{ "_id": "6acfda39e2d863f66295389b", "Package": "fastsae", "Type": "Package", "Title": "High-Performance Frequentist and Bayesian Small Area Estimation\nin R", "Version": "1.0.0", "Authors@R": "c(\nperson(\"Ridson\", \"Al Farizal P\", role = c(\"aut\", \"cre\",\"cph\"), email = \"alfrzlp@gmail.com\", comment = c(ORCID = \"0000-0003-0617-0214\")),\nperson(\"Azka\", \"Ubaidillah\", role = \"aut\", email = \"azka@stis.ac.id\", comment = c(ORCID = \"0000-0002-3597-0459\"))\n)", "Description": "High-performance Small Area Estimation (SAE) in R,\nimplementing frequentist EBLUP models via compiled C++ and\nBayesian hierarchical models via 'INLA' (Integrated Nested\nLaplace Approximations) or, optionally, full MCMC with\n'NumPyro' (Phan et al., 2019) \non 'JAX' (CPU or NVIDIA GPU). Frequentist models include the\narea-level Fay-Herriot model (Fay and Herriot, 1979\n), spatial Fay-Herriot\n(Pratesi and Salvati, 2008 ),\nspatio-temporal Fay-Herriot (Marhuenda et al., 2013\n), unit-level\nBattese-Harter-Fuller (Battese et al., 1988\n), and two-fold subarea\nmodels (Torabi and Rao, 2014 )\nfor nested area-subarea hierarchies. Bayesian models use 'INLA'\n(Rue et al., 2009 ),\nsupporting 6 likelihood families (Gaussian, Beta, Binomial,\nPoisson, Negative Binomial, Gamma), spatial structures (BYM2,\nBesag ICAR), temporal dynamics (RW1, RW2, AR1), and\nspatio-temporal interactions. Key features include analytical\nand bootstrap MSE estimation, benchmarking to aggregate targets\n(Rao and Molina, 2015 ), automatic\nhandling of unsampled domains, and S3 methods for summaries,\ndiagnostics and plots.", "License": "GPL (>= 3)", "URL": "https://ridsonap.github.io/fastsae/,\nhttps://github.com/ridsonap/fastsae", "BugReports": "https://github.com/ridsonap/fastsae/issues", "Encoding": "UTF-8", "Language": "en-US", "LazyData": "true", "Additional_repositories": "https://inla.r-inla-download.org/R/stable", "VignetteBuilder": "knitr", "SystemRequirements": "Python (>= 3.10) with 'jax' and 'numpyro'\n(optional, for hb_area(engine = \"jax\"); see\nsetup_numpyro_env())", "Collate": "utils.R inla_utils.R RcppExports.R data.R methods.R autoplot.R\ngvf.R create_weights.R eblup_fh.R eblup_sfh.R eblup_stfh.R\neblup_twofold.R eblup_bhf.R engine.R hb_area.R hb_area_jax.R\npython_env.R hb_unit.R hb_twofold.R sim_spatial_weights.R\nsim_area_data.R sim_series_data.R diagnose.R spatial_test.R\nbenchmark.R compare_sae.R export_sae.R map_sae.R", "Config/roxygen2/version": "8.0.0", "Config/testthat/edition": "3", "Config/pak/sysreqs": "python3", "Repository": "https://ridsonap.r-universe.dev", "Date/Publication": "2026-10-14 16:01:21 UTC", "RemoteUrl": "https://github.com/ridsonap/fastsae", "RemoteRef": "HEAD", "RemoteSha": "7e20210fc30d18a611fd8375d32e731aa4195180", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-10-14 18:59:03 UTC", "User": "root" }, "Author": "Ridson Al Farizal P [aut, cre, cph] (ORCID:\n),\nAzka Ubaidillah [aut] (ORCID: )", "Maintainer": "Ridson Al Farizal P ", "_user": "ridsonap", "_type": "src", "_file": "fastsae_1.0.0.tar.gz", "_fileid": "https://r2.ropensci.org/ecf6b885c26bd685cf1e227edc019317e572d076cc6b2fab3a7a20acfbc99b4f", "_filesize": 2379960, "_sha256": "ecf6b885c26bd685cf1e227edc019317e572d076cc6b2fab3a7a20acfbc99b4f", "_expires": "2027-01-22T19:38:31.000Z", "_created": "2026-10-14T18:59:03.000Z", "_published": "2026-10-15T11:49:41.375Z", "_buildurl": "https://github.com/r-universe/ridsonap/actions/runs/38618596328", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 116045302129, "time": 326, "config": "linux-devel-arm64", "r": "4.7.0", "check": "ERROR", "artifact": "11921857029" }, { "job": 116045301615, "time": 592, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11921338509" }, { "job": 116045301471, "time": 403, "config": "linux-release-arm64", "r": "4.6.1", "check": "ERROR", "artifact": "11922675803" }, { "job": 116045301482, "time": 639, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11922137930" }, { "job": 116045301402, "time": 320, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "ERROR", "artifact": "11922029380" }, { "job": 116045301400, "time": 759, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "ERROR", "artifact": "11923085675" }, { "job": 116045301390, "time": 948, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "11922319619" }, { "job": 116045301378, "time": 1566, "config": "macos-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11923432295" }, { "job": 116040090102, "time": 637, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "11921083235" }, { "job": 116045301096, "time": 362, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "11921617302" }, { "job": 116045301431, "time": 498, "config": "windows-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "11922172385" }, { "job": 116045301593, "time": 684, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11921577962" }, { "job": 116045301756, "time": 891, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "11922438297" }, { "job": 116045301436, "time": 446, "config": "windows-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "11921532634" }, { "job": 116045301732, "time": 505, "config": "windows-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11922522170" } ], "_upstream": "https://github.com/ridsonap/fastsae", "_commit": { "id": "7e20210fc30d18a611fd8375d32e731aa4195180", "author": "Alfrzlp ", "committer": "Alfrzlp ", "message": "feat(eblup_stfh): unsampled domains and missing periods\n\neblup_stfh() no longer requires a complete panel. Cells with an NA response\n(missing periods of a sampled domain, or domains never sampled) stay in the\ndata and are predicted:\n\n- The model lives on the full D x T grid (SAR effect on all domains of W,\n AR(1) effect on all periods). REML uses the sampled cells, i.e. the\n marginal likelihood with the missing cells integrated out; every cell gets\n the BLUP given the sampled data (Saei & Chambers 2005; Benedetti et al.\n 2024): AR(1) + SAR borrowing for gaps, spatial kriging for unsampled\n domains.\n- C++ core: per-domain block layout (offsets and observed periods) instead of\n the fixed T stride; T x T matrices are sliced per block. Complete panels\n follow exactly the previous arithmetic and are bit-for-bit identical\n (checked against a clean build of main, analytical and bootstrap MSE).\n- MSE: sampled cells keep g1 + g2 + 2 g3 - g4; other cells get g1 + g2 + g3\n (g3 from the exact derivative of the predictor). The plug-in second-order\n correction was implemented and verified, but is numerically unstable near\n rho1 = +-1 or with weakly identified variances; without it the Monte Carlo\n relative bias is -1% (gaps) and +3% (unsampled domains) with ~95% coverage.\n The bootstrap draws the effects on the full grid, refits on the sampled\n cells and covers every cell (RB 2-3%).\n- R: NA responses accepted; covariates must be complete, vardir positive in\n sampled cells, W must keep unsampled domains. df_eblup gains `sampled`;\n print()/summary() report the predicted cells. Examples and the vignette\n use the full mys_panel / mys_proxmat.\n- Tests: dense reference implementation (BLUP, g1-g4, REML score), edge\n cases, row order, bootstrap thread invariance, utilities.\n- benchmarks/sim_stfh_unsampled.R (Monte Carlo); technical report section\n and generated simulation table, CODE_MAP, DISCREPANCIES (STFH-9), R Journal\n sentence; NEWS.