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gdpar
View on CRAN: Click
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Download and install gdpar package within the R console
Install from CRAN:
install.packages("gdpar")
Install from Github:
library("remotes")
install_github("cran/gdpar") Install by package version:
library("remotes")
install_version("gdpar", "0.1.0") Attach the package and use:
library("gdpar")
Maintained by
José Mauricio Gómez Julián
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First Published: 2026-07-15
Latest Update: 2026-07-15
Description:
Implements a unified predictive framework in which individual parameters are decomposed as theta_i equal to theta_ref plus Delta(x_i, theta_ref), with theta_ref a population reference and Delta an explicit deviation function. The decomposition follows the Additive-Multiplicative-Modulated canonical form and is estimated through three complementary paths: hierarchical Bayesian inference via 'Stan', varying-coefficient models via penalized splines, and amortized inference via hypernetworks in 'torch'. The package provides identifiability diagnostics, validity tests for the population reference, and benchmarks against canonical zero-inflated count datasets and avian abundance data from the eBird Status and Trends project. The framework and its estimation paths are described in Gomez Julian (2026) <doi:10.5281/zenodo.21046269>.
How to cite:
José Mauricio Gómez Julián (2026). gdpar: General Dynamic Parameter Models via Reference Anchoring. R package version 0.1.0, https://cran.r-project.org/web/packages/gdpar. Accessed 05 Nov. 2026.
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