Provides user friendly methods for the identification of sequence patterns that are statistically significantly associated with a property of the sequence. For instance, SeqFeatR allows to identify viral immune escape mutations for hosts of given HLA types. The underlying statistical method is Fisher's exact test, with appropriate corrections for multiple testing, or Bayes. Patterns may be point mutations or n-tuple of mutations. SeqFeatR offers several ways to visualize the results of the statistical analyses, see Budeus (2016) <doi:10.1371/journal.pone.0146409>.

Generates/modifies RNA-seq data for use in simulations. We provide
    a suite of functions that will add a known amount of signal to a real 
    RNA-seq dataset. The advantage of using this approach over simulating under
    a theoretical distribution is that common/annoying aspects of the data
    are more preserved, giving a more realistic evaluation of your method. 
    The main functions are select_counts(), thin_diff(), thin_lib(), 
    thin_gene(), thin_2group(), thin_all(), and effective_cor(). See
    Gerard (2020) <doi:10.1186/s12859-020-3450-9> for details on the
    implemented methods.

It provides miscellaneous sequence analysis functions for describing episodes in individual sequences, measuring association between domains in multidimensional sequence analysis (see Piccarreta (2017) <doi:10.1177/0049124115591013>), heat maps of sequence data, Globally Interdependent Multidimensional Sequence Analysis (see Robette et al (2015) <doi:10.1177/0081175015570976>), smoothing sequences for index plots (see Piccarreta (2012) <doi:10.1177/0049124112452394>), coding sequences for Qualitative Harmonic Analysis (see Deville (1982)), measuring stress from multidimensional scaling factors (see Piccarreta and Lior (2010) <doi:10.1111/j.1467-985X.2009.00606.x>), symmetrical (or canonical) Partial Least Squares (see Bry (1996)).

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>).