coglasso 1.1.0

New functions

• New bs() is a wrapping function that single-handedly builds the
  multi-omics networks with coglasso() and selects the best one
  according to the preferred model selection method with
  select_coglasso() in a single function call.

• New get_network() is extracts a network in the igraph format
  from an object either of class coglasso or of class
  select_coglasso.

• New get_pcor() extracts a matrix of partial correlations from an
  object either of class coglasso or of class select_coglasso.

• New plot() can now plot both coglasso and select_coglasso
  objects. The plots will have color coded nodes and weighted edges.

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

• New xestars(), performs 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.

• New xstars() implements the XStARS algorithm seen in the original
  manuscript of collaborative graphical lasso. It performs
  stability-based selection of the c hyperparameter simultaneously
  with lambda_w and lambda_b. It substitutes the more primitive
  stars_coglasso(), now under deprecation.

New features and upgrades

• A new version of the collaborative graphical lasso algorithm,
  is 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 by the 2.0.0 release.

• Added a logo to the package.

• In bs() and coglasso(), the generation procedure oflambda_w
  and lambda_b is now different: 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.

• coglasso() now outputs an object of S3 class coglasso, while
  all functions whose returned object concerns a selected network, like
  bs(),select_coglasso(), and all the other selecting functions
  output a select_coglasso. Both these classes have related
  print() and plot() methods.

• coglasso() gains a lock_lambdas argument to simulate the
  single penalty parameter-behavior of the original glasso. It is
  currently chiefly for testing purposes, so we have not implemented
  any selection procedure for it yet.

Bug fixes

• xstars() now properly selects lambda_b. The selection process
  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.

Deprecations

• In coglasso() and bs(), pX is being deprecated, will be
  unusable from version 1.2.0 (or 2.0.0). It is now substituted by the
  argument p. p can take a vector with the dimensions of multiple
  omics layer, as now the package accepts more than two omics layers.

• stars_coglasso() is being deprecated, will be unusable from
  version 1.2.0 (or 2.0.0). Substituted by xstars().

v1.0.2.9000

Releasing a development version for practical purposes, but feel free to use it. It should be 100% backward compatible, but that will be tested only when the official CRAN release will happen.
The NEWS document is regrettably not updated yet, also for that one we are waiting for having the new 1.1 CRAN release ready. If you are interested to check the massive changes that happened please check the full changelog below here, or give a look at the current state of the NEWS file, which does contain some info.
Full Changelog: v1.0.2...v1.0.2.9000

coglasso 1.0.2

v1.0.2

Increment version number to 1.0.2