R: Cooperative Learning for Multi-View Analysis

# Cooperative Learning for Multi-View Analysis

## Documentation for package ‘multiview’ version 1.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

multiview-package | Cooperative learning for multiple views using generalized linear models
coef.cv.multiview | Extract coefficients from a cv.multiview object
coef.multiview | Extract coefficients from a multiview object
coef_ordered | Extract an ordered list of standardized coefficients from a 'multiview' or 'cv.multiview' object
coef_ordered.cv.multiview | Extract an ordered list of standardized coefficients from a cv.multiview object
coef_ordered.multiview | Extract an ordered list of standardized coefficients from a multiview object
collapse_named_lists | Collapse a list of named lists into one list with the same name
cox_obj_function | Elastic net objective function value for Cox regression model
cv.multiview | Perform k-fold cross-validation for cooperative learning
dev_function | Elastic net deviance value
elnet.fit | Solve weighted least squares (WLS) problem for a single lambda value
get_cox_lambda_max | Get lambda max for Cox regression model
get_eta | Helper function to get etas (linear predictions)
get_start | Get null deviance, starting mu and lambda max
make_row | Build a block row matrix for multiview
multiview | Perform cooperative learning using the direct algorithm for two or more views.
multiview.control | Internal multiview parameters
multiview.cox.fit | Fit a Cox regression model with elastic net regularization for a single value of lambda
multiview.cox.path | Fit a Cox regression model with elastic net regularization for a path of lambda values
multiview.fit | Fit a GLM with elastic net regularization for a single value of lambda
multiview.path | Fit a GLM with elastic net regularization for a path of lambda values
obj_function | Elastic net objective function value
pen_function | Elastic net penalty value
plot.multiview | Plot coefficients from a "multiview" object
predict.cv.multiview | Make predictions from a "cv.multiview" object.
predict.multiview | Get predictions from a 'multiview' fit object
reshape_x_to_xlist | Return a new list of x matrices of same shapes as those in x_list
response.coxnet | Make response for coxnet
select_matrix_list_columns | Select x_list columns specified by (conformable) list of indices
to_nvar_index | Translate from column indices in list of x matrices to indices in '1:nvars'. No sanity checks for efficiency
to_xlist_index | Translate indices in '1:nvars' to column indices in list of x matrices. No sanity checks
view.contribution | Evaluate the contribution of data views in making prediction
weighted_mean_sd | Helper function to compute weighted mean and standard deviation