\n\nCo-Authored-By: Claude Opus", "time": 1791993681 }, "_maintainer": { "name": "Ridson Al Farizal P", "email": "alfrzlp@gmail.com", "login": "ridsonap", "description": "Manusia biasa yang biasa2 aja", "uuid": 74123953, "orcid": "0000-0003-0617-0214" }, "_distro": "resolute", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1.0", "role": "Depends" }, { "package": "Rcpp", "role": "LinkingTo" }, { "package": "RcppArmadillo", "role": "LinkingTo" }, { "package": "RcppEigen", "role": "LinkingTo" }, { "package": "cli", "role": "Imports" }, { "package": "ggplot2", "role": "Imports" }, { "package": "Matrix", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "Rcpp", "role": "Imports" }, { "package": "rlang", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "covr", "role": "Suggests" }, { "package": "doParallel", "role": "Suggests" }, { "package": "dplyr", "role": "Suggests" }, { "package": "emdi", "role": "Suggests" }, { "package": "foreach", "role": "Suggests" }, { "package": "INLA", "role": "Suggests" }, { "package": "lme4", "role": "Suggests" }, { "package": "MASS", "role": "Suggests" }, { "package": "parallel", "role": "Suggests" }, { "package": "reticulate", "role": "Suggests" }, { "package": "sae", "role": "Suggests" }, { "package": "spdep", "role": "Suggests" }, { "package": "spam", "role": "Suggests" }, { "package": "sf", "role": "Suggests" }, { "package": "sn", "role": "Suggests" }, { "package": "tipsae", "role": "Suggests" }, { "package": "rstantools", "role": "Suggests" }, { "package": "writexl", "role": "Suggests" }, { "package": "openxlsx", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" } ], "_owner": "ridsonap", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2026-37", "n": 3 }, { "week": "2026-38", "n": 10 }, { "week": "2026-39", "n": 27 }, { "week": "2026-40", "n": 34 }, { "week": "2026-41", "n": 2 }, { "week": "2026-42", "n": 37 } ], "_tags": [], "_topics": [ "cpp", "inla", "openmp", "small-area-estimation", "openblas", "cpp" ], "_stars": 1, "_contributors": [ { "user": "ridsonap", "count": 104, "uuid": 74123953 } ], "_userbio": { "uuid": 74123953, "type": "user", "name": "Ridson Al Farizal P", "followers": 0, "description": "Manusia biasa yang biasa2 aja" }, "_downloads": { "count": 309, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/fastsae" }, "_devurl": "https://github.com/ridsonap/fastsae", "_pkgdown": "https://ridsonap.github.io/fastsae/", "_searchresults": 0, "_rbuild": "4.6.1", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/fastsae.html", "extra/manual.pdf", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md" ], "_homeurl": "https://github.com/ridsonap/fastsae", "_realowner": "ridsonap", "_cranurl": true, "_releases": [ { "version": "0.1.0", "date": "2026-09-30" } ], "_exports": [ "add_reliability_flags", "autoplot", "benchmark_sae", "check_numpyro_available", "compare_sae", "create_weights", "diagnose", "eblup_bhf", "eblup_fh", "eblup_sfh", "eblup_stfh", "eblup_twofold", "export_sae", "gvf_smooth", "hb_area", "hb_twofold", "hb_unit", "inla_engine", "jax_engine", "map_sae", "setup_numpyro_env", "sim_area_data", "sim_series_data", "sim_spatial_weights", "spatial_test" ], "_datasets": [ { "name": "cornsoybean", "title": "Corn and Soybean Survey and Satellite Data in 12 Iowa Counties", "object": "cornsoybean", "class": [ "data.frame" ], "fields": [ "County", "CornHec", "SoyBeansHec", "CornPix", "SoyBeansPix" ], "rows": 37, "table": true, "tojson": true }, { "name": "cornsoybeanmeans", "title": "Corn and Soybean Mean Number of Pixels per Segment for 12 Iowa Counties", "object": "cornsoybeanmeans", "class": [ "data.frame" ], "fields": [ "CountyIndex", "CountyName", "SampSegments", "PopnSegments", "MeanCornPixPerSeg", "MeanSoyBeansPixPerSeg" ], "rows": 12, "table": true, "tojson": true }, { "name": "mys", "title": "mys: mean years of schooling people with disabilities.", "object": "mys", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "area", "y", "vardir", "rse", "x1", "x2", "x3", "n", "weight" ], "rows": 42, "table": true, "tojson": true }, { "name": "mys_panel", "title": "mys_panel: mean years of schooling people with disabilities 2016 - 2026.", "object": "mys_panel", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "area", "year", "y", "vardir", "rse", "x1", "x2", "x3", "n", "weight" ], "rows": 462, "table": true, "tojson": true }, { "name": "mys_proxmat", "title": "Example proximity matrix", "object": "mys_proxmat", "class": [ "matrix", "array" ], "fields": [ "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42" ], "rows": 42, "table": true, "tojson": true }, { "name": "sim_area", "title": "Multi-Distribution Synthetic Area-Level Dataset", "object": "sim_area", "class": [ "data.frame" ], "fields": [ "area", "x1", "x2", "y_gaussian", "vardir", "y_poisson", "exposure", "y_binomial", "trials", "y_beta", "y_nbinomial", "y_gamma", "x_coord", "y_coord" ], "rows": 42, "table": true, "tojson": true }, { "name": "sim_panel", "title": "Multi-Distribution Spatio-Temporal Panel Dataset", "object": "sim_panel", "class": [ "data.frame" ], "fields": [ "area", "year", "x1", "x2", "y_gaussian", "vardir", "y_poisson", "exposure", "y_binomial", "trials", "y_beta", "y_nbinomial", "y_gamma", "x_coord", "y_coord" ], "rows": 210, "table": true, "tojson": true } ], "_help": [ { "page": "add_reliability_flags", "title": "Add Official Statistical Reliability Flags", "topics": [ "add_reliability_flags" ] }, { "page": "autoplot.fastsae", "title": "Autoplot Method for fastsae Objects", "topics": [ "autoplot.fastsae", "autoplot.list" ] }, { "page": "autoplot.fastsae_diagnose", "title": "Autoplot Method for fastsae_diagnose Objects", "topics": [ "autoplot.fastsae_diagnose" ] }, { "page": "benchmark_sae", "title": "Benchmark Small Area Estimation Predictions to Aggregate Targets", "topics": [ "autoplot.fastsae_benchmark", "benchmark_sae", "benchmark_sae.default", "benchmark_sae.fastsae", "plot.fastsae_benchmark", "print.fastsae_benchmark", "summary.fastsae_benchmark" ] }, { "page": "check_numpyro_available", "title": "Check whether the NumPyro / JAX engine is available", "topics": [ "check_numpyro_available" ] }, { "page": "coef.fastsae", "title": "Extract coefficients from a fastsae object", "topics": [ "coef.fastsae" ] }, { "page": "compare_sae", "title": "Compare Two Small Area Estimation Models", "topics": [ "autoplot.fastsae_comparison", "compare_sae", "compare_sae.default", "plot.fastsae_comparison", "print.fastsae_comparison", "summary.fastsae_comparison" ] }, { "page": "cornsoybean", "title": "Corn and Soybean Survey and Satellite Data in 12 Iowa Counties", "topics": [ "cornsoybean" ] }, { "page": "cornsoybeanmeans", "title": "Corn and Soybean Mean Number of Pixels per Segment for 12 Iowa Counties", "topics": [ "cornsoybeanmeans" ] }, { "page": "create_weights", "title": "Create Spatial Proximity and Weights Matrix", "topics": [ "create_weights" ] }, { "page": "diagnose", "title": "Diagnostic Evaluation of Small Area Estimation Models", "topics": [ "diagnose" ] }, { "page": "eblup_bhf", "title": "Empirical Best Linear Unbiased Prediction (EBLUP) for the Battese-Harter-Fuller Model", "topics": [ "eblup_bhf" ] }, { "page": "eblup_fh", "title": "Empirical Best Linear Unbiased Prediction based on a Fay-Herriot Model.", "topics": [ "eblup_fh" ] }, { "page": "eblup_sfh", "title": "Empirical Best Linear Unbiased Prediction