- unnecessary mentions of dots argument removed from some help pages.
`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.

- the
`permtest`

class now has getters that allow to extract the information from the test. - For LOO-CV, you can use the alternatives
`"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.

- All help pages are checked and corrected where necessary.

- For consistency, the
`g`

matrix in`predict()`

now expects the new nodes to be on the rows.

- the
`permtest`

function is added.

- For consistency, the arguments
`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.- There’s a
`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 networks

- Preliminary function
`impute_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!

- A
`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`

.

- the class
`tskrrTune`

provides a more complete object with all information of tuning. It is a superclass with two real subclasses,`tskrrTuneHeterogenous`

and`tskrrTuneHomogenous`

. - the function
`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

- testing skewed homogenous networks added.
- testing validations added
- testing symmetric calculations
- testing processing of labels
- testing shortcuts
- testing update function

input testing for

`tskrr`

moved to its own function and is also used by`impute_tskrr`

now.

- class
`tskrr`

,`tskrrHeterogenous`

and`tskrrHomogenous`

:- the slot
`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. - the slots
`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 slot
- 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.

- classes
`tskrrHeterogenousImpute`

and`tskrrHomogenousImpute`

are added to allow for storing models with imputed predictions.

`get_loo_fun()`

:- argument
`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.

- argument

- There’s a new function
`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.- functions
`loss_mse()`

and`loss_auc()`

are provided for tuning. `update()`

allows to retrain the model with new lambdas.

- predictions were calculated wrongly in
`tune()`

: fixed. - MSE was calculated wrongly in the previous version of
`tune()`

: fixed.