifiability Scan",
      "topics": [
        "plot.hsic_identifiability"
      ]
    },
    {
      "page": "plot.hsic_sensitivity",
      "title": "Plot HSIC-Sensitivity Indices",
      "topics": [
        "plot.hsic_sensitivity"
      ]
    },
    {
      "page": "plot.kernel_test_result",
      "title": "Plot a Kernel Test Result",
      "topics": [
        "plot.kernel_test_result"
      ]
    },
    {
      "page": "posterior_sample_aggregate",
      "title": "Sample from the posterior of an aggregate-downscale fit",
      "concept": [
        "downscaling and embeddings"
      ],
      "topics": [
        "posterior_sample_aggregate"
      ]
    },
    {
      "page": "predict_density_ratio",
      "title": "Predict from a Fitted Density-Ratio Model",
      "concept": [
        "density ratio and propensity"
      ],
      "topics": [
        "predict_density_ratio"
      ]
    },
    {
      "page": "predict.cme_fit",
      "title": "Predict Conditional Mean Embedding Weights at New Points",
      "topics": [
        "predict.cme_fit"
      ]
    },
    {
      "page": "predict.dist_regression",
      "title": "Predict from a Fitted Distribution Regression Model",
      "topics": [
        "predict.dist_regression"
      ]
    },
    {
      "page": "print.cme_fit",
      "title": "Print a Conditional Mean Embedding Fit",
      "topics": [
        "print.cme_fit"
      ]
    },
    {
      "page": "rff_features",
      "title": "Random Fourier Features for the RBF Kernel",
      "concept": [
        "low-rank acceleration"
      ],
      "topics": [
        "rff_features"
      ]
    },
    {
      "page": "select_bandwidth",
      "title": "Select Kernel Bandwidth",
      "concept": [
        "kernel primitives"
      ],
      "topics": [
        "select_bandwidth"
      ]
    }
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