Regularized sparse inverse covariance matrix estimation
c(person(given = "Cho-Jui", family = "Hsieh", role = "aut"),
person(given = "Matyas A.", family = "Sustik", role = c("aut",
"cre"), email = "sustik@cs.utexas.edu"), person(given =
"Inderjit S.", family = "Dhillon", role = "aut"), person(given
= "Pradeep", family = "Ravikumar", role = "aut"))
Use Newton's method and coordinate descent to solve the
regularized inverse covariance matrix estimation problem.
Please refer to: Sparse Inverse Covariance Matrix Estimation
Using Quadratic Approximation, Cho-Jui Hsieh, Matyas A. Sustik,
Inderjit S. Dhillon, Pradeep Ravikumar, Advances in Neural
Information Processing Systems 24, 2011, p. 2330--2338.
Cho-Jui Hsieh [aut], Matyas A. Sustik [aut, cre], Inderjit S.
Dhillon [aut], Pradeep Ravikumar [aut]

Regularized Sparse Inverse Covariance Matrix Estimation
Cho-Jui Hsieh [aut], Matyas A. Sustik [aut], Inderjit S.
Dhillon [aut], Pradeep Ravikumar [aut]

Estimate sparse inverse correlation matrix using regularization
Use Newton's method and coordinate descent to solve the
regularized inverse correlation matrix estimation problem.
Please refer to: Sparse Inverse Covariance Matrix Estimation
Using Quadratic Approximation, Cho-Jui Hsieh, Matyas A. Sustik,
Inderjit S. Dhillon, Pradeep Ravikumar, Advances in Neural
Information Processing Systems 24, 2011, p. 2330--2338.

Nery Sofia Huerta-Pacheco, Purificacion Vicente-Galindo
Implement a multivariate analysis interface for questionnaire validation of Likert-type scale variables.
tcltk, gWidgets,mvtnorm, nlme, pander, psych, IMPACT,
multilevel
Tcl/Tk packages gWidgets and XQuartz on Mac.

Nery Sofia Huerta-Pacheco, Purificacion Vicente-Galindo, Mercedes Sanchez Barba, Maria Purificacion Galindo Villardon
tcltk, gWidgets, mvtnorm, nlme, pander, psych, IMPACT,
multilevel

Quantum Walk-Based Data Analysis and Prediction
c(person("Binghuang","Pan",role=c("aut","cre"),
email = "bright1up@163.com"),
person("Zhaoyuan","Yu",role="aut",
email = "yuzhaoyuan@njnu.edu.cn"),
person("Xu","Hu",role="ctb"),
person("Dongshuang","Li",role="ctb"),
person("Yuhao","Teng",role="ctb"),
person("Xinxin","Zhou",role="ctb"))
Binghuang Pan [aut, cre],
Zhaoyuan Yu [aut],
Xu Hu [ctb],
Dongshuang Li [ctb],
Yuhao Teng [ctb],
Xinxin Zhou [ctb]
Modeling and predicting time series based on continuous time quantum.This package can be divided into three parts: Basis Generation, Data Modeling and Prediction, and Model Evaluation according to the analysis process of time series. 'Basis Generation' has realized the continuous time quantum walk simulation for generating modes as the basis. Some regression methods are used to model the observed time series and predict in 'Data Modeling and Prediction', and 'Model Evaluation' can be used to evaluate the data correspondence between two series.