#' scores
#'
#' extract the factor score matrix for a multivariate model
#' @param ... extra args
#' @param x the model fit
#' @export
scores <- function(x,...) UseMethod("scores")
#reduce <- function(x, Y, ...) UseMethod("reduce")
#' loadings
#'
#' extract the loadings matrix (the variable coefficients) for a multivariate model.
#' @param x the model fit
#' @param ... extra args
#' @export
loadings <- function(x,...) UseMethod("loadings")
#' ncomp get the number of components in the estimated model
#'
#' @param x the model fit
#' @export
ncomp <- function(x) UseMethod("ncomp")
#ncol <- function(x) UseMethod("ncol")
#ncol.default <- base::ncol
#nrow <- function(x) UseMethod("nrow")
#nrow.default <- base::nrow
#dim <- function(x) UseMethod("dim")
#dim.default <- base::dim
#' @export
correlations <- function(x,...) UseMethod("correlations")
#' cross_validate a model
#'
#' @param the model fit
#' @param ... extra args
#' @export
cross_validate <- function(x, ...) UseMethod("cross_validate")
#' @export
nested_cv <- function(x, ...) UseMethod("nested_cv")
#' bootstrap a model
#' @param x the model fit
#' @param nboot the number of bootstrap resamples
#' @param ... extra args
#' @export
bootstrap <- function(x, nboot, ...) UseMethod("bootstrap")
#' resample data from a model fit
#' @param x the model fit
#' @param ... extra args
#' @export
resample <- function(x, ...) UseMethod("resample")
#' block_apply
#' apply a function to each block of a multi-block data structure.
#' @param x the multi-block data
#' @param f the function to apply
#' @param ... extra args
#' @export
block_apply <- function(x, f, ...) UseMethod("block_apply")
#' nblocks
#' extract the number of blocks in a mutli-block data structure or model
#'
#' @param x the object
#' @export
nblocks <- function(x) UseMethod("nblocks")
#' @export
#' @param x the model fit object
#' @param ... extra args
permutation <- function(x, ...) UseMethod("permutation")
#' @export
#' @param x the model fit object
#' @param type the tpye of variance to account for.
#' @param ncomp the number of components to include.
vaf <- function(x, type, ncomp) UseMethod("vaf")
#' @param x the model fit object
#' @export
performance <- function(x, yobs, ncomp, folds, metric, ...) UseMethod("performance")
#' @param x the model fit object
#' @export
permute_refit <- function(x, ...) UseMethod("permute_refit")
#' @param x the model fit object
#' @export
permute <- function(x, ...) UseMethod("permute")
#' @param x the model fit object
#' @export
refit <- function(x, ...) UseMethod("refit")
#' @param x the model fit object
#' @export
reprocess <- function(x, ...) UseMethod("reprocess")
#' @param x the model fit object
#' @export
supplementary_predictor <- function(x, ...) UseMethod("supplementary_predictor")
#' @param x the model fit object
#' @export
supplementary_loadings <- function(x,...) UseMethod("supplementary_loadings")
#' @param x the model fit object
#' @export
summarize_loadings <- function(x, stat, ncomp, ...) UseMethod("summarize_loadings")
#' @param x the model fit object
#' @export
compose <- function(x,y) UseMethod("compose")
#' @param x the model fit object
#' @export
concat <- function(x,...) UseMethod("concat")
#' @param x the model fit object
#' @export
combine <- function(x,...) UseMethod("combine")
#' @export
singular_values <- function(x) UseMethod("singular_values")
#' @param x the model fit object
#' @export
truncate <- function(x, ncomp) UseMethod("truncate")
#' @param x the model fit object
#' @export
subset_rows <- function(x, idx) UseMethod("subset_rows")
#' partial_scores
#'
#' compute the partial scores from a multivariate model using a subset of the input
#'
#' @param x the model fit
#' @export
partial_scores <- function(x, ...) UseMethod("partial_scores")
#' contributions
#'
#' compute the contributions (of observations, variables, tables) to a model.
