Carries out integrative clustering analysis using multiple types of genomic dataset using integrative Non-negative Matrix factorization.

Models, analyzes, and forecasts financial intraday signals. This package
    currently supports a univariate state-space model for intraday trading volume provided
    by Chen (2016) <doi:10.2139/ssrn.3101695>.

For tree ensembles such as random forests, regularized random forests and gradient boosted trees, this package provides functions for: extracting, measuring and pruning rules; selecting a compact rule set; summarizing rules into a learner; calculating frequent variable interactions; formatting rules in latex code.  Reference: Interpreting tree ensembles with inTrees (Houtao Deng, 2019, <doi:10.1007/s41060-018-0144-8>).

Performs Goodness of Fit for regression models 
  using Integrated Regression method. Works for several 
  different fitting techniques.

Estimate the proportions of the null and the reproducibility and
    non-reproducibility of the signal group for the input data set. The Bayes factor
    calculation and EM (Expectation Maximization) algorithm procedures are also
    included.

Provides functions to estimate the intrinsic dimension of a dataset 
  via likelihood-based approaches. Specifically, the package implements the 
  'TWO-NN' and 'Gride' estimators and the 'Hidalgo' Bayesian mixture model. 
  In addition, the first reference contains an extended vignette on the usage of 
  the 'TWO-NN' and 'Hidalgo' models. References: 
    Denti (2023, <doi:10.18637/jss.v106.i09>); 
    Allegra et al. (2020, <doi:10.1038/s41598-020-72222-0>); 
    Denti et al. (2022, <doi:10.1038/s41598-022-20991-1>);
    Facco et al. (2017, <doi:10.1038/s41598-017-11873-y>); 
    Santos-Fernandez et al. (2021, <doi:10.1038/s41598-022-20991-1>).

A variety of methods for estimating intrinsic dimension of data sets (i.e the manifold or Hausdorff dimension of the support of the distribution that generated the data) as reviewed in Johnsson, K. (2016, ISBN:978-91-7623-921-6) and Johnsson, K., Soneson, C. and Fontes, M. (2015) <doi:10.1109/TPAMI.2014.2343220>. Furthermore, to evaluate the performance of these estimators, functions for generating data sets with given intrinsic dimensions are provided.