ridsonap.r-universe.dev

[ridsonap] fastsae 1.0.0

alfrzlp@gmail.com (Ridson Al Farizal P) — Wed, 14 Oct 2026 07:50:23 GMT

High-performance Small Area Estimation (SAE) in R, implementing frequentist EBLUP models via compiled C++ and Bayesian hierarchical models via 'INLA' (Integrated Nested Laplace Approximations) or, optionally, full MCMC with 'NumPyro' (Phan et al., 2019) on 'JAX' (CPU or NVIDIA GPU). Frequentist models include the area-level Fay-Herriot model (Fay and Herriot, 1979 ), spatial Fay-Herriot (Pratesi and Salvati, 2008 ), spatio-temporal Fay-Herriot (Marhuenda et al., 2013 ), unit-level Battese-Harter-Fuller (Battese et al., 1988 ), and two-fold subarea models (Torabi and Rao, 2014 ) for nested area-subarea hierarchies. Bayesian models use 'INLA' (Rue et al., 2009 ), supporting 6 likelihood families (Gaussian, Beta, Binomial, Poisson, Negative Binomial, Gamma), spatial structures (BYM2, Besag ICAR), temporal dynamics (RW1, RW2, AR1), and spatio-temporal interactions. Key features include analytical and bootstrap MSE estimation, benchmarking to aggregate targets (Rao and Molina, 2015 ), automatic handling of unsampled domains, and S3 methods for summaries, diagnostics and plots.

[ridsonap] sae.projection 0.1.5

ridsonalfarizal15@gmail.com (Ridson Al Farizal P) — Sat, 26 Sep 2026 02:17:08 GMT

Combines information from two independent surveys using a model-assisted projection method. Designed for survey sampling scenarios where a large sample collects only auxiliary information (Survey 1) and a smaller sample provides data on both variables of interest and auxiliary variables (Survey 2). Implements a working model to generate synthetic values of the variable of interest by fitting the model to Survey 2 data and predicting values for Survey 1 based on its auxiliary variables (Kim & Rao, 2012) .

[ridsonap] saens 0.1.2

alfrzlp@gmail.com (Ridson Al Farizal P) — Mon, 18 Nov 2024 01:43:45 GMT

Implementation of small area estimation (Fay-Herriot model) with EBLUP (Empirical Best Linear Unbiased Prediction) Approach for non-sampled area estimation by adding cluster information and assuming that there are similarities among particular areas. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Anisa et al. (2013) .

[ridsonap] saeHB.unit 0.1.0

alfrzlp@gmail.com (Ridson Al Farizal P) — Sun, 15 Oct 2023 23:53:40 GMT

Small area estimation unit level models (Battese-Harter-Fuller model) with a Bayesian Hierarchical approach. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Battese et al. (1988) .