{ "_id": "6a1bfc5e1d7bb097a0a280de", "Package": "RcppCensSpatial", "Type": "Package", "Title": "Spatial Estimation and Prediction for Censored/Missing Responses", "Version": "1.0.0", "Authors@R": "c(person(given = \"Katherine A.\",\nfamily = \"L. Valeriano\",\nrole = c(\"aut\", \"cre\"),\nemail = \"katandreina@gmail.com\",\ncomment = c(ORCID = \"0000-0001-6388-4753\")),\nperson(given = \"Christian\",\nfamily = \"Galarza Morales\",\nrole = \"ctb\",\nemail = \"cgalarza88@gmail.com\",\ncomment = c(ORCID = \"0000-0002-4818-6006\")),\nperson(given = \"Larissa\",\nfamily = \"Avila Matos\",\nrole = \"ctb\",\nemail = \"larissam@unicamp.br\",\ncomment = c(ORCID = \"0000-0002-2635-0901\")))", "Description": "It provides functions for estimating parameters in linear\nspatial models with censored or missing responses using the\nExpectation-Maximization (EM), Stochastic Approximation EM\n(SAEM), and Monte Carlo EM (MCEM) algorithms. These methods are\nwidely used to obtain maximum likelihood (ML) estimates in the\npresence of incomplete data. The EM algorithm computes ML\nestimates when a closed-form expression for the conditional\nexpectation of the complete-data log-likelihood is available.\nThe MCEM algorithm replaces this expectation with a Monte Carlo\napproximation based on independent simulations of the missing\ndata. In contrast, the SAEM algorithm decomposes the E-step\ninto simulation and stochastic approximation steps, improving\ncomputational efficiency in complex settings. In addition, the\npackage provides standard error estimation based on the Louis\nmethod. It also includes functionality for spatial prediction\nat new locations. References used for this package: Galarza, C.\nE., Matos, L. A., Castro, L. M., & Lachos, V. H. (2022).\nMoments of the doubly truncated selection elliptical\ndistributions with emphasis on the unified multivariate skew-t\ndistribution. Journal of Multivariate Analysis, 189, 104944\n; Valeriano, K. A., Galarza, C.\nE., & Matos, L. A. (2023). Moments and random number generation\nfor the truncated elliptical family of distributions.\nStatistics and Computing, 33(1), 32\n.", "License": "GPL (>= 2)", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "RdMacros": "Rdpack", "LazyData": "true", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-31 09:09:09 UTC", "User": "root" }, "Author": "Katherine A. L. Valeriano [aut, cre] (ORCID:\n), Christian Galarza\nMorales [ctb] (ORCID: ),\nLarissa Avila Matos [ctb] (ORCID:\n)", "Maintainer": "Katherine A. L. 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