Allows ATA (Automatic Time series analysis using the Ata method) models from the 'ATAforecasting' package to be used in a tidy workflow with the modeling interface of
    'fabletools'. This extends 'ATAforecasting' to provide enhanced model specification and management, performance evaluation methods, and
    model combination tools. The Ata method (Yapar et al. (2019) <doi:10.15672/hujms.461032>), an alternative to exponential smoothing (described in Yapar (2016)
    <doi:10.15672/HJMS.201614320580>, Yapar et al. (2017) <doi:10.15672/HJMS.2017.493>), is a new univariate time series forecasting method which provides
    innovative solutions to issues faced during the initialization and optimization stages of existing forecasting methods.
    Forecasting performance of the Ata method is superior to existing methods both in terms of easy implementation and accurate forecasting.
    It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend and seasonal).

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.

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 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>).