mvgam 1.1.3 (development version; not yet on CRAN)

New functionalities

Allow intercepts to be included in process models when trend_formula is supplied. This breaks the assumption that the process has to be zero-centred, adding more modelling flexibility but also potentially inducing nonidentifiabilities with respect to any observation model intercepts. Thoughtful priors are a must for these models
Added standata.mvgam_prefit, stancode.mvgam and stancode.mvgam_prefit methods for better alignment with ‘brms’ workflows
Added ‘gratia’ to Enhancements to allow popular methods such as draw() to be used for ‘mvgam’ models if ‘gratia’ is already installed
Added an ensemble.mvgam_forecast method to generate evenly weighted combinations of probabilistic forecast distributions
Added an irf.mvgam method to compute Generalized and Orthogonalized Impulse Response Functions (IRFs) from models fit with Vector Autoregressive dynamics

The drift argument has been deprecated. It is now recommended for users to include parametric fixed effects of “time” in their respective GAM formulae to capture any expected drift effects

Added a new check to ensure that exception messages are only suppressed by the silent argument if the user’s version of ‘cmdstanr’ is adequate
Updated dependency for ‘brms’ to version >= ‘2.21.0’ so that read_csv_as_stanfit can be imported, which should future-proof the conversion of ‘cmdstanr’ models to stanfit objects (#70)

Added options for silencing some of the ‘Stan’ compiler and modeling messages using the silent argument in mvgam()
Moved a number of packages from ‘Depends’ to ‘Imports’ for simpler package loading and fewer potential masking conflicts
Improved efficiency of the model initialisation by tweaking parameters of the underlying ‘mgcv’ gam object’s convergence criteria, resulting in much faster model setups
Added an option to use trend_model = 'None' in State-Space models, increasing flexibility by ensuring the process error evolves as white noise (#51)
Added an option to use the non-centred parameterisation for some autoregressive trend models, which speeds up mixing most of the time
Updated support for multithreading so that all observation families (apart from nmix()) can now be modeled with multiple threads
Changed default priors on autoregressive coefficients (AR1, AR2, AR3) to enforce stationarity, which is a much more sensible prior in the majority of contexts

Fixed a small bug that prevented conditional_effects.mvgam() from handling effects with three-way interactions

Changed indexing of an internal c++ function after Prof Brian Ripley’s
email: Dear maintainer, Please see the problems shown on https://cran.r-project.org/web/checks/check_results_mvgam.html. Please correct before 2024-05-22 to safely retain your package on CRAN. The CRAN Team