based on a Spatial Fay-Herriot Model.", "topics": [ "eblup_sfh" ] }, { "page": "eblup_stfh", "title": "Empirical Best Linear Unbiased Prediction based on a Spatio-Temporal Fay-Herriot Model.", "topics": [ "eblup_stfh" ] }, { "page": "eblup_twofold", "title": "Empirical Best Linear Unbiased Prediction under a Two-fold Fay-Herriot Model.", "topics": [ "eblup_twofold" ] }, { "page": "export_sae", "title": "Export Small Area Estimation Results to Excel or CSV", "topics": [ "export_sae" ] }, { "page": "fitted.fastsae", "title": "Extract fitted values (EBLUP or HB) from a fastsae object", "topics": [ "fitted.fastsae" ] }, { "page": "gvf_smooth", "title": "Generalized Variance Function (GVF) Smoothing for Sampling Variances", "topics": [ "gvf_smooth" ] }, { "page": "hb_area", "title": "Hierarchical Bayes for Area-Level Small Area Estimation", "topics": [ "hb_area" ] }, { "page": "hb_engine", "title": "Estimation engines for the Hierarchical Bayes models", "topics": [ "hb_engine", "inla_engine", "jax_engine", "print.fastsae_engine" ] }, { "page": "hb_twofold", "title": "Two-Fold Hierarchical Bayes for Sub-Area Level Small Area Estimation", "topics": [ "hb_twofold" ] }, { "page": "hb_unit", "title": "Hierarchical Bayes for Unit-Level Small Area Estimation", "topics": [ "hb_unit" ] }, { "page": "map_sae", "title": "Unified Spatial Choropleth Mapping for Small Area Estimates", "topics": [ "map_sae", "map_sae.default", "map_sae.fastsae", "map_sae.fastsae_benchmark", "map_sae.list" ] }, { "page": "mys", "title": "mys: mean years of schooling people with disabilities.", "topics": [ "mys" ] }, { "page": "mys_panel", "title": "mys_panel: mean years of schooling people with disabilities 2016 - 2026.", "topics": [ "mys_panel" ] }, { "page": "mys_proxmat", "title": "Example proximity matrix", "topics": [ "mys_proxmat" ] }, { "page": "plot.fastsae", "title": "Plot method for fastsae objects", "topics": [ "plot.fastsae" ] }, { "page": "print.fastsae", "title": "Print a fastsae object", "topics": [ "print.fastsae" ] }, { "page": "print.summary.fastsae", "title": "Print summary of a fastsae object", "topics": [ "print.summary.fastsae" ] }, { "page": "residuals.fastsae", "title": "Extract residuals from a fastsae object", "topics": [ "residuals.fastsae" ] }, { "page": "setup_numpyro_env", "title": "Set up the Python environment for the NumPyro / JAX engine", "topics": [ "setup_numpyro_env" ] }, { "page": "sim_area", "title": "Multi-Distribution Synthetic Area-Level Dataset", "topics": [ "sae_area_multi", "sim_area" ] }, { "page": "sim_area_data", "title": "Simulate Multi-Distribution Small Area Data with Spatial Autocorrelation", "topics": [ "sim_area_data" ] }, { "page": "sim_panel", "title": "Multi-Distribution Spatio-Temporal Panel Dataset", "topics": [ "sae_panel_multi", "sim_panel" ] }, { "page": "sim_series_data", "title": "Simulate Spatio-Temporal Multi-Distribution Small Area Data", "topics": [ "sim_series_data" ] }, { "page": "sim_spatial_weights", "title": "Simulate Spatial Proximity and Adjacency Matrices", "topics": [ "sim_spatial_weights" ] }, { "page": "spatial_test", "title": "Pre-Modeling Spatial Diagnostic and Autocorrelation Tests", "topics": [ "spatial_test" ] }, { "page": "summary.fastsae", "title": "Summarize a fastsae object", "topics": [ "summary.fastsae" ] } ], "_pkglogo": "https://github.com/ridsonap/fastsae/raw/HEAD/man/figures/logo.png", "_readme": "https://github.com/ridsonap/fastsae/raw/HEAD/README.md", "_rundeps": [ "cli", "cpp11", "farver", "ggplot2", "glue", "gtable", "isoband", "labeling", "lattice", "lifecycle", "Matrix", "R6", "RColorBrewer", "Rcpp", "RcppArmadillo", "RcppEigen", "rlang", "S7", "scales", "vctrs", "viridisLite", "withr" ], "_sysdeps": [ { "shlib": "liblapack", "package": "libopenblas0-pthread", "source": "openblas", "version": "0.3.32+ds-5", "name": "openblas", "homepage": "https://www.openblas.net/", "description": "Optimized BLAS (linear algebra) library (shared lib, pthread)" }, { "shlib": "libblas", "package": "libopenblas0-pthread", "source": "openblas", "version": "0.3.32+ds-5", "name": "openblas", "homepage": "https://www.openblas.net/", "description": "Optimized BLAS (linear algebra) library (shared lib, pthread)" }, { "shlib": "libstdc++", "package": "libstdc++6", "source": "gcc", "version": "16-20260322-1ubuntu1", "name": "c++", "homepage": "http://gcc.gnu.org/", "description": "GNU Standard C++ Library v3" }, { "shlib": "libgomp", "package": "libgomp1", "source": "gcc", "version": "16-20260322-1ubuntu1", "name": "openmp", "homepage": "http://gcc.gnu.org/", "description": "GCC OpenMP (GOMP) support library" } ], "_vignettes": [ { "source": "hb-area-inla.Rmd", "filename": "hb-area-inla.html", "title": "Bayesian Area-Level Small Area Estimation with INLA", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Supported Distribution Families", "Mathematical Formulation", "Level 1: Sampling Model (Likelihood)", "Level 2: Prior on Fixed Effects", "Level 3: Prior on Random Effects", "INLA Approximation", "Spatial and Spatio-Temporal Structures", "Spatial Priors", "Temporal Models", "Space-Time Interaction Structures (st_interaction)", "Case Study: Spatio-Temporal Beta SAE on Emilia-Romagna Poverty", "1. Load Data and Construct Spatial Adjacency Matrix", "2. Fit Spatio-Temporal Beta Model", "3. Inspect Posterior Predictions and Uncertainty", "Comparison and Validation Against tipsae (Stan MCMC)", "Interactive Scalability Explorer ($n = 30$ to $n = 1,000$)", "Inferential Equivalence & Validation Metrics ($n = 1,000$)", "Cross-Sectional Count Data: Poisson & Negative Binomial", "References" ], "created": "2026-09-29 10:58:10", "modified": "2026-10-13 18:18:05", "commits": 6 }, { "source": "finite-population-validation.Rmd", "filename": "finite-population-validation.html", "title": "Finite Population Simulation: Validating fastsae against Ground Truth", "engine": "knitr::rmarkdown", "headings": [ "Introduction: Why Finite Population Simulation?", "Simulation Architecture", "Mathematical Formulations of Empirical Properties", "Empirical Benchmark Results ($R = 100$ Replications, $D = 60$)", "Key Empirical Findings:", "National-Scale Benchmark ($D = 500$ Domains, $N \\approx 250{,}000$ Individuals, $R = 25$)", "Key National Scalability Takeaways:", "Visualizations", "1. Domain-Specific Empirical RMSE", "2. Empirical 95% Credible Interval Coverage", "How to Replicate", "References" ], "created": "2026-09-27 02:48:54", "modified": "2026-10-13 06:17:25", "commits": 6 }, { "source": "getting-started.Rmd", "filename": "getting-started.html", "title": "Getting Started with fastsae", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "The Fay-Herriot Model", "Step-by-Step Example", "1. Load Package and Dataset", "2. Fit Fay-Herriot Model (eblup_fh)", "3. Model Summary and Coefficients", "4. Diagnostic Plots (autoplot)", "Direct Estimates vs EBLUP", "Mean Squared Error (MSE) Across Domains", "Exact Numerical Equivalence with sae", "Next Steps", "References" ], "created": "2026-09-21 03:29:21", "modified": "2026-09-29 10:58:10", "commits": 4 }, { "source": "model-diagnostics.Rmd", "filename": "model-diagnostics.html", "title": "Model Diagnostics and Residual Analysis", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Core Diagnostic Criteria", "Practical Example: Diagnosing a Fay-Herriot Model", "1. Fit Model and Run diagnose()", "2. Inspect