{ "_id": "6ad3007590c8bbdf41a50cb2", "Package": "proxymix", "Type": "Package", "Title": "Kullback-Leibler Optimal Gaussian Mixture Proxies for Target\nDensities", "Version": "0.16.0.9000", "Authors@R": "c(\nperson(given = \"Max\",\nfamily = \"Moldovan\",\nrole = c(\"aut\", \"cre\"),\nemail = \"max.moldovan@gmail.com\",\ncomment = c(ORCID = \"0000-0001-9680-8474\")),\nperson(given = \"John\",\nfamily = \"van der Hoek\",\nrole = \"aut\",\nemail = \"john.vanderhoek@adelaide.edu.au\"))", "Description": "Fits multivariate Gaussian-mixture proxies that are\nKullback-Leibler optimal to user-supplied target densities on\nreal Euclidean space. Three fitting regimes are unified under\none verb: (i) closed-form moment matching for a single\ncomponent, (ii) classical expectation-maximisation when\nindependent samples are available, and (iii) importance-sampled\nexpectation-maximisation that minimises the Kullback-Leibler\ndivergence when the target can be evaluated point-wise but not\n(cheaply) sampled. Closed-form Gaussian-mixture operators\n(density, sampling, marginalisation, conditioning, divergence)\nround out the toolkit. The conditioning operator drives\nmultiple imputation of data missing at random, covering the\nmultimodal and heteroscedastic cases a single-Gaussian model\ncannot represent. Implements the regime hierarchy of van der\nHoek and Elliott (2024) .", "License": "MIT + file LICENSE", "URL": "https://github.com/max578/proxymix,\nhttps://max578.github.io/proxymix/", "BugReports": "https://github.com/max578/proxymix/issues", "Encoding": "UTF-8", "Language": "en-AU", "VignetteBuilder": "knitr", "Roxygen": "list(markdown = TRUE)", "Config/testthat/edition": "3", "Config/testthat/parallel": "true", "Config/roxygen2/version": "8.1.0", "Repository": "https://max578.r-universe.dev", "Date/Publication": "2026-10-16 12:32:53 UTC", "RemoteUrl": "https://github.com/max578/proxymix", "RemoteRef": "main", "RemoteSha": "b66ddbbae18b2907f441e82427c72c6b52346841", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-17 04:53:48 UTC", "User": "root" }, "Author": "Max Moldovan [aut, cre] (ORCID:\n),\nJohn van der Hoek [aut]", "Maintainer": "Max Moldovan ", "_user": "max578", "_type": "src", "_file": "proxymix_0.16.0.9000.tar.gz", "_fileid": "https://r2.ropensci.org/8318457b5b251f1c29eb0840ce36115f3223639b424bcfda857e906a3a7fd84a", "_filesize": 4937336, "_sha256": "8318457b5b251f1c29eb0840ce36115f3223639b424bcfda857e906a3a7fd84a", "_expires": "2027-01-25T04:58:26.000Z", "_created": "2026-10-17T04:53:48.000Z", "_published": "2026-10-17T04:58:28.910Z", "_buildurl": "https://github.com/r-universe/max578/actions/runs/39003908812", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 117313532183, "time": 220, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12090555839" }, { "job": 117313532169, "time": 215, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12090241659" }, { "job": 117313532178, "time": 154, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "12089999689" }, { "job": 117313532158, "time": 159, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "12089499294" }, { "job": 117312390124, "time": 377, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "12090252542" }, { "job": 117313532150, "time": 125, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "12090660979" }, { "job": 117313532187, "time": 221, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12090681149" }, { "job": 117313532228, "time": 211, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "12089379644" }, { "job": 117313532177, "time": 210, "config": "windows-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12090104122" } ], "_upstream": "https://github.com/max578/proxymix", "_commit": { "id": "b66ddbbae18b2907f441e82427c72c6b52346841", "author": "Max Moldovan ", "committer": "Max Moldovan ", "message": "ci: keep the CRAN release site at the root, development site under dev/\n", "time": 1792153973 }, "_maintainer": { "name": "Max Moldovan", "email": "max.moldovan@gmail.com", "login": "max578", "orcid": "0000-0001-9680-8474", "description": "Research scientist, R&D translation professional, woking on practical impact applications in agriculture, healthcare/madicine and good-practices-AI education.", "uuid": 35710829 }, "_distro": "resolute", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.3", "role": "Depends" }, { "package": "S7", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "mvnfast", "role": "Imports" }, { "package": "cli", "role": "Imports" }, { "package": "rlang", "role": "Imports" }, { "package": "data.table", "role": "Imports" }, { "package": "withr", "role": "Imports" }, { "package": "mclust", "role": "Suggests" }, { "package": "mice", "role": "Suggests" }, { "package": "generics", "role": "Suggests" }, { "package": "ggplot2", "role": "Suggests" }, { "package": "viridis", "role": "Suggests" }, { "package": "patchwork", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" }, { "package": "vdiffr", "role": "Suggests" }, { "package": "spelling", "role": "Suggests" }, { "package": "covr", "role": "Suggests" } ], "_owner": "max578", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2026-20", "n": 4 }, { "week": "2026-23", "n": 1 }, { "week": "2026-24", "n": 2 }, { "week": "2026-25", "n": 24 }, { "week": "2026-26", "n": 7 }, { "week": "2026-27", "n": 8 }, { "week": "2026-35", "n": 2 }, { "week": "2026-36", "n": 1 }, { "week": "2026-37", "n": 1 }, { "week": "2026-39", "n": 7 }, { "week": "2026-40", "n": 6 }, { "week": "2026-41", "n": 7 }, { "week": "2026-42", "n": 6 } ], "_tags": [ { "name": "v0.3.0", "date": "2026-05-15" }, { "name": "v0.4.0", "date": "2026-06-14" }, { "name": "v0.5.0", "date": "2026-06-16" }, { "name": "v0.6.0", "date": "2026-06-17" }, { "name": "v0.7.0", "date": "2026-06-19" }, { "name": "v0.8.0", "date": "2026-06-19" }, { "name": "v0.9.0", "date": "2026-06-20" }, { "name": "v0.10.0", "date": "2026-06-21" }, { "name": "v0.11.0", "date": "2026-06-21" }, { "name": "v0.11.1", "date": "2026-06-22" }, { "name": "v0.11.2", "date": "2026-06-22" }, { "name": "v0.11.3", "date": "2026-06-24" }, { "name": "v0.11.4", "date": "2026-06-24" }, { "name": "v0.11.6", "date": "2026-07-05" }, { "name": "v0.12.0", "date": "2026-07-05" }, { "name": "v0.13.0", "date": "2026-07-05" }, { "name": "v0.14.0", "date": "2026-07-05" }, { "name": "v0.15.0", "date": "2026-07-05" }, { "name": "v0.15.1", "date": "2026-07-05" }, { "name": "v0.15.2", "date": "2026-08-25" }, { "name": "v0.16.0", "date": "2026-09-29" } ], "_stars": 1, "_contributors": [ { "user": "max578", "count": 117, "uuid": 35710829 } ], "_userbio": { "uuid": 35710829, "type": "user", "name": "Max Moldovan", "followers": 2, "description": "Research scientist, R&D translation professional, woking on practical impact applications in agriculture, healthcare/madicine and good-practices-AI education." }, "_downloads": { "count": 161, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/proxymix" }, "_devurl": "https://github.com/max578/proxymix", "_pkgdown": "https://max578.github.io/proxymix/", "_searchresults": 0, "_rbuild": "4.6.1", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/manual.pdf", "extra/NEWS.html", "extra/NEWS.txt", "extra/proxymix.html", "extra/readme.html", "extra/readme.md", "LICENSE" ], "_homeurl": "https://github.com/max578/proxymix", "_realowner": "max578", "_cranurl": true, "_releases": [ { "version": "0.16.0", "date": "2026-10-08" } ], "_exports": [ "as_mids", "banana_target", "bic_aic", "censored", "dgmm", "donut_target", "epanechnikov_target", "ess_summary", "ess_trace", "fit_em_samples", "fit_kld_em", "fit_moment_match", "fit_mppca_samples", "fit_proxymix", "fit_uplift", "from_cf", "from_cf_elliptical", "from_cf_sources", "from_kde", "from_objective", "gmm", "gmm_affine", "gmm_aggregate", "gmm_anneal_path", "gmm_canonicalise", "gmm_cf", "gmm_cf_distance", "gmm_cf_mean", "gmm_cf_tail_prob", "gmm_cf_variance", "gmm_complete", "gmm_conditional_entropy", "gmm_conditionalise", "gmm_convolve", "gmm_counterfactual", "gmm_counterfactual_law", "gmm_cov", "gmm_covariances", "gmm_dim", "gmm_divergence", "gmm_entropy", "gmm_eos_test", "gmm_evidence", "gmm_filter", "gmm_fit", "gmm_fit_ensemble", "gmm_fit_quality", "gmm_imputation", "gmm_impute", "gmm_independence_graph", "gmm_intervene", "gmm_kld", "gmm_marginalise", "gmm_mean", "gmm_means", "gmm_missing", "gmm_mix", "gmm_modes", "gmm_mutual_information", "gmm_n_components", "gmm_observe", "gmm_product", "gmm_reduce", "gmm_target", "gmm_target_from_posterior", "gmm_target_from_samples", "gmm_weights", "hellinger_mc", "init_kmeans", "init_moment_seed", "init_random", "init_warm_start", "is_mvn", "is_mvt", "is_proposal", "is_uniform", "kld_trace", "mar", "maxent_target", "mixture_target", "mnar", "multi_start_best_of", "pgmm", "proposal_mvn", "proposal_mvt", "proposal_uniform", "proxy_cate", "proxy_confounding_gap", "proxy_decide", "proxy_fmi", "proxy_functional_ci", "proxy_identification_report", "proxy_mnar_sensitivity", "proxy_overlap", "proxy_policy_value", "proxy_pool", "proxy_predict", "proxy_regime_segments", "proxy_retrospective_uplift", "proxy_uplift", "qgmm", "rgmm", "select_N", "uplift_identification", "uplift_model" ], "_help": [ { "page": "as_mids", "title": "Convert imputations to a mice multiply-imputed dataset", "concept": [ "imputation" ], "topics": [ "as_mids" ] }, { "page": "autoplot.gmm_fit", "title": "Plot a fitted Gaussian-mixture proxy", "concept": [ "classes" ], "topics": [ "autoplot.gmm_fit" ] }, { "page": "banana_target", "title": "Banana-shaped 2-D target", "concept": [ "targets" ], "topics": [ "banana_target" ] }, { "page": "bic_aic", "title": "Information criteria: BIC, AIC, and ICL", "concept": [ "diagnostics" ], "topics": [ "bic_aic" ] }, { "page": "dgmm", "title": "Density of a Gaussian mixture", "concept": [ "ops" ], "topics": [ "dgmm" ] }, { "page": "donut_target", "title": "Donut-shaped 2-D target", "concept": [ "targets" ], "topics": [ "donut_target" ] }, { "page": "epanechnikov_target", "title": "Compact-support Epanechnikov target", "concept": [ "targets" ], "topics": [ "epanechnikov_target" ] }, { "page": "ess_summary", "title": "Summary of importance-sampling diagnostics", "concept": [ "diagnostics" ], "topics": [ "ess_summary" ] }, { "page": "ess_trace", "title": "Effective sample size of the importance-sampling weights", "concept": [ "diagnostics" ], "topics": [ "ess_trace" ] }, { "page": "fit_em_samples", "title": "Classical EM fit on samples", "concept": [ "fitting" ], "topics": [ "fit_em_samples" ] }, { "page": "fit_kld_em", "title": "Importance-sampled KLD-EM fit (regime iii)", "concept": [ "fitting" ], "topics": [ "fit_kld_em" ] }, { "page": "fit_moment_match", "title": "Closed-form moment-matching fit", "concept": [ "fitting" ], "topics": [ "fit_moment_match" ] }, { "page": "fit_mppca_samples", "title": "Mixture of probabilistic PCA fitted to samples", "concept": [ "fitting" ], "topics": [ "fit_mppca_samples" ] }, { "page": "fit_proxymix", "title": "Fit a Gaussian-mixture proxy to a target density", "concept": [ "interface" ], "topics": [ "fit_proxymix" ] }, { "page": "fit_uplift", "title": "Fit an uplift / next-best-action model from a data frame", "concept": [ "decision" ], "topics": [ "fit_uplift" ] }, { "page": "from_cf", "title": "Fit a Gaussian mixture to a characteristic function", "concept": [ "fitting" ], "topics": [ "from_cf" ] }, { "page": "from_cf_elliptical", "title": "Fit concentric normals to an elliptical characteristic function", "concept": [ "fitting" ], "topics": [ "from_cf_elliptical" ] }, { "page": "from_cf_sources", "title": "Fit a characteristic function that is a linear mix of independent sources", "concept": [ "fitting" ], "topics": [ "from_cf_sources" ] }, { "page": "from_kde", "title": "Compile a kernel-density estimate into a Gaussian-mixture proxy", "concept": [ "fitting" ], "topics": [ "from_kde" ] }, { "page": "from_objective", "title": "Map the optima of an objective with a Gaussian-mixture proxy", "concept": [ "fitting" ], "topics": [ "from_objective" ] }, { "page": "glance.gmm_fit", "title": "Glance at a fitted Gaussian-mixture proxy", "concept": [ "classes" ], "topics": [ "glance.gmm_fit" ] }, { "page": "gmm", "title": "A Gaussian mixture", "concept": [ "classes" ], "topics": [ "gmm" ] }, { "page": "gmm_affine", "title": "Affine pushforward of a Gaussian mixture", "concept": [ "operators" ], "topics": [ "gmm_affine" ] }, { "page": "gmm_aggregate", "title": "Aggregation pushforward of a Gaussian mixture", "concept": [ "operators" ], "topics": [ "gmm_aggregate" ] }, { "page": "gmm_anneal_path", "title": "Phase-transition component discovery by deterministic annealing", "concept": [ "diagnostics" ], "topics": [ "gmm_anneal_path" ] }, { "page": "gmm_canonicalise", "title": "Canonicalise the component ordering of a Gaussian mixture", "concept": [ "ops" ], "topics": [ "gmm_canonicalise" ] }, { "page": "gmm_cf", "title": "Characteristic function of a Gaussian mixture", "concept": [ "ops" ], "topics": [ "gmm_cf" ] }, { "page": "gmm_cf_distance", "title": "Kolmogorov