moretrees_design_tree: Here's a brief description. 'moretrees_design_tree' converts...
In IQSS/moretrees: Multi-Outcome Regression with Tree-Structured Shrinkage

Description Usage Arguments Value Model Description See Also

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1	moretrees_design_tree(y, X, W = NULL, outcomes, tr)

`y`	Vector of length n containing outcomes data. If family = "bernoulli", y must be an integer vector where 1 = success, 0 = failure. If family = "gaussian", y must be a numeric vector containing continuous data.
`X`	An n x K matrix of exposure data, where K is the dimension of the exposure. Grouping of the outcomes will be based on their relationships with the variables in X.
`W`	Matrix of covariates of dimension n x m. Coefficients for these variables do not affect grouping of the outcomes. Default is NULL (no covariates).
`outcomes`	is a character vector of length n, where entry i
`tr`	is an igraph tree, where the leaves represent outcomes

A list containing the following elements: y: Re-ordered outcome vector. X: Re-ordered exposure matrix. W: Re-ordered covariate matrix. outcomes_units: list of length equal to the number of unique outcomes. Each element of the list is an integer vector indicating which units (entries of y_reord, rows of X_reord) correspond to each outcomes. #' outcomes_nodes: list of length equal to the number of unique nodes. Each element of the list is an integer vector indicating which outcomes are descendants of each node. ancestors: list of length equal to the number of unique outcomes. Each element of the list is an integer vector indicating which nodes on the tree (including leaves) are ancestors of the corresponding outcome.

Describe group spike and slab prior and all parameters here.

Other MOReTreeS functions: moretrees_compute_betas(), moretrees_compute_thetas(), moretrees_design_matrix(), moretrees_init_W_logistic(), moretrees_init_logistic(), moretrees_init_rand(), moretrees()