Extends the 'fable' framework to support forecasting methods 
    specifically designed for intermittent time series data, where demand 
    occurs sporadically with many zero values. All methods produce probabilistic
    forecasts returned as 'distributional' objects. The returned forecasts can be
    used to evaluate accuracy, plot and print the results seamlessly with 'fable'.
    The methods include:
    Harvey, Fernandes (1989) <doi:10.1080/07350015.1989.10509750>,
    Willemain, Smart, Schwarz (2004) <doi:10.1016/S0169-2070(03)00013-X>,
    Zhou, Viswanathan (2011) <doi:10.1016/j.ijpe.2010.09.021>,
    Snyder, Ord, Beaumont (2012) <doi:10.1016/j.ijforecast.2011.03.009>,
    Kolassa (2016) <doi:10.1016/j.ijforecast.2015.12.004>,
    Hasni, Aguir, Babai, Jemai (2019) <doi:10.1080/00207543.2018.1424375>,
    Damato, Azzimonti, Corani (2025) <doi:10.1016/j.ijforecast.2025.10.001>,
    Sbrana (2025) <doi:10.1080/01605682.2025.2569661>.

Allows prophet models from the 'prophet' package to be used in a tidy workflow with the modelling interface of 'fabletools'. This extends 'prophet' to provide enhanced model specification and management, performance evaluation methods, and model combination tools.

Provides a tidy R interface for count time series analysis. It includes implementation of the INGARCH (Integer Generalized Autoregressive Conditional Heteroskedasticity) model from the 'tscount' package and the GLARMA (Generalized Linear Autoregressive Moving Averages) model from the 'glarma' package. Additionally, it offers automated parameter selection algorithms based on the minimization of a penalized likelihood.

Provides tools, helpers and data structures for
    developing models and time series functions for 'fable' and extension
    packages. These tools support a consistent and tidy interface for time
    series modelling and analysis.

Model-based clustering of multivariate continuous data using Bayesian mixtures of factor analyzers (Papastamoulis (2019) <DOI:10.1007/s11222-019-09891-z> (2018) <DOI:10.1016/j.csda.2018.03.007>). The number of clusters is estimated using overfitting mixture models (Rousseau and Mengersen (2011) <DOI:10.1111/j.1467-9868.2011.00781.x>): suitable prior assumptions ensure that asymptotically the extra components will have zero posterior weight, therefore, the inference is based on the ``alive'' components. A Gibbs sampler is implemented in order to (approximately) sample from the posterior distribution of the overfitting mixture. A prior parallel tempering scheme is also available, which allows to run multiple parallel chains with different prior distributions on the mixture weights. These chains run in parallel and can swap states using a Metropolis-Hastings move. Eight different parameterizations give rise to parsimonious representations of the covariance per cluster (following Mc Nicholas and Murphy (2008) <DOI:10.1007/s11222-008-9056-0>). The model parameterization and number of factors is selected according to the Bayesian Information Criterion. Identifiability issues related to label switching are dealt by post-processing the simulated output with the Equivalence Classes Representatives algorithm (Papastamoulis and Iliopoulos (2010) <DOI:10.1198/jcgs.2010.09008>, Papastamoulis (2016) <DOI:10.18637/jss.v069.c01>).