distance between a characteristic-function target and a mixture", "concept": [ "diagnostics" ], "topics": [ "gmm_cf_distance" ] }, { "page": "gmm_cf_mean", "title": "The counterfactual mean", "concept": [ "decision" ], "topics": [ "gmm_cf_mean" ] }, { "page": "gmm_cf_tail_prob", "title": "A counterfactual tail probability", "concept": [ "decision" ], "topics": [ "gmm_cf_tail_prob" ] }, { "page": "gmm_cf_variance", "title": "The counterfactual variance", "concept": [ "decision" ], "topics": [ "gmm_cf_variance" ] }, { "page": "gmm_complete", "title": "Extract completed datasets from a 'gmm_imputation'", "concept": [ "imputation" ], "topics": [ "gmm_complete" ] }, { "page": "gmm_conditional_entropy", "title": "Renyi-2 or Shannon entropy of a conditional Gaussian mixture", "concept": [ "diagnostics" ], "topics": [ "gmm_conditional_entropy" ] }, { "page": "gmm_conditionalise", "title": "Conditional of a Gaussian mixture", "concept": [ "ops" ], "topics": [ "gmm_conditionalise" ] }, { "page": "gmm_convolve", "title": "Convolution of two independent Gaussian mixtures", "concept": [ "operators" ], "topics": [ "gmm_convolve" ] }, { "page": "gmm_counterfactual", "title": "Counterfactual law of one unit (abduction, action, prediction)", "concept": [ "decision", "operators" ], "topics": [ "gmm_counterfactual" ] }, { "page": "gmm_counterfactual_law", "title": "A per-unit counterfactual law", "concept": [ "classes" ], "topics": [ "gmm_counterfactual_law" ] }, { "page": "gmm_dim", "title": "Dimension of a Gaussian mixture", "concept": [ "classes" ], "topics": [ "gmm_dim" ] }, { "page": "gmm_divergence", "title": "Divergence between two Gaussian mixtures", "concept": [ "ops" ], "topics": [ "gmm_divergence" ] }, { "page": "gmm_entropy", "title": "Renyi-2 or Shannon entropy of a Gaussian mixture", "concept": [ "diagnostics" ], "topics": [ "gmm_entropy" ] }, { "page": "gmm_eos_test", "title": "End-of-sample instability test on a Gaussian state-space filter", "topics": [ "gmm_eos_test" ] }, { "page": "gmm_evidence", "title": "Estimate the target's normalising constant from a fitted proxy", "concept": [ "diagnostics" ], "topics": [ "gmm_evidence" ] }, { "page": "gmm_filter", "title": "Bounded Gaussian-sum filtering over an observation series", "concept": [ "operators" ], "topics": [ "gmm_filter" ] }, { "page": "gmm_fit", "title": "A fitted Gaussian-mixture proxy", "concept": [ "classes" ], "topics": [ "gmm_fit" ] }, { "page": "gmm_fit_ensemble", "title": "Bootstrap ensemble of a fitted proxy", "concept": [ "diagnostics" ], "topics": [ "gmm_fit_ensemble" ] }, { "page": "gmm_fit_quality", "title": "The quality certificate of a fit or derived mixture", "concept": [ "diagnostics" ], "topics": [ "gmm_fit_quality" ] }, { "page": "gmm_imputation", "title": "A Gaussian-mixture multiple-imputation result", "concept": [ "imputation" ], "topics": [ "gmm_imputation" ] }, { "page": "gmm_impute", "title": "Multiple imputation by Gaussian-mixture conditioning", "concept": [ "imputation" ], "topics": [ "gmm_impute" ] }, { "page": "gmm_independence_graph", "title": "Conditional-independence (Gaussian graphical model) structure of a mixture", "concept": [ "diagnostics" ], "topics": [ "gmm_independence_graph" ] }, { "page": "gmm_intervene", "title": "Interventional law of a Gaussian mixture (the do-operator)", "concept": [ "decision", "operators" ], "topics": [ "gmm_intervene" ] }, { "page": "gmm_kld", "title": "Kullback-Leibler divergence between two Gaussian mixtures", "concept": [ "ops" ], "topics": [ "gmm_kld" ] }, { "page": "gmm_marginalise", "title": "Marginal of a Gaussian mixture", "concept": [ "ops" ], "topics": [ "gmm_marginalise" ] }, { "page": "gmm_mean", "title": "Mean and covariance of a Gaussian mixture", "concept": [ "ops" ], "topics": [ "gmm_cov", "gmm_mean" ] }, { "page": "gmm_missing", "title": "Condition a Gaussian mixture on the exact values of some coordinates", "concept": [ "operators" ], "topics": [ "gmm_missing" ] }, { "page": "gmm_mix", "title": "Mix Gaussian mixtures into one mixture", "concept": [ "operators" ], "topics": [ "gmm_mix" ] }, { "page": "gmm_modes", "title": "Modes of a Gaussian mixture", "concept": [ "ops" ], "topics": [ "gmm_modes" ] }, { "page": "gmm_mutual_information", "title": "Cauchy-Schwarz mutual information between two coordinate blocks", "concept": [ "diagnostics" ], "topics": [ "gmm_mutual_information" ] }, { "page": "gmm_n_components", "title": "Number of components in a Gaussian mixture", "concept": [ "classes" ], "topics": [ "gmm_n_components" ] }, { "page": "gmm_observe", "title": "Bayesian update of a Gaussian mixture on a noisy linear observation", "concept": [ "operators" ], "topics": [ "gmm_observe" ] }, { "page": "gmm_product", "title": "Pointwise product of two Gaussian mixtures", "concept": [ "operators" ], "topics": [ "gmm_product" ] }, { "page": "gmm_reduce", "title": "Reduce a Gaussian mixture to fewer components", "concept": [ "operators" ], "topics": [ "gmm_reduce" ] }, { "page": "gmm_target", "title": "A target density on R^p", "concept": [ "classes" ], "topics": [ "gmm_target" ] }, { "page": "gmm_target_from_posterior", "title": "Compile an unnormalised Bayesian posterior into a 'gmm_target'", "concept": [ "interop" ], "topics": [ "gmm_target_from_posterior", "gmm_target_from_posterior.default", "gmm_target_from_posterior.function" ] }, { "page": "gmm_target_from_samples", "title": "Build a target from samples alone", "concept": [ "targets" ], "topics": [ "gmm_target_from_samples" ] }, { "page": "gmm_weights", "title": "Component parameters of a Gaussian mixture", "concept": [ "classes" ], "topics": [ "gmm_covariances", "gmm_means", "gmm_weights" ] }, { "page": "hellinger_mc", "title": "Monte-Carlo Hellinger distance between a fit and its target", "concept": [ "diagnostics" ], "topics": [ "hellinger_mc" ] }, { "page": "init_kmeans", "title": "k-means initialisation", "concept": [ "init" ], "topics": [ "init_kmeans" ] }, { "page": "init_moment_seed", "title": "Moment-seed initialisation", "concept": [ "init" ], "topics": [ "init_moment_seed" ] }, { "page": "init_random", "title": "Random initialisation", "concept": [ "init" ], "topics": [ "init_random" ] }, { "page": "init_warm_start", "title": "Warm-start initialisation from an existing fit", "concept": [ "init" ], "topics": [ "init_warm_start" ] }, { "page": "is_mvn", "title": "Multivariate-normal proposal", "concept": [ "proposals" ], "topics": [ "is_mvn" ] }, { "page": "is_mvt", "title": "Multivariate-t proposal", "concept": [ "proposals" ], "topics": [ "is_mvt" ] }, { "page": "is_proposal", "title": "An importance-sampling proposal", "concept": [ "classes" ], "topics": [ "is_proposal" ] }, { "page": "is_uniform", "title": "Uniform-on-a-box proposal", "concept": [ "proposals" ], "topics": [ "is_uniform" ] }, { "page": "kld_trace", "title": "Per-iteration KLD trace of a fit", "concept": [ "diagnostics" ], "topics": [ "kld_trace" ] }, { "page": "maxent_target", "title": "Maximum-entropy target under moment and support constraints", "concept": [ "targets" ], "topics": [ "maxent_target" ] }, { "page": "mechanism", "title": "Missingness mechanisms for multiple imputation", "concept": [ "imputation" ], "topics": [ "censored", "mar", "mechanism", "mnar" ] }, { "page": "mixture_target", "title": "Three-component Gaussian-mixture target", "concept": [ "targets" ], "topics": [ "mixture_target" ] }, { "page": "multi_start_best_of", "title": "Multi-start best-of wrapper", "concept": [ "init" ], "topics": [ "multi_start_best_of" ] }, { "page": "pgmm", "title": "Distribution and quantile functions of a one-dimensional mixture", "concept": [ "ops" ], "topics": [ "pgmm", "qgmm" ] }, { "page": "proposal_uniform", "title": "Preferred names for the importance-proposal constructors", "concept": [ "proposals" ], "topics": [ "proposal_mvn", "proposal_mvt", "proposal_uniform" ] }, { "page": "proxy_cate", "title": "Heterogeneous treatment effects (CATE / uplift)", "concept": [ "decision" ], "topics": [ "proxy_cate" ] }, { "page": "proxy_confounding_gap", "title": "Confounding gap: the sensitivity of the effect to the latent regime", "concept": [ "decision" ], "topics": [ "proxy_confounding_gap" ] }, { "page": "proxy_decide", "title": "Optimal action and expected incremental value per unit", "concept": [ "decision" ], "topics": [ "proxy_decide" ] }, { "page": "proxy_fmi", "title": "Fraction of missing information for a column mean", "concept": [ "imputation" ], "topics": [ "proxy_fmi" ] }, { "page": "proxy_functional_ci", "title": "Percentile interval for any functional of a fitted proxy", "concept": [ "diagnostics" ], "topics": [ "proxy_functional_ci" ] }, { "page": "proxy_identification_report", "title": "The