Meta-analysis traditionally assigns more weight to studies with lower standard errors,
    assuming higher precision. However, in observational research, precision must be
    estimated and is vulnerable to manipulation, such as p-hacking, to achieve statistical
    significance. This can lead to spurious precision, invalidating inverse-variance
    weighting and bias-correction methods like funnel plots. Common methods for addressing
    publication bias, including selection models, often fail or exacerbate the problem.
    This package introduces an instrumental variable approach to limit bias caused by
    spurious precision in meta-analysis. Methods are described in 'Irsova et al.' (2025)
    <doi:10.1038/s41467-025-63261-0>.

A hybrid of the K-means algorithm and a Majorization-Minimization method to introduce a robust clustering. The reference paper is: Julien Mairal, (2015) <doi:10.1137/140957639>. The two most important functions in package 'MajKMeans' are cluster_km() and cluster_MajKm(). cluster_km() clusters data without Majorization-Minimization and cluster_MajKm() clusters data with Majorization-Minimization method. Both of these functions calculate the sum of squares (SS) of clustering.

A hybrid of the K-means algorithm and a Majorization-Minimization method to introduce a robust clustering. The reference paper is: Julien Mairal, (2015) <doi:10.1137/140957639>. The two most important functions in package 'MajMinKmeans' are cluster_km() and cluster_MajKm(). Cluster_km() clusters data without Majorization-Minimization and cluster_MajKm() clusters data with Majorization-Minimization method. Both of these functions calculate the sum of squares (SS) of clustering. Another useful function is MajMinOptim(), which helps to find the optimum values of the Majorization-Minimization estimator.

Create dummy variables from categorical data.
    This package can convert categorical data (factor and ordered) into
    dummy variables and handle multiple columns simultaneously.
    This package enables to select whether a dummy variable for base group
    is included (for principal component analysis/factor analysis) or
    excluded (for regression analysis) by an option.
    'makedummies' function accepts 'data.frame', 'matrix', and
    'tbl' (tibble) class (by 'tibble' package).
    'matrix' class data is automatically converted to 'data.frame' class.