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.
Need a high-speed mirror for your open-source project?
Contact our mirror admin team at info@clientvps.com.
This archive is provided as a free public service to the community.
Proudly supported by infrastructure from VPSPulse , RxServers , BuyNumber , UnitVPS , OffshoreName and secure payment technology by ArionPay.