identification report (an executive one-pager)", "concept": [ "decision" ], "topics": [ "proxy_identification_report" ] }, { "page": "proxy_mnar_sensitivity", "title": "Missing-not-at-random sensitivity analysis for a coordinate mean", "concept": [ "imputation" ], "topics": [ "proxy_mnar_sensitivity" ] }, { "page": "proxy_overlap", "title": "Per-unit overlap / positivity diagnostic", "concept": [ "decision" ], "topics": [ "proxy_overlap" ] }, { "page": "proxy_policy_value", "title": "Off-line value of a targeting policy", "concept": [ "decision" ], "topics": [ "proxy_policy_value" ] }, { "page": "proxy_pool", "title": "Pool a column mean across imputations", "concept": [ "imputation" ], "topics": [ "proxy_pool" ] }, { "page": "proxy_predict", "title": "Predicted outcome under a treatment (the seeing rung)", "concept": [ "decision" ], "topics": [ "proxy_predict" ] }, { "page": "proxy_regime_segments", "title": "The fitted regimes as an interpretable segment table", "concept": [ "decision" ], "topics": [ "proxy_regime_segments" ] }, { "page": "proxy_retrospective_uplift", "title": "Retrospective (counterfactual-mean) uplift for observed units", "concept": [ "decision" ], "topics": [ "proxy_retrospective_uplift" ] }, { "page": "proxy_uplift", "title": "Uplift (alias of 'proxy_cate()' for a binary treatment)", "concept": [ "decision" ], "topics": [ "proxy_uplift" ] }, { "page": "rgmm", "title": "Sample from a Gaussian mixture", "concept": [ "ops" ], "topics": [ "rgmm" ] }, { "page": "select_N", "title": "Select the number of mixture components", "concept": [ "fitting" ], "topics": [ "select_N" ] }, { "page": "tidy.gmm", "title": "Tidy a Gaussian mixture into a component table", "concept": [ "classes" ], "topics": [ "tidy.gmm" ] }, { "page": "uplift_identification", "title": "Identification-report object", "concept": [ "decision" ], "topics": [ "uplift_identification" ] }, { "page": "uplift_model", "title": "A fitted uplift / next-best-action model", "concept": [ "decision" ], "topics": [ "uplift_model" ] } ], "_pkglogo": "https://github.com/max578/proxymix/raw/main/man/figures/logo.png", "_readme": "https://github.com/max578/proxymix/raw/main/README.md", "_rundeps": [ "BH", "cli", "data.table", "mvnfast", "Rcpp", "RcppArmadillo", "rlang", "S7", "withr" ], "_vignettes": [ { "source": "three_regimes.Rmd", "filename": "three_regimes.html", "title": "Choosing between the three fitting regimes", "engine": "knitr::rmarkdown", "headings": [ "The problem", "Package capabilities", "Addressing the problem", "A target with both a formula and a sample", "Method (i): one normal distribution with the sample's mean and spread", "Method (ii): several normal distributions fitted to the sample", "Method (iii): several normal distributions fitted to the formula", "The three fits side by side", "What each method improves in each round", "One normal distribution fitted two ways, against the exact answer", "Comparison with mclust, mixtools and flexmix", "Interpretation", "Limitations", "Further reading", "References", "Reproduce" ], "created": "2026-05-14 08:05:40", "modified": "2026-09-26 17:26:48", "commits": 6 }, { "source": "posterior_proxy.Rmd", "filename": "posterior_proxy.html", "title": "Compressing a Bayesian posterior you can evaluate but not sample", "engine": "knitr::rmarkdown", "headings": [ "The problem", "Package capabilities", "Addressing the problem", "A posterior with no sampler", "Fit the proxy", "Check the fit before using it", "The normalising constant", "Read the answers off the proxy", "Check the answers against exact integration", "Error bars on the proxy itself", "Comparison with Stan, INLA, a Laplace approximation and BayesianTools", "Interpretation", "Limitations", "Further reading", "References", "Reproduce" ], "created": "2026-07-05 01:43:32", "modified": "2026-09-26 17:26:48", "commits": 4 }, { "source": "from_kde.Rmd", "filename": "from_kde.html", "title": "Compressing a kernel density estimate into a mixture", "engine": "knitr::rmarkdown", "headings": [ "The problem", "Package capabilities", "Addressing the problem", "A sample from two groups", "The recovered groups", "Check the fit", "What the compression loses", "Use the proxy", "The effect of the bandwidth", "Comparison with ks, np, KernSmooth, mclust and mixtools", "Interpretation", "Limitations", "Further