Provides an extension to the Partial Credit Model and Generalized Partial Credit Models which allows for an additional person parameter that characterizes the uncertainty of the person. The method was originally proposed by Tutz and Schauberger (2020) <doi:10.1177/0146621620920932>.

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Implements empirical Bayes approaches to genotype
       polyploids from next generation sequencing data while
       accounting for allele bias, overdispersion, and sequencing
       error. The main functions are flexdog() and multidog(), 
       which allow the specification
       of many different genotype distributions. Also provided are functions to
       simulate genotypes, rgeno(), and read-counts, rflexdog(), as well as
       functions to calculate oracle genotyping error rates, oracle_mis(), and
       correlation with the true genotypes, oracle_cor(). These latter two
       functions are useful for read depth calculations. Run
       browseVignettes(package = "updog") in R for example usage. See
       Gerard et al. (2018) <doi:10.1534/genetics.118.301468> and
       Gerard and Ferrao (2020) <doi:10.1093/bioinformatics/btz852> for details 
       on the implemented methods.

Provides an algorithm to detect and characterize disturbances (start, end dates, intensity) that can occur at different hierarchical levels by studying the dynamics of longitudinal observations at the unit level and group level based on Nadaraya-Watson's smoothing curves, but also a shiny app which allows to visualize the observations and the detected disturbances. Finally the package provides a dataframe mimicking a pig farming system subsected to disturbances simulated according to Le et al.(2022) <doi:10.1016/j.animal.2022.100496>.