Author: Søren Højsgaard <sorenh@math.aau.dk>, Steffen L. Lauritzen <lauritzen@math.ku.dk>
Depends: R (>= 3.6.0), methods, gRbase (>= 1.8-5)
Imports: MASS, magrittr, Rcpp (>= 0.11.1)
LinkingTo: Rcpp (>= 0.11.1), RcppArmadillo

Description: Estimation, model selection and other aspects of statistical inference in Graphical Gaussian models with edge and vertex symmetries (coloured Graphical Gaussian models)

Description: Implements graphical interaction models for contingency
tables (i.e. log-linear models) and graphical Gaussian models
for the multivariate normal data (i.e. covariance selection
Depends: R (>= 2.1.0),methods,gRbase (>= 1.6-2),gRain,doBy

Depends: R (>= 2.1.0),methods,gRbase (>= 1.6-2),gRain,doBy, Rcpp (>=

Models for for contingency tables (i.e. log-linear models)
Graphical Gaussian models for multivariate normal data (i.e. covariance
Depends: R (>= 2.15.1),methods,gRbase(>= 1.6-3),gRain,doBy,Rcpp(>=

Depends: R (>= 2.15.2),methods,gRbase(>= 1.6-5),gRain,doBy,Rcpp(>=

Graphical Gaussian models for multivariate normal data (i.e. covariance selection

for the multivariate normal data (i.e. covariance selection models).

Description: Provides the following types of models: Models for for contingency
tables (i.e. log-linear models) Graphical Gaussian models for multivariate
normal data (i.e. covariance selection models) Mixed interaction models.
Depends: R (>= 3.0.2), methods, gRbase (>= 1.8-3)
Imports: igraph, graph, gRain, Rcpp (>= 0.11.1)

Depends: R (>= 3.6.0), methods, gRbase (>= 1.8-6)
Imports: graph, Rgraphviz, igraph, gRain (>= 1.3-4), magrittr, Rcpp (>=

Description: Provides the following types of models: Models for contingency
Documentation about 'gRim' is provided by vignettes included in this
package and the book by Højsgaard, Edwards and Lauritzen (2012,
Imports: graph, Rgraphviz, igraph, stats4, gRain (>= 1.3.10), magrittr,

Imports: graph, Rgraphviz, igraph, stats4, gRain (>= 1.3-4), magrittr,

Title: Semi-Supervised Generalized Structural Equation Modelling
Description: Conducts a semi-gSEM statistical analysis (semi-supervised generalized structural equation modeling) on a data frame of coincident observations of multiple continuous variables, via two functions sgSEMp1() and  sgSEMp2(), representing fittings based on two statistical principles. Principle 1 determines the univariate relationships in the spirit of the Markovian process. The relationship between each pair of system elements, including predictors and the system level response, is determined with the Markovian property that assumes the value of the current predictor is sufficient in relating to the next level variable, i.e., the relationship is independent of the specific value of the preceding-level variable to the current predictor, given the current value. Principle 2 resembles the multiple regression principle in the way multiple predictors are considered simultaneously. Specifically, the first-level predictors to the system level variable, such as, Time and unit level variables, acted on the system level variable collectively by an additive model. This collective additive model can be found with a generalized stepwise variable selection (using the step() function in R, which performs variable selection on the basis of AIC) and this proceeds iteratively.
Author: Junheng Ma <jxm216@case.edu>, Nicholas Wheeler <nrw16@case.edu>, Yifan Xu <ethan.yifanxu@case.edu>, Wenyu Du <wxd97@case.edu>, Abdulkerim Gok <axg515@case.edu>, Jiayang Sun <jiayang.sun@case.edu>
Imports: knitr, MASS, htmlwidgets, DiagrammeR
Packaged: 2015-12-23 14:30:51 UTC; Junheng Ma