Provides exact and approximate algorithms for the horseshoe prior
    in linear regression models, which were proposed by Johndrow et al. (2020)
    <https://www.jmlr.org/papers/v21/19-536.html>.

High-performance implementation of the Modified Hodrick-Prescott (HP) Filter 
    for decomposing macroeconomic time series into trend and cyclical components. Based on 
    the methodology of Choudhary, Hanif and Iqbal (2014) <doi:10.1080/00036846.2014.894631> 
    "On smoothing macroeconomic time series using the modified HP filter", which uses 
    generalized cross-validation (GCV) to automatically select the optimal smoothing 
    parameter lambda, following McDermott (1997) "An automatic method for choosing the 
    smoothing parameter in the HP filter" (as described in Coe and McDermott (1997) 
    <doi:10.2307/3867497>). Unlike the standard HP filter that uses fixed lambda values 
    (1600 for quarterly, 100 for annual data), this package estimates series-specific lambda 
    values that minimize the GCV criterion. Implements efficient C++ routines via 
    'RcppArmadillo' for fast computation, supports batch processing of multiple series, and 
    provides comprehensive visualization tools using 'ggplot2'. Particularly useful for 
    cross-country macroeconomic comparisons, business cycle analysis, and when the 
    appropriate smoothing parameter is uncertain.

Transforms, calculates, and presents results from the Mental Health Quality of Life Questionnaire (MHQoL), a measure of health-related quality of life for individuals with mental health conditions. Provides scoring functions, summary statistics, and visualization tools to facilitate interpretation. For more details see van Krugten et al.(2022) <doi:10.1007/s11136-021-02935-w>.

Parameter estimation and prediction for hidden Markov and semi-Markov models for data with multiple observation sequences.  Suitable for equidistant time series data, with multivariate and/or missing data. Allows user defined emission distributions.