Runs classical item analysis for multiple-choice test items and polytomous items (e.g., rating scales). The statistics reported in this package can be found in any measurement textbook such as Crocker and Algina (2006, ISBN:9780495395911).

Automates the identification and comparative evaluation of 
    item-removal strategies in exploratory factor analysis, producing 
    transparent summaries (explained variance, loading ranges, reliability) 
    to support comfortable, reproducible decisions. The criteria are 
    based on best practices and established heuristics (e.g., Costello & 
    Osborne (2005) <doi:10.7275/jyj1-4868>, Howard (2016) 
    <doi:10.1080/10447318.2015.1087664>).

Some functions for performing ICA, MICA, Group ICA, and Multilinear ICA are implemented.
    ICA, MICA/Group ICA, and Multilinear ICA extract statistically independent components from single matrix, multiple matrices, and single tensor, respectively.
    For the details of these methods, see the reference section of GitHub README.md <https://github.com/rikenbit/iTensor>.

Fits large-scale regression models with a penalty that 
  restricts the maximum number of non-zero regression coefficients
  to a prespecified value.  While Chu et al (2020) <doi:10.1093/gigascience/giaa044>
  describe the basic algorithm, this package uses Cyclops for an efficient implementation.

Know which loop iteration the code execution is up to by including
    a single, convenient function call inside the loop.

Support for iterators, which allow a programmer to traverse
    through all the elements of a vector, list, or other collection of data.

The iterLap (iterated Laplace approximation) algorithm approximates a
             general (possibly non-normalized) probability density on R^p, by repeated
             Laplace approximations to the difference between current approximation 
             and true density (on log scale). The final approximation is a mixture of
             multivariate normal distributions and might be used for example as a
             proposal distribution for importance sampling (eg in Bayesian applications). 
             The algorithm can be seen as a computational generalization of the Laplace 
             approximation suitable for skew or multimodal densities.