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**ggm**: Functions for graphical Markov models**blkdiag**: Block diagonal matrix

# Block diagonal matrix

### Description

Block diagonal concatenation of input arguments.

### Usage

1 |

### Arguments

`...` |
Variable number of matrices |

### Value

A block diagonal matrix `diag(M1, M2, ...)`

.

### Author(s)

Giovanni M. Marchetti

### See Also

`diag`

### Examples

1 2 | ```
X <- c(1,1,2,2); Z <- c(10, 20, 30, 40); A <- factor(c(1,2,2,2))
blkdiag(model.matrix(~X+Z), model.matrix(~A))
``` |

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- adjMatrix: Adjacency matrix of a graph
- AG: Ancestral graph
- allEdges: All edges of a graph
- anger: Anger data
- basiSet: Basis set of a DAG
- bfsearch: Breadth first search
- binve: Inverts a marginal log-linear parametrization
- blkdiag: Block diagonal matrix
- blodiag: Block diagonal matrix
- checkIdent: Identifiability of a model with one latent variable
- cmpGraph: The complementary graph
- conComp: Connectivity components
- correlations: Marginal and partial correlations
- cycleMatrix: Fundamental cycles
- DAG: Directed acyclic graphs (DAGs)
- derived: Data on blood pressure body mass and age
- DG: Directed graphs
- diagv: Matrix product with a diagonal matrix
- drawGraph: Drawing a graph with a simple point and click interface.
- dSep: d-separation
- edgematrix: Edge matrix of a graph
- essentialGraph: Essential graph
- findPath: Finding paths
- fitAncestralGraph: Fitting of Gaussian Ancestral Graph Models
- fitConGraph: Fitting a Gaussian concentration graph model
- fitCovGraph: Fitting of Gaussian covariance graph models
- fitDag: Fitting of Gaussian DAG models
- fitDagLatent: Fitting Gaussian DAG models with one latent variable
- fitmlogit: Multivariate logistic models
- fundCycles: Fundamental cycles
- ggm: The package 'ggm': summary information
- glucose: Glucose control
- grMAT: Graph to adjacency matrix
- icf: Iterative conditional fitting
- In: Indicator matrix
- InducedGraphs: Graphs induced by marginalization or conditioning
- isAcyclic: Graph queries
- isADMG: Acyclic directed mixed graphs
- isAG: Ancestral graph
- isGident: G-identifiability of an UG
- MAG: Maximal ancestral graph
- makeMG: Mixed Graphs
- marg.param: Link function of marginal log-linear parameterization
- MarkEqMag: Markov equivalence of maximal ancestral graphs
- MarkEqRcg: Markov equivalence for regression chain graphs.
- marks: Mathematics marks
- mat.mlogit: Multivariate logistic parametrization
- Max: Maximisation for graphs
- MRG: Maximal ribbonless graph
- msep: The m-separation criterion
- MSG: Maximal summary graph
- null: Null space of a matrix
- parcor: Partial correlations
- pcor: Partial correlation
- pcor.test: Test for zero partial association
- plotGraph: Plot of a mixed graph
- powerset: Power set
- rcorr: Random correlation matrix
- RepMarBG: Representational Markov equivalence to bidirected graphs.
- RepMarDAG: Representational Markov equivalence to directed acyclic...
- RepMarUG: Representational Markov equivalence to undirected graphs.
- RG: Ribbonless graph
- rnormDag: Random sample from a decomposable Gaussian model
- rsphere: Random vectors on a sphere
- SG: summary graph
- shipley.test: Test of all independencies implied by a given DAG
- SimpleGraphOperations: Simple graph operations
- stress: Stress
- surdata: A simulated data set
- swp: Sweep operator
- topSort: Topological sort
- transClos: Transitive closure of a graph
- triDec: Triangular decomposition of a covariance matrix
- UG: Defining an undirected graph (UG)
- unmakeMG: Loopless mixed graphs components
- UtilityFunctions: Utility functions