Develops algorithms for fitting, prediction, simulation 
  and initialization of the following models
  (1)- hidden hybrid Markov/semi-Markov model, 
  introduced by Guedon (2005) <doi:10.1016/j.csda.2004.05.033>, 
  (2)- nonparametric mixture of B-splines emissions (Langrock et al., 2015
  <doi:10.1111/biom.12282>), 
  (3)- regime switching regression model 
  (Kim et al., 2008 <doi:10.1016/j.jeconom.2007.10.002>) and auto-regressive 
  hidden hybrid Markov/semi-Markov model, 
  (4)- spline-based nonparametric 
  estimation of additive state-switching models  
  (Langrock et al., 2018 <doi:10.1111/stan.12133>)
  (5)- robust emission model proposed by 
  Qin et al, 2024 <doi:10.1007/s10479-024-05989-4>
  (6)- several emission distributions, including mixture of multivariate normal  
  (which can also handle missing data using EM algorithm) and multi-nomial emission 
  (for modeling polymer or DNA sequences)
  (7)- tools for prediction of future state sequence, computing the score of a new sequence,
  splitting the samples and sequences to train and test sets, computing the information measures of 
  the models, computing the residual useful lifetime (reliability) and many other useful tools ...
  (read for more description: Amini et al., 2022 <doi:10.1007/s00180-022-01248-x> and its 
  arxiv version: <doi:10.48550/arXiv.2109.12489>).

Builds on the EMD package to provide additional tools for empirical mode decomposition (EMD) and Hilbert spectral analysis. It also implements the ensemble empirical decomposition (EEMD) and the complete ensemble empirical mode decomposition (CEEMD) methods to avoid mode mixing and intermittency problems found in EMD analysis.  The package comes with several plotting methods that can be used to view intrinsic mode functions, the HHT spectrum, and the Fourier spectrum.

A user-friendly tool to fit Bayesian regression models. It can fit 3 types of Bayesian models using individual-level, summary-level, and individual plus pedigree-level (single-step) data for both Genomic prediction/selection (GS) and Genome-Wide Association Study (GWAS), it was designed to estimate joint effects and genetic parameters for a complex trait, including:
    (1) fixed effects and coefficients of covariates,
    (2) environmental random effects, and its corresponding variance, 
    (3) genetic variance, 
    (4) residual variance, 
    (5) heritability, 
    (6) genomic estimated breeding values (GEBV) for both genotyped and non-genotyped individuals,
    (7) SNP effect size, 
    (8) phenotype/genetic variance explained (PVE) for single or multiple SNPs, 
    (9) posterior probability of association of the genomic window (WPPA), 
    (10) posterior inclusive probability (PIP). 
    The functions are not limited, we will keep on going in enriching it with more features. 
    References: Lilin Yin et al. (2025) <doi:10.18637/jss.v114.i06>; Meuwissen et al. (2001) <doi:10.1093/genetics/157.4.1819>; Gustavo et al. (2013) <doi:10.1534/genetics.112.143313>; Habier et al. (2011) <doi:10.1186/1471-2105-12-186>; Yi et al. (2008) <doi:10.1534/genetics.107.085589>; Zhou et al. (2013) <doi:10.1371/journal.pgen.1003264>; Moser et al. (2015) <doi:10.1371/journal.pgen.1004969>; Lloyd-Jones et al. (2019) <doi:10.1038/s41467-019-12653-0>; Henderson (1976) <doi:10.2307/2529339>; Fernando et al. (2014) <doi:10.1186/1297-9686-46-50>.