reading", "References", "Reproduce" ], "created": "2026-05-14 08:05:40", "modified": "2026-09-26 17:26:48", "commits": 8 }, { "source": "quickstart.Rmd", "filename": "quickstart.html", "title": "Fitting a proxy to a density you cannot sample", "engine": "knitr::rmarkdown", "headings": [ "The problem", "Package capabilities", "Addressing the problem", "Which fitting method applies", "A target with no sampler", "Fit the proxy", "Check the fit before using it", "Compare the proxy with the target", "Use the proxy", "Comparison with the Laplace approximation, Stan and BayesianTools", "Interpretation", "Limitations", "Further reading", "References", "Reproduce" ], "created": "2026-05-14 08:05:40", "modified": "2026-09-27 04:08:10", "commits": 8 }, { "source": "density_shapes.Rmd", "filename": "density_shapes.html", "title": "How well a mixture proxies four awkward shapes", "engine": "knitr::rmarkdown", "headings": [ "The problem", "Package capabilities", "Addressing the problem", "Fit the three shapes that cover the whole plane", "Compare each proxy with its target", "Measure each fit in two ways", "Give the ring more components and more trial points", "A density with hard edges", "When the trial points miss the target", "Interpretation", "Limitations", "Further reading", "References", "Reproduce" ], "created": "2026-05-14 08:05:40", "modified": "2026-09-26 17:26:48", "commits": 8 }, { "source": "missing_data.Rmd", "filename": "missing_data.html", "title": "Imputing missing data with a mixture", "engine": "knitr::rmarkdown", "headings": [ "The problem", "Package capabilities", "Addressing the problem", "Data with two groups and known missing values", "Impute with a mixture and with a single normal distribution", "Where the imputed values fall", "Pool the mean of x2", "Pool a regression slope with mice", "Comparison with mice and Amelia", "Interpretation", "Limitations", "Further reading", "References", "Reproduce" ], "created": "2026-06-20 15:17:48", "modified": "2026-09-26 17:26:48", "commits": 6 }, { "source": "calibration.Rmd", "filename": "calibration.html", "title": "Mapping the optima of an objective", "engine": "knitr::rmarkdown", "headings": [ "The problem", "Package capabilities", "Addressing the problem", "From an objective to a distribution", "Two minima in one dimension", "Check the fit before using it", "Four minima in two dimensions", "Interpretation", "Limitations", "Further reading", "References", "Reproduce" ], "created": "2026-06-19 03:52:00", "modified": "2026-09-26 17:26:48", "commits": 6 }, { "source": "missing_data_mnar.Rmd", "filename": "missing_data_mnar.html", "title": "Missing data that depends on the missing value", "engine": "knitr::rmarkdown", "headings": [ "The problem", "Package capabilities", "Addressing the problem", "Data in which the large values go missing", "Impute under two assumptions", "Sweep the assumed slope", "Censoring at a known limit", "Comparison with mice, Amelia, sampleSelection and the Tobit model", "Interpretation", "Limitations", "Further reading", "References", "Reproduce" ], "created": "2026-06-21 21:37:49", "modified": "2026-09-29 21:04:01", "commits": 7 }, { "source": "many_methods.Rmd", "filename": "many_methods.html", "title": "One mixture, many methods", "engine": "knitr::rmarkdown", "headings": [ "The problem", "Package capabilities", "Addressing the problem", "Regression: lm()", "Kernel regression: Nadaraya-Watson", "Clustering: kmeans()", "Principal components: prcomp()", "Penalised regression: ridge", "Treatment effects: a separate model for each arm", "Other uses", "Interpretation", "Limitations", "Further reading", "References", "Reproduce" ], "created": "2026-06-17 04:14:12", "modified": "2026-09-26 17:26:48", "commits": 7 }, { "source": "entropy.Rmd", "filename": "entropy.html", "title": "Reading the entropy of a fitted mixture", "engine": "knitr::rmarkdown", "headings": [ "The problem", "Package capabilities", "Addressing the problem", "Why some quantities are exact", "Entropy of a mixture", "Distance between two mixtures", "Dependence between variables", "Cooling the fit", "Maximum-entropy targets", "Choosing the number of components", "Which variables are related", "Comparison with FNN, mclust and pcalg", "Interpretation", "Limitations", "Further reading", "References", "Reproduce" ], "created": "2026-06-17 22:39:59", "modified": "2026-09-27 