The package solves linear system of equations Ax=b by using Preconditioned Conjugate Gradient Algorithm where A is real symmetric positive definite matrix. A suitable preconditioner matrix may be provided by user. This can also be used to minimize quadratic function (x'Ax)/2-bx for unknown x.

Implements the pcgen algorithm, which is a modified version of the standard pc-algorithm,
             with specific conditional independence tests and modified orientation rules. pcgen extends 
			 the approach of Valente et al. (2010) <doi:10.1534/genetics.109.112979> with reconstruction of 
			 direct genetic effects.

Large-scale gene expression studies allow gene network construction to uncover associations among genes. This package is developed for estimating and testing partial correlation graphs with prior information incorporated.

Contains logic for computing the statistical association of variable groups, i.e., gene sets, with respect to the principal components of genomic data.

Piecewise constant hazard models for survival data. 
    The package allows for right-censored, left-truncated, and interval-censored data.

Bayesian network learning using the PCHC, FEDHC, MMHC and variants of these algorithms. PCHC stands for PC Hill-Climbing, a new hybrid algorithm that uses PC to construct the skeleton of the BN and then 
			 applies the Hill-Climbing greedy search. More algorithms and variants have been added, such as MMHC, FEDHC, and the Tabu search variants, PCTABU, MMTABU and FEDTABU. 
			 The relevant papers are:
			 a) Tsagris M. (2021). "A new scalable Bayesian network learning algorithm with applications to economics". Computational Economics, 57(1): 341-367. <doi:10.1007/s10614-020-10065-7>. 
			 b) Tsagris M. (2022). "The FEDHC Bayesian Network Learning Algorithm". Mathematics 2022, 10(15): 2604. <doi:10.3390/math10152604>.

A collection of process capability index functions, such as
  C_p(), C_pk(), C_pm(), and others, along with metadata about each, like
  'LaTeX' equations and 'R' expressions. Its primary purpose is to form a
  foundation for other quality control packages to build on top of, by providing
  basic resources and functions. The indices belong to the field of statistical
  quality control, and quantify the degree to which a manufacturing process is
  able to create items that adhere to a certain standard of quality. For details
  see Montgomery, D. C. (2019, ISBN:978-1-119-39930-8).