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.
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.has_original has been removed and replaced by has_hatkeep of the function tskrr now stores the hat matrices instead of the original kernel matrices.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.