cmst: Causal Model Selection Tests for outcomes given driver and...
In byandell/CausalMST: Causal Model Selection Hypothesis Tests

Run CMST tests the causal direction between the first outcome (y1) and all other outcomes (y2, ...), adjusting for the driver and covariates. The models tested (ignoring covariates and noise) are:

M1.y2.y1.z : y2 <- y1 <- z
M2.y1.y2.z : y1 <- y2 <- z
M3.y1.z.y2 : y1 <- z -> y2
M4.y12.z : y1 <- z -> y2 and y1 <-> y2

in which the arrows indicate direction of causality. For M4, the directionality between y1 and y2 is ambiguous. The noise on outcomes yi is assumed to be normal. The driver z is assumed to be causal for one or more outcomes (y1, y2, ...), and may be categorical or continuous. The covariates X, qualitative or quantitative, may act additively or be interactive with the driver. The driver is the driver for outcomes, with covariates acting on outcomess. The resp_names, addcov and intcov names must all be valid names in the outcomes data frame.

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cmst(driver, outcomes, covariates = NULL, addcov = NULL, intcov = NULL,
  method = c("par", "non.par", "joint", "all"), penalty = c("bic", "aic",
  "both"), verbose = FALSE, fitFunction = fitDefault, ...)

`driver`	data frame with drivers
`outcomes`	data frame with outcomes
`addcov, intcov`	additive and interactive covariate names for responses
`method`	method for CMST test (parametric, non-parametric, joint or all three); can provide more than one value.
`penalty`	type of information criteria penalty (for BIC or AIC)
`verbose`	verbose output if `TRUE`
`fitFunction`	log likelihood calculation function; see `fitDefault`
`...`	possible additional arguments
`resp_names`	response names