Implementation of a procedure---Domingue (2012) <https://eric.ed.gov/?id=ED548657>, Domingue (2014) <doi:10.1007/s11336-013-9342-4>; see also Karabatsos (2001) <https://psycnet.apa.org/record/2002-01665-005> and Kyngdon (2011) <doi:10.1348/2044-8317.002004>---to test the single and double cancellation axioms of conjoint measure in data that is dichotomously coded and measured with error.

Generates synthetic data distributions to enable testing various modelling techniques in ways that real data does not allow. Noise can be added in a controlled manner such that the data seems real. This methodology is generic and therefore benefits both the academic and industrial research.

Functions calculating Conley (1999) <doi:10.1016/S0304-4076(98)00084-0> standard errors. The package started by merging and extending multiple packages and 
  other published scripts on this econometric technique. It strongly emphasizes computational optimization. Details are available in the function documentation and in 
  the vignette.

With this package you can run 'ConMET' locally in R. 'ConMET' is an R-shiny application that facilitates performing and evaluating confirmatory factor analyses (CFAs) and is useful for running and reporting typical measurement models in applied psychology and management journals. 'ConMET' automatically creates, compares and summarizes CFA models. Most common fit indices (E.g., CFI and SRMR) are put in an overview table. ConMET also allows to test for common method variance. The application is particularly useful for teaching and instruction of measurement issues in survey research. The application uses the 'lavaan' package (Rosseel, 2012) to run CFAs.

The biases introduced in association measures, particularly mutual information, 
    are influenced by factors such as tumor purity, mutation burden, and hypermethylation. 
    This package provides the estimation of conditional mutual information (CMI) and its 
    statistical significance with a focus on its application to multi-omics data. Utilizing 
    B-spline functions (inspired by Daub et al. (2004) <doi:10.1186/1471-2105-5-118>), the package offers tools to estimate the association between heterogeneous multi-
    omics data, while removing the effects of confounding factors. This helps to unravel complex
    biological interactions. In addition, it includes methods to evaluate the statistical significance 
    of these associations, providing a robust framework for multi-omics data integration and 
    analysis. This package is ideal for researchers in computational biology, bioinformatics, 
    and systems biology seeking a comprehensive tool for understanding interdependencies in 
    omics data.