Implements the convex clustering through majorization-minimization (CCMM) algorithm described in Touw, Groenen, and Terada (2022) <doi:10.48550/arXiv.2211.01877> to perform minimization of the convex clustering loss function.

Provides an implementation of Congruence Class Models (CCMs) for 
    generating  networks. For additional details on CCMs see Goyal, Blitzstein, 
    and De Gruttola (2014) <doi:10.1017/nws.2014.2> and 
    Goyal, De Gruttola, Martin, Rennert, and Onnela <doi:10.48550/arXiv.2603.02467>.
    'ccmnet' facilitates sampling networks based on specific topological properties
    and attribute mixing patterns using a Markov Chain Monte Carlo framework. 
    The implementation builds upon code from the 'ergm' package; 
    see Handcock, Hunter, Butts, Goodreau, and Morris (2008) <doi:10.18637/jss.v024.i01>.

Provides a set of functions to perform queries against the 'CCM' API <https://mohcontacttracing.my.salesforce.com>.

Cases are matched to controls in an efficient, optimal and computationally flexible way. It uses 
             the idea of sub-sampling in the level of the case, by creating pseudo-observations of controls. The 
             user can select between replacement and without replacement, the number of controls, and several covariates 
             to match upon. See Mamouris (2021) <doi:10.1186/s12874-021-01256-3> for an overview.

Implementation of estimators for inferring 
    the mean of censored cost data. Including the estimators
    BT from Bang and Tsiatis (2000) <doi:10.1093/biomet/87.2.329> and 
    ZT from Zhao and Tian (2001) <doi:10.1111/j.0006-341X.2001.01002.x>.

Significance tests are provided for canonical correlation analysis, including asymptotic tests and a Monte Carlo method.

Combines multiple functions that automate and simplify methods
    commonly employed in cross-cultural psychology, providing a unified
    analysis approach for measurement invariance testing, effect sizes for
    differential item functioning, factor congruence and multi-group
    reliability. Methods follow Fischer and Karl (2019)
    <doi:10.3389/fpsyg.2019.01507> and Gunn, Grimm and Edwards (2020)
    <doi:10.1080/10705511.2019.1689507>.