Package: agfh 0.2.1

agfh: Agnostic Fay-Herriot Model for Small Area Statistics

Implements the Agnostic Fay-Herriot model, an extension of the traditional small area model. In place of normal sampling errors, the sampling error distribution is estimated with a Gaussian process to accommodate a broader class of distributions. This flexibility is most useful in the presence of bounded, multi-modal, or heavily skewed sampling errors.

Authors:Marten Thompson [aut, cre, cph], Snigdhansu Chatterjee [ctb, cph]

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agfh.pdf |agfh.html✨
agfh/json (API)
NEWS

# Install 'agfh' in R:

install.packages('agfh', repos = c('https://martenthompson.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

22 exports 0.00 score 38 dependencies 2 scripts 339 downloads

Last updated 1 years agofrom:61b98cccbc. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Sep 17 2024
R-4.5-win	OK	Sep 17 2024
R-4.5-linux	OK	Sep 17 2024
R-4.4-win	OK	Sep 17 2024
R-4.4-mac	OK	Sep 17 2024
R-4.3-win	OK	Sep 17 2024
R-4.3-mac	OK	Sep 17 2024

Exports:adj_profile_likelihood_theta_var_maker adj_resid_likelihood_theta_var_maker agfh_theta_new_pred anderson_darling beta_err_gen cramer_vonmises gamma_err_gen hb_theta_new_pred kolmogorov_smirnov make_agfh_sampler make_gibbs_sampler map_from_density mse null_gen resid_likelihood_theta_var_maker RM_beta_eblue RM_theta_eblup RM_theta_new_pred RM_theta_var_moment_est shapiro_wilk test_u_normal theta_var_est_grid

Dependencies:cli colorspace fansi farver FNN ggplot2 glue goftest gtable isoband kernlab KernSmooth ks labeling lattice lifecycle magrittr MASS Matrix mclust mgcv multicool munsell mvtnorm nlme pillar pkgconfig pracma R6 RColorBrewer Rcpp rlang scales tibble utf8 vctrs viridisLite withr

agfh: Agnostic Fay-Herriot Model

Marten Thompson

Rendered fromvignette_agfh.Rmdusingknitr::knitron Sep 17 2024.

Last update: 2023-06-21
Started: 2023-06-21

Citation

Readme and manuals

Help Manual

Help page	Topics
Maker Function: Adjusted Profile Likelihood of Latent Variance	adj_profile_likelihood_theta_var_maker
Maker Function: Adjusted Residual Likelihood of Latent Variance	adj_resid_likelihood_theta_var_maker
Agnostic Fay-Herriot Hierarchical Bayesian Model Predictions at Latent Level	agfh_theta_new_pred
Anderson-Darling Normality Test	anderson_darling
Generate Data with Beta Sampling Errors	beta_err_gen
Cramer-Von Mises Normality Test	cramer_vonmises
Generate Data with Gamma Sampling Errors	gamma_err_gen
Traditional Fay-Herriot Hierarchical Bayesian Model Predictions	hb_theta_new_pred
Kolmogorov-Smirnov Normality Test	kolmogorov_smirnov
Maker Function: Agnostic Fay-Herriot Sampler	make_agfh_sampler
Maker Function: Traditional Fay-Herriot Gibbs Sampler	make_gibbs_sampler
Calculate the MAP Estimate from Posterior Samples	map_from_density
Calculate the Mean Squared Error Between two Vectors	mse
Generate Data with Normal Sampling Errors	null_gen
Maker Function: Residual Likelihood of Latent Variance	resid_likelihood_theta_var_maker
Traditional EBLUE Estimator of Beta	RM_beta_eblue rm_beta_eblue
Traditional EBLUP Estimator of Theta	RM_theta_eblup rm_theta_eblup
Traditional EBLUP Estimator of Theta for new 'X' values	RM_theta_new_pred rm_theta_new_pred
Moment-Based Estimator of Latent Model Variance	RM_theta_var_moment_est rm_theta_var_moment_est
Shaprio-Wilk Normality Test	shapiro_wilk
Normality Test	test_u_normal
Basic Grid Optimizer for Likelihood	theta_var_est_grid