Multiple tools are now available for inferring the personalised 
    germ line set from an adaptive immune receptor repertoire. 
    Output from these tools is converted to 
    a single format and supplemented with rich data such as usage and 
    characterisation of 'novel' germ line alleles. This data can be 
    particularly useful when considering the validity of novel inferences. Use
    of the analysis provided is described in <doi:10.3389/fimmu.2019.00435>.

Prepares census and core housing needs data, specifically designed for use with Statistics Canada data and standardized input data. The package offers functions for tidying, organizing, and splitting complex data tables, making it easier for users to perform analyses on the data. 'OHCSpackage''' is particularly useful for those working with census data that has a consistent format, number of rows, and number of columns. With this package, users can save time and streamline their data preparation processes.

Supports the modeling of ordinal random variables, 
    like the outcomes of races, via Softmax regression,
    under the Harville <doi:10.1080/01621459.1973.10482425> and
    Henery <doi:10.1111/j.2517-6161.1981.tb01153.x> models.

Ing and Lai (2011) <doi:10.5705/ss.2010.081> proposed a high-dimensional model selection procedure that comprises three steps: orthogonal greedy algorithm (OGA), high-dimensional information criterion (HDIC), and Trim. The first two steps, OGA and HDIC, are used to sequentially select input variables and determine stopping rules, respectively. The third step, Trim, is used to delete irrelevant variables remaining in the second step. This package aims at fitting a high-dimensional linear regression model via OGA+HDIC+Trim.

Inference using a class of Hidden Markov models
    (HMMs) called 'oHMMed'(ordered HMM with emission densities 
    <doi:10.1186/s12859-024-05751-4>): The 'oHMMed' algorithms identify 
    the number of comparably homogeneous regions within observed sequences
    with autocorrelation patterns. These are modelled as discrete hidden
    states; the observed data points are then realisations of continuous
    probability distributions with state-specific means that enable
    ordering of these distributions. The observed sequence is labelled
    according to the hidden states, permitting only neighbouring states
    that are also neighbours within the ordering of their associated
    distributions. The parameters that characterise these state-specific
    distributions are then inferred. Relevant for application to genomic
    sequences, time series, or any other sequence data with serial
    autocorrelation.