v1.1.4

New functionalities

• Added the how_to_cite.mvgam() function to generate a scaffold methods description of fitted models, which can hopefully make it easier for users to fully describe their programming environment
• Improved various plotting functions by returning ggplot objects in place of base plots (thanks to @mhollanders #38)
• Added the brier score (score = 'brier') as an option in score.mvgam_forecast() for scoring forecasts of binary variables when using family = bernoulli() (#80)
• Added augment() function to add residuals and fitted values to an mvgam object's observed data (thanks to @swpease #83)
• Added support for approximate gp() effects with more than one covariate and with different kernel functions (#79)
• Added function jsdgam() to estimate Joint Species Distribution Models in which both the latent factors and the observation model components can include any of mvgam's complex linear predictor effects. Also added a function residual_cor() to compute residual correlation, covariance and precision matrices from jsdgam models. See ?mvgam::jsdgam and ?mvgam::residual_cor for details
• Added a stability.mvgam() method to compute stability metrics from models fit with Vector Autoregressive dynamics (#21 and #76)
• 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 (#21 and #76)

Deprecations

• Arguments use_stan, jags_path, data_train, data_test, adapt_delta, max_treedepth and drift have been removed from primary functions to streamline documentation and reflect the package's mission to deprecate 'JAGS' as a suitable backend. Both adapt_delta and max_treedepth should now be supplied in a named list() to the new argument control

Bug fixes

• Bug fix to ensure marginaleffects::comparisons functions appropriately recognise internal rowid variables
• Updates to ensure ensemble provides appropriate weighting of forecast draws (#98)
• Not necessarily a "bug fix", but this update removes several dependencies to lighten installation and improve efficiency of the workflow (#93)
• Fixed a minor bug in the way trend_map recognises levels of the series factor
• Bug fix to ensure lfo_cv recognises the actual times in time, just in case the user supplies data that doesn't start at t = 1. Also updated documentation to better reflect this
• Bug fix to ensure update.mvgam captures any knots or trend_knots arguments that were passed to the original model call