Time series toolkit with identical behavior for all
  time series classes: 'ts','xts', 'data.frame', 'data.table', 'tibble', 'zoo',
  'timeSeries', 'tsibble', 'tis' or 'irts'. Also converts reliably between these classes.

Different estimators are provided to solve the blind source separation problem for multivariate time series with stochastic volatility and supervised dimension reduction problem for multivariate time series. Different functions based on AMUSE and SOBI are also provided for estimating the dimension of the white noise subspace. The package is fully described in Nordhausen, Matilainen, Miettinen, Virta and Taskinen (2021) <doi:10.18637/jss.v098.i15>.

Two stage curvature identification with machine learning for causal 
    inference in settings when instrumental variable regression is not suitable
    because of potentially invalid instrumental variables. Based on Guo and 
    Buehlmann (2022) "Two Stage Curvature Identification with Machine Learning: 
    Causal Inference with Possibly Invalid Instrumental Variables"
    <arXiv:2203.12808>. The vignette is available in Carl, Emmenegger, Bühlmann and Guo (2023) 
    "TSCI: two stage curvature identification for causal inference with 
    invalid instruments" <arXiv:2304.00513>.

A set of measures of dissimilarity between time series to perform time series clustering. Metrics based on raw data, on generating models and on the forecast behavior are implemented. Some additional utilities related to time series clustering are also provided, such as clustering algorithms and cluster evaluation metrics.

Functions for the analysis of time series using copula models.  
    The package is based on methodology described in the following references.
    McNeil, A.J. (2021) <doi:10.3390/risks9010014>,
    Bladt, M., & McNeil, A.J. (2021) <doi:10.1016/j.ecosta.2021.07.004>,
    Bladt, M., & McNeil, A.J. (2022) <doi:10.1515/demo-2022-0105>.

Likelihood-based methods for model fitting and assessment, prediction and intervention analysis of count time series following generalized linear models are provided. Models with the identity and with the logarithmic link function are allowed. The conditional distribution can be Poisson or Negative Binomial.