Description: Allows to create, evaluate, and differentiate computational graphs in R. A computational graph is a graph representation of a multivariate function decomposed by its (elementary) operations. Nodes in the graph represent arrays while edges represent dependencies among the arrays. An advantage of expressing a function as a computational graph is that this enables to differentiate the function by automatic differentiation. The 'cgraph' package supports various functions including basic arithmetic, trigonometry functions, and linear algebra functions. It differentiates computational graphs by reverse automatic differentiation. The flexible architecture of the package makes it applicable to solve a variety of problems including local sensitivity analysis, gradient-based optimization, and machine learning.
Collate: 'session.R' 'array.R' 'cgraph.R' 'fun.R' 'math.R' 'misc.R'

Description: Allows to create, evaluate, and differentiate computational graphs in R. A computational graph is a graph representation of a multivariate function decomposed by its (elementary) operations. Nodes in the graph represent arrays while edges represent dependencies among the arrays. An advantage of expressing a function as a computational graph is that this enables to differentiate the function by automatic differentiation. The 'cgraph' package supports various operations including basic arithmetic, trigonometry operations, and linear algebra operations. It differentiates computational graphs by reverse automatic differentiation. The flexible architecture of the package makes it applicable to solve a variety of problems including local sensitivity analysis, gradient-based optimization, and machine learning.

Title: Continuous Time Generalized Rapid Response CUSUM
Description: Allows users to construct the Continuous Time Generalized Rapid
Response CUSUM (CGR-CUSUM), Biswas & Kalbfleisch (2008)
<doi:10.1002/sim.3296> CUSUM, Binary CUSUM and risk-adjusted funnel
plot for survival data. These procedures can be used to monitor
survival processes and detect problems in their quality.
Author: Daniel Gomon [aut, cre] (<https://orcid.org/0000-0001-9011-3743>)