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 algorithm. 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).

Provides a work-alike to R's POSIXct class which implements
  360- and 365-day calendars in addition to the gregorian calendar.

Estimates the multidimensional polytomous Rasch model (Rasch, 1961) with conditional maximum likelihood estimation.

We fit causal models using proxies. We implement two stage proximal least squares estimator. E.J. Tchetgen Tchetgen, A. Ying, Y. Cui, X. Shi, and W. Miao. (2020). An Introduction to Proximal Causal Learning. arXiv e-prints, arXiv-2009 <arXiv:2009.10982>.

A method for fitting the entire regularization path 
    of the principal components lasso for linear and
    logistic regression models. The algorithm uses cyclic coordinate descent
    in a path-wise fashion. See URL below for more information on the algorithm.
    See Tay, K., Friedman, J. ,Tibshirani, R., (2014) 'Principal component-guided sparse regression'
    <arXiv:1810.04651>.