Restored kegg.RData and
kegg.pathways.RData (November 2021) to ensure
retro‑compatibility with benchmarked examples.
Added a new loadPathways() function to retrieve the
latest pathway lists
and their union (interactome) as igraph objects, using a
wrapper for the graphite package from the Bioconductor
project.
Added a tutorial vignette “Get Started”, that was only on the Github before.
Various fixed bugs discovered after the release 1.2.3.
Update kegg.RData and
kegg.pathways.RData (February 2025).
Various fixed bugs discovered after the release 1.2.2.
Delete predictSink() function. A general function
for SEM-based out-of-sample prediction is now included in the
SEMdeep package, which uses Deep Neural Network (DNN) and
Machine Learning(ML) algorithms, has been released on CRAN:
10.32614/CRAN.package.SEMdeep
Various fixed bugs discovered after the release 1.2.1.
Added new predictSink() function for SEM-based
out-of-sample prediction of (observed) response y-variables (sink nodes)
given the values of (observed) x-variables (source and mediator) nodes
from the fitted graph structure.
Added new transformData() function implementing
various data trasformation methods to perform optimal scaling for
ordinal or nominal data, and to help relax the assumption of normality
(gaussianity) for continuous data.
Update kegg.RData and
kegg.pathways.RData (February 2024).
Various fixed bugs discovered after the release 1.2.0.
Version 1.2.0 is a major release with several new features, including:
SEMrun() function. The algo =“cggm” based on
high-dimensional GGGM is now implemented with the de-sparsified
(de-biased) nodewise LASSO procedure applied on a Gaussian DAG model.
The overall indices “deviance/df” and “srmr” are now computed using the
observed correlation matrix also in p > n regime, where the estimated
parameters are computed using the “regularized” (lambda corrected)
correlation matrix.
SEMbap() function. New deconfounding methods to
adjust the data matrix by removing latent sources of confounding encoded
in them are implemented. The selected methods are either based on: (i)
Bow-free Acyclic Paths (BAP) search (dalgo = “cggm” or “glpc”), (ii) LVs
proxies as additional source nodes of the data matrix, Y (dalgo = “pc”
or “glpc”) or (iii) spectral transformation of Y (dalgo = “pc” or
“trim”).
SEMdag() function. New two-step DAG estimation from
an input (or empty) graph, using in step 1) graph topological order or
bottom-up search order, and in step 2) parent recovery with the
LASSO-based algorithm are implemented. The estimate linear order are
obtained from a priori graph topological vertex (LO = “TO”) or level (LO
= “TL”) ordering, or with a data-driven vertex or level Bottom-up (LO =
“BU”) based on “glasso” residual variance ordering. The Top-Down (LO =
“TD”) is removed, being the BU more efficient to implement the
topological search order.
Shipley.test() function. Added new argument cmax =
Inf (default). This parameter can be used to perform only those tests
where the number of conditioning variables does not exceed the given
value. Output of the data.frame “dsep” has the same format of the
localCI.test() function.
Various fixed bugs discovered after the release 1.1.3.
Added in SEMrun() function the argumet SE =
c(“standard” or “none”), if algo = “lavaan”.
Added in SEMrun() function the bootstrap resampling
of SE (95% CI), and new argoment n_rep = 1000 (default) to set the
bootstrap samples or permutation flip, if algo = “ricf”.
Added in SEMrun() function the de-sparsified SE (95%
CI) of omega parameters (the elements of the precision matrix), if algo
= “cggm”.
Added new parameterEstimates() function for
parameter estimates output of a fitted SEM for RICF and CGGM algorithms
similar to lavaan.
Updating summary.RICF() and
summary.GGM() functions with
parameterEstimates().
Various fixed bugs discovered after the release 1.1.2.
Added new SEMtree() function for tree-based
structure learning methods. Four methods with graph (type= “ST” or
“MST”) and data-driven (type = “CAT” or “CPDAG”) algorithms are
implemented.
Deprecated activeModule() and
corr2graph() functions in favor of new
SEMtree() function.
Added new dagitty2graph() function for conversion
from a dagitty graph object to an igraph object.
Added new localCI.test() function for local
conditional indipendence (CI) test of missing edges from an acyclic
graph. This function is a wrapper to the localTests()
function from package dagitty.
Added new arguments for SEMace() function: type =
c(“parents”, “minimal”, “optimal”) to choose the conditioning set Z of Y
over X; effect = c(“all”, “source2sink”, “direct”,) to choose the type
of X to Y effect.
Added new argument for SEMdci() function: type =
“ace” from SEMace() function with fixed type=“parents”, and
effect=“direct”.
Change mergeGraph() function. Now the function
combines groups of graph nodes using hierarchical clustering with
prototypes derived from protoclust package or custom membership
attribute (e.g., cluster membership derived from
clusterGraph() function).
Delete argument seed = c(0.05, 0.5, 0.5) in the function
weigthGraph(). Now if group is NOT NULL also node weighting
is actived, and node weights correspond to the sign and P-value of the
z-test = b/SE(b) from glm(node ~ group).
Various fixed bugs discovered after the release 1.1.0.
Version 1.1.0 is a major release with significant changes:
Added new arguments for SEMdag() function: LO = “TO”
or “TD” for knowledge-based topological order (TO) or data-driven
top-down order (TD), and penalty = TRUE or FALSE, binary penalty factors
can be applied to each L1-coefficient.
Deprecated extendGraph() in favor of new
resizeGraph() function, that re-sized graph, removing edges
or adding edges/nodes if they are present or absent in a given reference
network.
Change modelSerch(), interactive procedure is out,
and now a three step procedure is implemented for search strategies with
new SEMdag() and resizeGraph()
functions.
Change SEMgsa() deleting D,A,E p-values with more
performing activation and inhibition pvalues.
Added argument MCX2= TRUE or FALSE for
Shipley.test() function, a Monte Carlo P-value of the
combined C test.
Added new SEMdci() function for differentially
connected genes inference.
Change properties(), now extracted components are
order by component sizes.
Change argument q = q-quantile with q = 1-top/vcount(graph) in
activeModule() function, now the induced graph for the
“rwr” and “hdi” algorithms is defined by the top-n ranking
nodes.
Various fixed bugs.
First stable version on CRAN.
Update kegg.RData (November, 2021).
Added kegg.pathways.RData (November, 2021).
Added pkgdown website.
Various fixed bugs.
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