Hierarchical cors

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: mvgam
 Title: Multivariate (Dynamic) Generalized Additive Models
-Version: 1.1.3
+Version: 1.1.4
 Date: 2024-09-05
 Authors@R: person("Nicholas J", "Clark", , "[email protected]", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-7131-3301"))
 Description: Fit Bayesian Dynamic Generalized Additive Models to sets of time series. Users can build dynamic nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2022) <doi:10.1111/2041-210X.13974>.

NAMESPACE

@@ -77,7 +77,14 @@ export(GP)
 export(PW)
 export(RW)
 export(VAR)
+export(ZMVN)
 export(add_residuals)
+export(as_draws)
+export(as_draws_array)
+export(as_draws_df)
+export(as_draws_list)
+export(as_draws_matrix)
+export(as_draws_rvars)
 export(bernoulli)
 export(beta_binomial)
 export(betar)
@@ -95,7 +102,9 @@ export(eval_smoothDotmodDotsmooth)
 export(eval_smoothDotmoiDotsmooth)
 export(fevd)
 export(forecast)
+export(get_data)
 export(get_mvgam_priors)
+export(get_predict)
 export(gp)
 export(hindcast)
 export(hypotheses)

NEWS.md

@@ -1,6 +1,9 @@
 # mvgam 1.1.4 (development version; not yet on CRAN)
 ## New functionalities
 * Added a `stability.mvgam` method to compute stability metrics from models fit with Vector Autoregressive dynamics (#21)
+* Added functionality to estimate hierarchical error correlations when using multivariate latent process models and when the data are nested among levels of a relevant grouping factor (#75); see `?mvgam::AR` for an example
+* Added `ZMVN()` error models for estimating Zero-Mean Multivariate Normal errors; convenient for working with non time-series data where latent residuals are expected to be correlated (such as when fitting Joint Species Distribution Models); see `?mvgam::ZMVN` for examples
+=======
 * Added a `fevd.mvgam` method to compute forecast error variance decompositions from models fit with Vector Autoregressive dynamics
 
 ## Bug fixes