Designed for estimating variants of hidden (latent) Markov models
    (HMMs), mixture HMMs, and non-homogeneous HMMs (NHMMs) for social sequence
    data and other categorical time series. Special cases include
    feedback-augmented NHMMs, Markov models without latent layer, mixture
    Markov models, and latent class models. The package supports models for one
    or multiple subjects with one or multiple parallel sequences (channels).
    External covariates can be added to explain cluster membership in mixture 
    models as well as initial, transition and emission probabilities in NHMMs.
    The package provides functions for evaluating and comparing models, as well
    as functions for visualizing of multichannel sequence data and HMMs. For
    NHMMs, methods for computing average causal effects and marginal state and
    emission probabilities are available. Models are estimated using maximum
    likelihood via the EM algorithm or direct numerical maximization with
    analytical gradients. Documentation is available via several vignettes,
    and Helske and Helske (2019, <doi:10.18637/jss.v088.i03>). For methodology
    behind the NHMMs, see Helske (2025, <doi:10.48550/arXiv.2503.16014>).

Contains an implementation of invariant causal prediction for sequential data. The main function in the package is 'seqICP', which performs linear sequential invariant causal prediction and has guaranteed type I error control. For non-linear dependencies the package also contains a non-linear method 'seqICPnl', which allows to input any regression procedure and performs tests based on a permutation approach that is only approximately correct. In order to test whether an individual set S is invariant the package contains the subroutines 'seqICP.s' and 'seqICPnl.s' corresponding to the respective main methods.

Multiple imputation of missing data in a dataset using MICT or 
    MICT-timing methods. The core idea of the algorithms is to fill gaps of 
    missing data, which is the typical form of missing data in a longitudinal 
    setting, recursively from their edges. Prediction is based on either a 
    multinomial or random forest regression model. Covariates and 
    time-dependent covariates can be included in the model.