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fastsae - High-Performance Frequentist and Bayesian Small Area Estimation in R

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) <doi:10.48550/arXiv.1912.11554> on 'JAX' (CPU or NVIDIA GPU). Frequentist models include the area-level Fay-Herriot model (Fay and Herriot, 1979 <doi:10.1080/01621459.1979.10482505>), spatial Fay-Herriot (Pratesi and Salvati, 2008 <doi:10.1007/s10260-007-0061-9>), spatio-temporal Fay-Herriot (Marhuenda et al., 2013 <doi:10.1016/j.csda.2012.09.002>), unit-level Battese-Harter-Fuller (Battese et al., 1988 <doi:10.1080/01621459.1988.10478561>), and two-fold subarea models (Torabi and Rao, 2014 <doi:10.1016/j.jmva.2014.02.001>) for nested area-subarea hierarchies. Bayesian models use 'INLA' (Rue et al., 2009 <doi:10.1111/j.1467-9868.2008.00700.x>), 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 <doi:10.1002/9781118735855>), automatic handling of unsampled domains, and S3 methods for summaries, diagnostics and plots.

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cppinlaopenmpsmall-area-estimationopenblascpp

5.32 score 1 stars 309 downloads

sae.projection - Small Area Estimation Using Model-Assisted Projection Method

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) <doi:10.1093/biomet/asr063>.

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3.78 score 12 scripts 357 downloads

saens - Small Area Estimation with Cluster Information for Estimation of Non-Sampled Areas

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) <doi:10.9790/5728-10121519>.

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2.85 score 1 stars 14 scripts 388 downloads

saeHB.unit - Basic Unit Level Model using Hierarchical Bayesian Approach

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) <doi:10.1080/01621459.1988.10478561>.

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jagscpp

2.70 score 1 scripts 201 downloads