v1.1.3

mvgam 1.1.3

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

Deprecations

• 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

Bug fixes

• 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)