linear_filter() takes floating point errors into account when checking whether the alpha values sum to 1.get_kernel is renamed get_kernelmatrix. the function get_kernel is deprecated.tskrrHomogenous and dependent classes are now called tskrrHomogeneous. The same correction is done for tskrrHeterogenous to tskrrHeterogeneous. This might affect code that uses get_loo_fun based on the class name.tskrrHomogeneousImpute and tskrrHeterogeneousImpute were renamed to tskrrImputeHomogeneous and tskrrImputeHeterogeneous to follow the naming convention for the classes.permtest class now has getters that allow to extract the information from the test."edges" and "vertices" for the settings "interaction" and "both" respectively. These give the same results, and make it more clear what actually happens. This is adapted in functions loo(), get_loo_fun(), tune() and those dependent on it.g matrix in predict() now expects the new
nodes to be on the rows.permtest function is added.K and G for the function predict()
have been renamed k and g (lower case).loo now adds the labels to the output (except for linear filters)tune now allows for a one-dimensional grid search for heterogenous
networks. Set onedim = TRUE to avoid a full grid search.has_onedim tells whether the grid search was one dimensional or not.
This is a getter for the appropriate slote in the tskrrTune class.plot_grid allows you to plot the loss in function of the
searched grid after tuning a model. It deals with both 1D and
2D grids and can be used for quick evaluation of the optimal
lambda values.residuals allows you to calculate the residuals based on
the predictions or on the loo values of choice.plot method available now for tskrr objects. It
allows to plot fitted values, residuals, original response and
the results of different loo settings, together with dendrograms
based on the kernel matrices.predict didn't give correct output when only g was passed.
fixed.colnames didn't get the correct labels for homogenous networksimpute_loo is removed from the
package.eigen2hat, eigen2map and eigen2matrix had the second argument
renamed from vec to val. The old name implied that the second argument
took the vectors, which it doesn't!tskrrImpute virtual class is added to represent imputed models.is_symmetric didn't take absolute values to compare. Fixed.show methods for objects are cleaned up.predict gave nonsensical output. Fixed.valid_labels now requires the K and G matrices to have the
same ordering of row and column names. Otherwise the matrix
wouldn't be symmetric and can't be used.linear_filter now forces the alphas to sum up to 1.tune now returns an object of class tskrrTuneHomogenous or
tskrrTuneHeterogenous.tskrrTune provides a more complete object with all
information of tuning. It is a superclass with two real subclasses,
tskrrTuneHeterogenous and tskrrTuneHomogenous.tune now allows to pass the matrices directly so
you don't have to create a model with tskrr first.linear_filter gave totally wrong predictions due to a code error: fixed.
linear_filter returned a matrix when NAs were present: fixed.
fitted now has an argument labels which allows to add the
labels to the returned object.
tskrr now returns an error if the Y matrix is not symmetric or
skewed when fitting a homogenous network.
labels now produces more informative errors and warnings.
In the testing procedures
input testing for tskrr moved to its own function and is
also used by impute_tskrr now.
class tskrr, tskrrHeterogenous and tskrrHomogenous:
has.orig has been removed as it doesn't make sense to
keep the original kernel matrices. It is replaced by a slot has.hat
allowing to store the hat matrices.k.orig and g.orig have been replaced by the slots
Hk and Hg to store the hat matrices. These are more needed for
fitting etc.The function has_original has been removed and replaced by has_hat
The argument keep of the function tskrr now stores the hat matrices
instead of the original kernel matrices.
The function tskrr has lost its argument homogenous. It didn't make
sense to set that by hand.
tskrrHeterogenousImpute and tskrrHomogenousImpute are added
to allow for storing models with imputed predictions.get_loo_fun() :
homogenous removed in favor of x. This
allows for extension of the function based on either an object or
the class of that object.x becomes the first argument.linear_filter that fits a linear filter over
an adjacency matrix. This function comes with a class linearFilter.tune() has a new argument fun that allows to specify a function
for optimization.loss_mse() and loss_auc() are provided for tuning.update() allows to retrain the model with new lambdas.tune(): fixed.tune(): fixed.