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mcmcsae  

Markov Chain Monte Carlo Small Area Estimation
View on CRAN: Click here


Download and install mcmcsae package within the R console
Install from CRAN:
install.packages("mcmcsae")

Install from Github:
library("remotes")
install_github("cran/mcmcsae")

Install by package version:
library("remotes")
install_version("mcmcsae", "0.7.7")



Attach the package and use:
library("mcmcsae")
Maintained by
Harm Jan Boonstra
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-09-01
Latest Update: 2023-06-29
Description:
Fit multi-level models with possibly correlated random effects using Markov Chain Monte Carlo simulation. Such models allow smoothing over space and time and are useful in, for example, small area estimation.
How to cite:
Harm Jan Boonstra (2020). mcmcsae: Markov Chain Monte Carlo Small Area Estimation. R package version 0.7.7, https://cran.r-project.org/web/packages/mcmcsae. Accessed 21 Nov. 2024.
Previous versions and publish date:
0.5.0 (2020-09-01 18:00), 0.6.0 (2021-01-20 09:40), 0.7.0 (2021-10-10 12:40), 0.7.1 (2022-09-02 15:30), 0.7.2 (2023-02-04 17:32), 0.7.3 (2023-06-02 22:40), 0.7.4 (2023-06-29 18:30), 0.7.5 (2023-10-10 22:40), 0.7.6 (2023-12-02 23:00)
Other packages that cited mcmcsae R package
View mcmcsae citation profile
Other R packages that mcmcsae depends, imports, suggests or enhances
Complete documentation for mcmcsae
Functions, R codes and Examples using the mcmcsae R package
Some associated functions: CG . CG_control . MCMC-diagnostics . MCMC-object-conversion . MCMCsim . Matrix-methods . SBC_test . ac_fft . acceptance_rates . aggrMatrix . anyNA-tabMatrix-method . bart . chol_control . combine_chains . combine_iters . computeDesignMatrix . compute_GMRF_matrices . correlation . create_TMVN_sampler . create_sampler . gen . generate_data . get_draw . glreg . labels . matrix-vector . maximize_log_lh_p . mcmcsae-TMVN-method . mcmcsae-family . mcmcsae-package . mcmcsae_example . mec . model-information-criteria . model_matrix . nchains-ndraws-nvars . par_names . plot.dc . plot.mcdraws . plot_coef . posterior-moments . pr_MLiG . pr_exp . pr_fixed . pr_gamma . pr_gig . pr_invchisq . pr_invwishart . pr_normal . predict.mcdraws . print.dc_summary . print.mcdraws_summary . read_draws . reg . residuals-fitted-values . sampler_control . setup_CG_sampler . setup_cluster . stop_cluster . subset.dc . summary.dc . summary.mcdraws . tabMatrix-indexing . transform_dc . vfac . vreg . weights.mcdraws . 
Some associated R codes: MCMCsim.R . MH.R . MatrixUtils.R . RcppExports.R . TMVN_methods.R . TMVN_sampler.R . aux_closures.R . cholesky.R . conjugate_gradients.R . family.R . formulas.R . kronprod.R . mc_bart.R . mc_block.R . mc_gen.R . mc_gl.R . mc_mec.R . mc_reg.R . mc_vfac.R . mc_vreg.R . mcmcsae.R . model_eval.R . modelmatrix.R . models.R . opts.R . parallel.R . plots.R . prediction.R . priors.R . random.R . samplers.R . sbc.R . sparse_template.R . tabMatrix.R . utils.R .  Full mcmcsae package functions and examples
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