#' @param x the model fit
#' @param ... extra args
#' @export
contributions <- function(x, ...) UseMethod("contributions")
#' reproducibility
#'
#' compute a measure of the reproducibility of a model under replication.
#' @param x the model fit
#' @param folds
#' @param metric
#' @param ...
#' @export
reproducibility <- function(x, folds, metric, ...) UseMethod("reproducibility")
#' reconstruct the data with some number of components
#'
#' @param x the model fit
#' @param newdata newdata to be inverse projected (optional)
#' @param comp the components to use
#' @param ... extra args
#' @export
reconstruct <- function(x, newdata, comp,...) UseMethod("reconstruct")
#' get the residuals of a model, after removing the first \code{ncomp} components
#'
#' @param x the model fit
#' @param ncomp the number of components
#' @param ... extra arguments
residuals <- function(x, ncomp, ...) UseMethod("residuals")
#' project_cols
#'
#' project supplementary variables in to the subspace defined by the model
#'
#' @param x the model fit
#' @param newdata a matrix or vector of new variables(s)
#' @param ... extra args
#' @export
project_cols <- function(x, newdata, ...) UseMethod("project_cols")
#' project
#'
#' project supplementary observations in to the subspace defined by the model
#'
#' @param x the model fit
#' @param newdata a matrix or vector of new obervations(s)
#' @param ... extra args
#' @export
project <- function(x, newdata, ...) UseMethod("project")
#' project a selected subset of indices onto the subspace
#'
#' @inheritParams project
#' @param the column indices to select in the projection matrix
#' @export
partial_project <- function(x, newdata, colind) UseMetod("partial_project")
#' project a single 'block' of data onto the subspace
#'
#' @inheritParams project
#' @param block the block id to select in the block projection matrix
#' @export
block_project <- function(x, newdata, block,...) UseMethod("block_project")
#' partial_projector
#'
#' extract a partial projector from a fitted model
#'
#' @param x the model fit
#' @param colind a vector of unique subindices
#' @param ... extra args
#' @export
partial_projector <- function(x, colind, ...) UseMethod("partial_projector")
#' projection_fun
#'
#' return a function that projects data to lower-dimensional space
#'
#' @export
projection_fun <- function(x, colind, ...) UseMethod("projection_fun")
#' project_table
#'
#' project a block of data into the subspace defined by the model.
#'
#' @export
project_table <- function(x, supY, supX, ncomp, ...) UseMethod("project_table")
#' @export
procrusteanize <- function(x,...) UseMethod("procrusteanize")
#' @export
pre_process <- function(obj, X, ...) UseMethod("pre_process")
#' @export
reverse_pre_process <- function(obj, X, ...) UseMethod("reverse_pre_process")
#' block_lengths
#'
#' extract the vector of lengths of each block in a multi-block object
#' @param object the object
#' @export
block_lengths <- function(object) UseMethod("block_lengths")
#' block_index_list
#'
#' extract the list of indices associated with each block in a multi-block object
#' @param object the object
#' @export
block_index_list <- function(object) UseMethod("block_index_list")
#' @export
project_copy <- function(x, ...) UseMethod("project_copy")
#' @param center whether to mean center the columns
#' @param scale whether to scale the columns
#' @export
pre_processor <- function(x, center, scale) UseMethod("pre_processor")
#' turn a model object into a classifier
#'
#' @param x the fitted object
#' @export
classifier <- function(x, ...) UseMethod("classifier")
#' apply a rotation matrix to a solution
#'
#' @param x the object to rotate
#' @param rot the rotation matrix to apply
#' @param ... extra arguments
#' @export
rotate <- function(x, rot,...) UseMethod("rotate")
#' get_block
#'
#' @export
#' @param x the object to retrive the block of variables from.
#' @param i the index of the block.
get_block <- function (x, i,...) { UseMethod("get_block") }
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