Changelog

Source: NEWS.md

coglasso (development version)

• Changed the xstars() algorithm to perform the stability-based selection of the c hyperparameter simultaneously with lambda_w and lambda_b.

• Began the deprecation process of stars_coglasso(), renamed to the more accurate xstars(). Made also some implementation improvements to xstars() that made it faster.

• Corrected major bug in xstars(), as the selection of lambda_b was never really happening, and we were selecting lambda_w twice, instead. This will lead to inevitable backward incompatibilities, at least of the results of the previous version of xstars(), that will not be reproducible.

• Implemented select_coglasso() for handling all possible present (and future) model selection methods from a single wrapping function. For the moment it allows to perform model selection with either eXtended StARS, eXtended Efficient StARS (see below), or eBIC.

• Implemented bs() a single wrapping function to build the multi-omics networks with coglasso() and select the best one according to the preferred model selection method with select_coglasso() in a single function call.

• Implemented xestars(), performing eXtended Efficient StARS, a significantly faster version of XStARS.
  How much faster?
  In our tests, xestars() runs 80-90% faster than xstars(), even more in specific instances.
  What features make xestars() faster?
  First of all, the check for stability that in xstars() is performed after iterating throughout all the penalty parameters, here is implemented as a stopping criterion. Hence, less penalty parameters are explored, moreover usually are excluded those that lead to denser network (and so to longer network estimations).
  Second, the use of vectors instead of matrixes to keep track of the network variabilities makes the algorithm proceed faster, for the former are easier and lighter objects to deal with.
  Third, a new sampling strategy allows a the computation of as many correlation matrixes (the input to coglasso()), as the number of repetitions of the algorithm only once at the beginning of the algorithm. The original strategy performs this every time the algorithm switches from the selection of lambda_w to that of a lambda_b (which can happen several times). Especially for larger data sets, this consists a huge difference.
  How do xstars() and xestars() differ in results?
  The impressive increase in speed comes with some minor costs.
  The different sampling strategy that guarantees not only a faster, but also a fairer parameter selection, may lead to different selected hyperparameters between the older and the new methodology.

• Created the classes coglasso and select_coglasso, with related print() and plot() methods. These former is returned when using coglasso(), while the latter when using any function that performs model selection as its final step (i.e. bs(),select_coglasso(), and all the model selection functions).

• Created a plotting module. Now every function of the package that produces or selects a network generates an object that can be directly be plotted with plot().

• Networks produced with plot() have weighted edges.

• Began the implementation of a new version of the collaborative graphical lasso algorithm, now able to accept more than two omics layers. This new version, called general |D| version, provides the same results for two omics layers, but it is slightly slower, so the general |D| algorithm will only be used when necessary. The current version has convergence issues for most values of c. Hopefully this will be fixed before the 2.0 release.

• Began the deprecation process of the argument pX of the functions coglasso() and bs(), substituted by the argument p, that can take a vector with the dimensions of multiple omics layer, as now the package accepts more than two omics layers.

• Added a logo to the package, but it will be changed to a white background.

• Smaller functional bugs have been corrected.

• A new parameter for coglasso(), lock_lambdas, has been introduced to simulate the single penalty parameter-beahavior of the original glasso. It is currently chiefly for testing purposes, so we have not implemented any selection procedure for it, yet.

• Changed the generation procedures for lambda_w and lambda_b: the maximum values will be, respectively, the highest within Pearson’s correlation value and the highest between Pearson’s correlation value. Moreover, in previous versions the granularity of the search grid increased as the values of lambda_w and lambda_b decreased. As our major interest lies in sparser network, this granularity has now been inverted.

• Implemented the new functions get_network() and get_pcor(). These two functions extract, respectively, a network in the igraph format or a matrix of partial correlations from an object either of class coglasso or of class select_coglasso. # coglasso 1.0.2

• Reformatted the Description field of DESCRIPTION file according to CRAN reviewer’s comments.

coglasso 1.0.1

• Made the Description field of the DESCRIPTION file more explanatory and added the official reference to the main method in it.

• Added the official reference to the main method in the README file.

• Changed to in man/stars_coglasso.Rd.

coglasso 1.0.0

• Initial CRAN submission.