Diagnostic Components", "Visual Diagnostics with autoplot()", "1. Calibration Plot (type = \"calibration\")", "2. Precision Comparison (type = \"rse\")", "3. Residual Diagnostics (type = \"residuals\" & type = \"qq\")", "4. Combined Diagnostic Dashboard (type = \"all\")", "Diagnosing Bayesian Spatio-Temporal Models", "References" ], "created": "2026-09-26 08:33:43", "modified": "2026-10-13 19:40:40", "commits": 4 }, { "source": "benchmarks.Rmd", "filename": "benchmarks.html", "title": "Performance & Scalability Benchmarks", "engine": "knitr::rmarkdown", "headings": [ "Executive Summary", "Benchmark Results Table", "Interactive Benchmark Explorer", "Architectural Insights: Why is fastsae so Fast?", "Bayesian Spatio-Temporal Benchmark: fastsae (INLA) vs tipsae (Stan MCMC)", "Interactive Beta SAE Benchmark Explorer ($n = 30$ to $n = 1,000$)", "Inferential Equivalence & Validation Metrics ($n = 1,000$)", "Finite Population Simulation & Ground Truth Validation" ], "created": "2026-09-21 03:29:21", "modified": "2026-10-13 18:18:05", "commits": 17 }, { "source": "spatial-temporal.Rmd", "filename": "spatial-temporal.html", "title": "Spatial and Spatio-Temporal Models", "engine": "knitr::rmarkdown", "headings": [ "Overview", "1. Spatial Fay-Herriot Model (eblup_sfh)", "Model Formulation", "Fitting the Model", "Unsampled Domains and Spatial Kriging", "Multi-Threaded Bootstrap MSE", "2. Spatio-Temporal Fay-Herriot Model (eblup_stfh)", "Fitting the Spatio-Temporal Model", "Unsampled Domains and Missing Periods", "3. Comparing Models with autoplot", "4. Bayesian Spatio-Temporal Modeling (hb_area)", "References" ], "created": "2026-09-21 03:29:21", "modified": "2026-10-14 16:01:21", "commits": 6 }, { "source": "unit-level-bhf.Rmd", "filename": "unit-level-bhf.html", "title": "Unit-Level Estimation with Battese-Harter-Fuller", "engine": "knitr::rmarkdown", "headings": [ "Overview", "Model Formulation", "Step-by-Step Example", "1. Data Preparation", "2. Fit BHF Model with Bootstrap MSE", "3. Inspect Domain Estimates", "4. Visualizing Results with autoplot", "References" ], "created": "2026-09-21 03:29:21", "modified": "2026-09-21 12:57:22", "commits": 2 } ], "_score": 5.3222192947339195, "_indexed": true, "_nocasepkg": "fastsae", "_universes": [ "ridsonap" ], "_progress_url": "https://github.com/r-universe/ridsonap/actions/runs/38730075035", "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.0.0", "date": "2026-10-14T19:03:55.000Z", "distro": "resolute", "arch": "aarch64", "commit": "7e20210fc30d18a611fd8375d32e731aa4195180", "fileid": "https://r2.ropensci.org/08aa7261061233eb73c3f9bea16b58ff56b2bd494e97b041aa1a1b125011aed8", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/ridsonap/actions/runs/38618596328" }, { "r": "4.7.0", "os": "linux", "version": "1.0.0", "date": "2026-10-14T19:04:12.000Z", "distro": "resolute", "arch": "x86_64", "commit": "7e20210fc30d18a611fd8375d32e731aa4195180", "fileid": "https://r2.ropensci.org/fe1ad2d4b5feab38d41b2741262f02a99e2c57e30d1d914df36b423b1edbb26a", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/ridsonap/actions/runs/38618596328" }, { "r": "4.6.1", "os": "linux", "version": "1.0.0", "date": "2026-10-14T19:05:24.000Z", "distro": "resolute", "arch": "aarch64", "commit": "7e20210fc30d18a611fd8375d32e731aa4195180", "fileid": "https://r2.ropensci.org/f2432bfcc81fa04bdaf26cd429bcc1dcd5294ec7b755956ad7ef79d0eb6534c8", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/ridsonap/actions/runs/38618596328" }, { "r": "4.6.1", "os": "linux", "version": "1.0.0", "date": "2026-10-14T19:05:44.000Z", "distro": "resolute", "arch": "x86_64", "commit": "7e20210fc30d18a611fd8375d32e731aa4195180", "fileid": "https