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agfh
View on CRAN: Click
here
Download and install agfh package within the R console
Install from CRAN:
install.packages("agfh")
Install from Github:
library("remotes")
install_github("cran/agfh") Install by package version:
library("remotes")
install_version("agfh", "0.2.1") Attach the package and use:
library("agfh")
Maintained by
Marten Thompson
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-06-21
Latest Update: 2023-06-21
Description:
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.
How to cite:
Marten Thompson (2023). agfh: Agnostic Fay-Herriot Model for Small Area Statistics. R package version 0.2.1, https://cran.r-project.org/web/packages/agfh. Accessed 07 Aug. 2026.
Previous versions and publish date:
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Complete documentation for agfh
Functions, R codes and Examples using
the agfh R package
Some associated functions: RM_beta_eblue . RM_theta_eblup . RM_theta_new_pred . RM_theta_var_moment_est . 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 . shapiro_wilk . test_u_normal . theta_var_est_grid .
Some associated R codes: agnostic_fh.R . data_gen.R . frequentist_eb.R . hier_bayes.R . u_normal.R . Full agfh package functions and examples
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