A Korean text-analysis layer over the 'MeCab' morphological
    analyzer. Provides tokenizers that follow the 'tokenizers' contract for
    use with 'tidytext', morpheme-aware n-grams, a curated Korean stopword
    table, access to the KNU sentiment lexicon, friendly user-dictionary
    management, predicate lemmatization, keyword extraction, keyword-in-context
    concordances, and light text normalization. The native 'MeCab' interface
    and dictionary compilation are provided by 'RcppMeCab'.

Computes confidence intervals for nonlinear functions of model 
    parameters (e.g., product of k coefficients) in single-level and multilevel 
    structural equation
     models. Methods include the distribution of the product, 
    Monte Carlo simulation, and bootstrap methods. It also performs the Model-Based 
    Constrained Optimization (MBCO) procedure for hypothesis testing of indirect 
    effects.
    References:
    Tofighi, D., and MacKinnon, D. P. (2011). RMediation: An R package for mediation 
    analysis confidence intervals. Behavior Research Methods, 43, 692-700. 
    <doi:10.3758/s13428-011-0076-x>;
    Tofighi, D., and Kelley, K. (2020). Improved inference in mediation analysis: Introducing the model-based constrained optimization procedure. 
    Psychological Methods, 25(4), 496-515. <doi:10.1037/met0000259>;
    Tofighi, D. (2020). Bootstrap Model-Based Constrained Optimization Tests of 
    Indirect Effects. Frontiers in Psychology, 10, 2989. 
    <doi:10.3389/fpsyg.2019.02989>.

Provides complete functionality to analyse data from repeated
    measures experiments with hierarchical or crossed experimental designs.
    Supports testing modeling assumptions, identifying outlier observations
    and experimental units, estimating statistical power, and performing
    sample size calculations. Uses linear mixed effects models via 'lme4'
    and simulation-based power analysis via 'simr'. Handles both normal and
    non-normal error distributions including binomial and Poisson families. For more details see Shin et al. (2022) <doi:10.1101/2022.07.18.500490>, Bates et al. (2015) <doi:10.18637/jss.v067.i01>, Green and MacLeod (2016) <doi:10.1111/2041-210X.12504>, Hartig (2024) <doi:10.32614/CRAN.package.DHARMa>, Nieuwenhuis et al. (2012) <doi:10.32614/RJ-2012-011>, Millard  (2013) <doi:10.1007/978-1-4614-8456-1> and Kuznetsova et al. (2017) <doi:10.18637/jss.v082.i13>.