04:08:10", "commits": 11 }, { "source": "end_of_sample.Rmd", "filename": "end_of_sample.html", "title": "Testing the last observation for instability", "engine": "knitr::rmarkdown", "headings": [ "The problem", "Package capabilities", "Addressing the problem", "A model and a stable series", "The same series with a broken final value", "What the filter expected", "How the subsampling p-value is reached", "How often each calibration rejects when the model is known", "Comparison with strucchange, changepoint, KFAS and dlm", "Interpretation", "Limitations", "Further reading", "References", "Reproduce" ], "created": "2026-06-21 07:49:17", "modified": "2026-09-28 08:58:33", "commits": 7 }, { "source": "operator_calculus.Rmd", "filename": "operator_calculus.html", "title": "The closed-form operator calculus on a mixture", "engine": "knitr::rmarkdown", "headings": [ "The problem", "Package capabilities", "Addressing the problem", "A mixture to work with", "Pass the mixture through a sensor", "Update after a noisy measurement", "Check the update against the Kalman formulas", "Add variables together", "Condition on values known exactly", "Two measurements, in turn or together", "Track an object over time", "Keep the number of components small", "The whole filter in one call", "Comparison with dlm, KFAS and particle filters", "Interpretation", "Limitations", "Further reading", "References", "Reproduce" ], "created": "2026-05-14 08:05:40", "modified": "2026-09-26 17:26:48", "commits": 13 } ], "_score": 5.89209460269048, "_indexed": true, "_nocasepkg": "proxymix", "_universes": [ "max578" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.16.0.9000", "date": "2026-10-17T04:56:39.000Z", "distro": "resolute", "commit": "b66ddbbae18b2907f441e82427c72c6b52346841", "fileid": "https://r2.ropensci.org/b9490aebf9c5f05763c8cb292134e64dda44a67efb28aaf219137aaa92a22254", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/max578/actions/runs/39003908812" }, { "r": "4.6.1", "os": "linux", "version": "0.16.0.9000", "date": "2026-10-17T04:56:43.000Z", "distro": "resolute", "commit": "b66ddbbae18b2907f441e82427c72c6b52346841", "fileid": "https://r2.ropensci.org/d24a87bc1d160a3ec2152d8aa17fda596f9182d7b85aee5b4777186aed5b996e", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/max578/actions/runs/39003908812" }, { "r": "4.5.3", "os": "mac", "version": "0.16.0.9000", "date": "2026-10-17T04:56:01.000Z", "commit": "b66ddbbae18b2907f441e82427c72c6b52346841", "fileid": "https://r2.ropensci.org/f0ef3c9e31bf64ba9c5b6cfa943b2956155bb92fd6fd50a7128dfac13439d441", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/max578/actions/runs/39003908812" }, { "r": "4.6.1", "os": "mac", "version": "0.16.0.9000", "date": "2026-10-17T04:56:17.000Z", "commit": "b66ddbbae18b2907f441e82427c72c6b52346841", "fileid": "https://r2.ropensci.org/9e29742e66423cee9f887e6aaeaa5b6d511d2f1f865604d9b6b613748eadb531", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/max578/actions/runs/39003908812" }, { "r": "4.6.0", "os": "wasm", "version": "0.16.0.9000", "date": "2026-10-17T04:56:33.000Z", "commit": "b66ddbbae18b2907f441e82427c72c6b52346841", "fileid": "https://r2.ropensci.org/58f364e413c98a337ac7db0f76d7a464e6b332c1e76f6b98d0927e0c02374d7f", "status": "success", "buildurl": "https://github.com/r-universe/max578/actions/runs/39003908812" }, { "r": "4.7.0", "os": "win", "version": "0.16.0.9000", "date": "2026-10-17T04:56:09.000Z", "commit": "b66ddbbae18b2907f441e82427c72c6b52346841", "fileid": "https://r2.ropensci.org/f174936a706f2429b93d4d66b6b78ddf32ed51e749e417aaabbf4e14a23a4675", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/max578/actions/runs/39003908812" }, { "r": "4.5.3", "os": "win", "version": "0.16.0.9000", "date": "2026-10-17T04:55:53.000Z", "commit": "b66ddbbae18b2907f441e82427c72c6b52346841", "fileid": "https://r2.ropensci.org/fb78564433025baa1e63ff52522b066d59eb11aff998d55de2bac0575938df06", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/max578/actions/runs/39003908812" }, { "r": "4.6.1", "os": "win", "version": "0.16.0.9000", "date": "2026-10-17T04:55:58.000Z", "commit": "b66ddbbae18b2907f441e82427c72c6b52346841", "fileid": "https://r2.ropensci.org/63ea42433cbcd0f63370d3257f82cac35bc81d39119ef4a6443fbe60c86b6389", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/max578/actions/runs/39003908812" } ] }