Sejong(http://www.sejong.or.kr/) corpus and
        Hannanum(http://semanticweb.kaist.ac.kr/home/index.php/HanNanum)
        dictionaries for KoNLP

Implements a method for fitting a bounded probability
        distribution to quantiles (for example stated by an expert),
        see Bornkamp and Ickstadt (2009) for details.  For this
        purpose B-splines are used, and the density is obtained by
        penalized least squares based on a Brier entropy penalty.  The
        package provides methods for fitting the distribution as well
        as methods for evaluating the underlying density and cdf. In
        addition methods for plotting the distribution, drawing random
        numbers and calculating quantiles of the obtained distribution
        are provided.

Calculates (unconditional) post-selection confidence intervals
    and p-values for the coefficients of (generalized) linear models.

Provides indices such as Manly's alpha, foraging ratio, and Ivlev's selectivity to allow for analysis of dietary selectivity and preference. Can accommodate multiple experimental designs such as constant prey number of prey depletion. Please contact the package maintainer with any publications making use of this package in an effort to maintain a repository of dietary selections studies.

An implementation of the selectboost algorithm (Bertrand et al. 2020, 'Bioinformatics', <doi:10.1093/bioinformatics/btaa855>), which is a general algorithm that improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. It can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective.

Adds variable-selection functions for Beta regression models
    (both mean and phi submodels) so they can be used within the 'SelectBoost' algorithm.
    Includes stepwise AIC, BIC, and corrected AIC on betareg() fits, 'gamlss'-based 
    LASSO/Elastic-Net, a pure 'glmnet' iterative re-weighted least squares-based selector 
    with an optional standardization speedup, and 'C++' helpers for iterative 
    re-weighted least squares working steps and precision updates. Also provides
    a fastboost_interval() variant for interval responses, comparison helpers,
    and a flexible simulator simulation_DATA.beta() for interval-valued data. 
    For more details see Bertrand and Maumy (2023) <doi:10.7490/f1000research.1119552.1>.