`rcausal`

is an R wrapper package containing a range of causal and
statistical model algorithms from the Tetrad library.

`rcausal`

is a program which creates, simulates data from, estimates,
tests, predicts with, and searches for causal and statistical models.
The aim of the program is to provide sophisticated methods in a friendly
interface requiring very little statistical sophistication of the user and no
programming knowledge. It is not intended to replace flexible statistical
programming systems such as Matlab, Splus or R. `rcausal`

is freeware that
performs many of the functions in commercial programs such as Netica, Hugin,
LISREL, EQS and other programs, and many discovery functions these commercial
programs do not perform.

`rcausal`

is unique in the suite of principled search
(“exploration”,“discovery”) algorithms it provides–for example
its ability to search when there may be unobserved confounders of
measured variables, to search for models of latent structure, and to search for
linear feedback models–and in the ability to calculate predictions of
the effects of interventions or experiments based on a model. All of its
search procedures are “pointwise consistent”–they are guaranteed to
converge almost certainly to correct information about the true structure in
the large sample limit, provided that structure and the sample data satisfy
various commonly made (but not always true!) assumptions.

`rcausal`

is limited to models of categorical data (which can also be used
for ordinal data) and to linear models (“structural equation models”)
with a Normal probability distribution, and to a very limited class of
time series models. The `rcausal`

programs describe causal models in
three distinct parts or stages: a picture, representing a directed graph
specifying hypothetical causal relations among the variables; a specification
of the family of probability distributions and kinds of parameters associated
with the graphical model; and a specification of the numerical values of
those parameters.

The program and its search algorithms have been developed over several years with support from the National Aeronautics and Space Administration and the Office of Naval Research. Joseph Ramsey has implemented most of the program, with substantial assistance from Frank Wimberly.

1 2 3 4 5 | ```
data("charity") #Load the charity dataset
tetradrunner <- tetradrunner(algoId = 'fges',df = charity,scoreId = 'fisher-z', dataType = 'continuous',alpha=0.1,faithfulnessAssumed=TRUE,maxDegree=-1,verbose=TRUE) #Compute FGES search
fges$parameters #Show the FGES's parameters
tetradrunner$nodes #Show the result's nodes
tetradrunner$edges #Show the result's edges
``` |

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