://r2.ropensci.org/82a5f5c4a367ae5881d83e249b8db79ddd040cf6632d5a09a529145b2a4c53f3", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/ridsonap/actions/runs/38618596328" }, { "r": "4.5.3", "os": "mac", "version": "1.0.0", "date": "2026-10-14T19:18:01.000Z", "arch": "aarch64", "commit": "7e20210fc30d18a611fd8375d32e731aa4195180", "fileid": "https://r2.ropensci.org/ea235264b13259d11cfb433776e5bc6f20af0ac2ad42345b80a510e77f17f29f", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/ridsonap/actions/runs/38618596328" }, { "r": "4.5.3", "os": "mac", "version": "1.0.0", "date": "2026-10-14T19:16:55.000Z", "arch": "x86_64", "commit": "7e20210fc30d18a611fd8375d32e731aa4195180", "fileid": "https://r2.ropensci.org/49c36897789113eb8e6566056e1d3b2c2e0d14557c2e7c0a0f83f0292e66e37d", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/ridsonap/actions/runs/38618596328" }, { "r": "4.6.1", "os": "mac", "version": "1.0.0", "date": "2026-10-14T19:10:10.000Z", "arch": "aarch64", "commit": "7e20210fc30d18a611fd8375d32e731aa4195180", "fileid": "https://r2.ropensci.org/08879e2a018b1cc564c55ed12f9af98b78ef72890c96828a05693ec2430193b8", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/ridsonap/actions/runs/38618596328" }, { "r": "4.6.1", "os": "mac", "version": "1.0.0", "date": "2026-10-14T19:16:20.000Z", "arch": "x86_64", "commit": "7e20210fc30d18a611fd8375d32e731aa4195180", "fileid": "https://r2.ropensci.org/adfa0ae943af1a72839cef780347f2a2ed4f8269668896f58463f9cbea90f149", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/ridsonap/actions/runs/38618596328" }, { "r": "4.6.0", "os": "wasm", "version": "1.0.0", "date": "2026-10-14T19:06:29.000Z", "arch": "emscripten", "commit": "7e20210fc30d18a611fd8375d32e731aa4195180", "fileid": "https://r2.ropensci.org/4581eab4d5f6e2fe06746dcf442d072aea4a8b8f738b9eb6be6831417b652aa5", "status": "success", "buildurl": "https://github.com/r-universe/ridsonap/actions/runs/38618596328" }, { "r": "4.7.0", "os": "win", "version": "1.0.0", "date": "2026-10-14T19:06:34.000Z", "arch": "aarch64", "commit": "7e20210fc30d18a611fd8375d32e731aa4195180", "fileid": "https://r2.ropensci.org/2def4d78a1e7acf80d8387765b2104fc7f110dd3a2884a2168abf66b48acd99b", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/ridsonap/actions/runs/38618596328" }, { "r": "4.7.0", "os": "win", "version": "1.0.0", "date": "2026-10-14T19:03:37.000Z", "arch": "x86_64", "commit": "7e20210fc30d18a611fd8375d32e731aa4195180", "fileid": "https://r2.ropensci.org/432eb29015a7b9b23aa45b57c6e48a75eb95df449386352ce06d1499292ca461", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/ridsonap/actions/runs/38618596328" }, { "r": "4.5.3", "os": "win", "version": "1.0.0", "date": "2026-10-14T19:03:33.000Z", "arch": "x86_64", "commit": "7e20210fc30d18a611fd8375d32e731aa4195180", "fileid": "https://r2.ropensci.org/95687e18941960e621bcf2d98f1da2f0f7e8f1caf58acb56ea15006e7cce0246", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/ridsonap/actions/runs/38618596328" }, { "r": "4.6.1", "os": "win", "version": "1.0.0", "date": "2026-10-14T19:05:49.000Z", "arch": "aarch64", "commit": "7e20210fc30d18a611fd8375d32e731aa4195180", "fileid": "https://r2.ropensci.org/88389bac2860881f20c7fbf5362b789de7a9f958817dbb48bc20e566f889ac50", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/ridsonap/actions/runs/38618596328" }, { "r": "4.6.1", "os": "win", "version": "1.0.0", "date": "2026-10-14T19:02:47.000Z", "arch": "x86_64", "commit": "7e20210fc30d18a611fd8375d32e731aa4195180", "fileid": "https://r2.ropensci.org/3fc0309b479c0d4237227834ec5423a3bb470d0a485bee8b014bdafceb6502ed", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/ridsonap/actions/runs/38618596328" } ] }