Version 2.2.7
Adjustments:
- Functions optPenaltyPchordal, ridgePchordal, ridgePsign, and
support4ridgeP (temporarily) deprecated (for major adjustments)
- Replaced if() conditions comparing class() to string with
evaluations using inherits()
Documentation:
Version 2.2.6
Documentation:
- Canonicalization of URLs.
- Update of published papers
Version 2.2.5
Documentation:
- Improved documentation and added new pkgdown
documentation website.
- NEWS file moved to markdown format instead of .Rd and available on
the website
Version 2.2.4
Adjustments:
- Documentation roxygenized.
- More selective importing and exporting.
- S3 implementation of
ridgeP
output.
Version 2.2.3
Documentation:
- Updated
CITATION
file
- Updated
README
file
Bug fixes:
- Fixed bug in
GGMpathStats
: Incorrectly stated before
that all igraph layouts were supported. Now they indeed are
supported.
Adjustments:
- Bioconductor dependencies are now automatically installed upon first
installation of rags2ridges.
GGMpathStats
now has additional visualization options:
It can handle all layout functions supported by igraph. Moreover, it is
now possible to specify custom coordinates for node-placement.
Version 2.2.2
Notifications:
- Hot fix due to class changes in “matrix”. No major visible user
changes.
CNplot
function again updated: higher max. iterations
for Lanczos method
Version 2.2.1
Notifications:
- Hot fix due to new RNG. No visible user changes.
Version 2.2
Notifications:
optPenalty.LOOCV
is deprecated. Please use
optPenalty.kCV
instead
optPenalty.LOOCVauto
is deprecated. Please use
optPenalty.kCVauto
instead
Version 2.1.1
Documentation:
- Updated
CITATION
file
- Updated
README
file
Adjustments:
sparsify
now has an additional thresholding option:
‘connected’
Version 2.1
Documentation:
- Updated
CITATION
file
- Updated
README
file
Bug fixes:
- Fixed bug in
Ugraph
:
- Incorrectly stated before that all igraph layouts were
supported.
- Now they indeed are supported.
Notifications:
conditionNumberPlot
is deprecated. Please use
CNplot
instead
- Features of the
CNplot
function (above and beyond
conditionNumberPlot
):
- The
digitLoss
and rlDist
arguments have
been removed
- These arguments have been replaced with the logical argument
Iaids
Iaids = TRUE
amends the basic condition number plot
with interpretational aids
- These aids are the approximate loss in digits of accuracy and and
approximation of the acceleration along the regularization path of the
condition number
- Argument
main
is now a character argument
- Argument
value
now by default takes the value 1e-100
(convenient)
- Now uses C++ functionalty for additional speed
Adjustments:
edgeHeat
now has aligned x-axis labels
- The visualizations of the
optPenalty.LOOCV
and
optPenalty.aLOOCV
functions now will no longer produce
horizontal and/or vertical lines that fall outside the boundaries of the
figure
optPenalty.LOOCV
now uses log-equidistant penalty grid
for optimal penalty parameter determination (this also enhances the
visualization)
- New features updated
optPenalty.aLOOCV
function:
- Function has been sped up by killing redundant inversion
- now uses log-equidistant penalty grid for optimal penalty parameter
determination (this also enhances the visualization)
- New features updated
Ugraph
function:
- One can now also specify vertex placement by coordinate
specification
- Now outputs, for convenience, the vertex coordinates of the plotted
graph
ridgePathS
has been sped up by killing redundant
inversion
- The
covML
function has been amended with an argument
that indicates if a correlation matrix (instead of an ML estimate of a
covariance matrix) is desired. This offers more flexibility. One can now
get the ML estimate of the covariance matrix, the ML estimate of the
covariance matrix on standardized data, as well as the correlation
matrix
- The
optPenalty.LOOCVauto
function has been amended with
an argument that indicates if the evaluation of the LOOCV score should
be performed on the correlation scale
- The
optPenalty.LOOCV
function has been amended with an
argument that indicates if the evaluation of the LOOCV score should be
performed on the correlation scale
- The
optPenalty.aLOOCV
function has been amended with an
argument that indicates if the evaluation of the approximate LOOCV score
should be performed on the correlation scale
Version 2.0
Documentation:
- Added this
NEWS
file!
- Updated (and corrected)
CITATION
file
- Added
README
file
- Added (selective) import statements for default packages as required
for R-devel
Additions:
- rags2ridges
now uses Rcpp and
RcppArmadillo
with core functions written in
C++
. The package should now
be at least two orders of magnitude faster in most cases.
- Added, next to the core module, the fused ridge module. The fused
module provides functionality for the estimation and graphical modeling
of multiple precision matrices from multiple high-dimensional data
classes. Functions from this module are generally suffixed with
.fused
. Functions tied to (or added with) this module are:
isSymmetricPD
isSymmetricPSD
is.Xlist
default.target.fused
createS
getKEGGPathway
kegg.target
pooledS
pooledP
KLdiv.fused
ridgeP.fused
optPenalty.fused.grid
print.optPenaltyFusedGrid
plot.optPenaltyFusedGrid
optPenalty.fused.auto
optPenalty.fused
default.penalty
fused.test
print.ptest
summary.ptest
hist.ptest
plot.ptest
sparsify.fused
GGMnetworkStats.fused
GGMpathStats.fused
- The following functions were added to the core module:
- Added miscellaneous (hidden) functions.
Bug fixes:
- Fixed bugs in
GGMpathstats
:
- Code no longer breaks down if variable names are absent.
- Now properly handles singleton pathsets.
- Fixed bug in
sparsify
: Now always returns symmetric
objects
Adjustments:
- Argument
verticle
as used in various functions has been
renamed to vertical
. Sorry for any inconvenience.
- Internal usage of
ridgeS
replaced by the faster
C++-dependent counterpart ridgeP
- New features updated
conditionNumberPlot
function:
- Function has been sped up
- Now uses log-equidistant grid for visualization
- Now includes option to additionally plot the approximate loss in
digits of accuracy
Notifications:
ridgeS
is deprecated. Please use ridgeP
instead
- Future versions of rags2ridges will be subject to changes in naming
conventions
Version 1.4
Additions:
- Inclusion hidden function
.pathContribution
for usage
in GGMpathStats
function
- Inclusion hidden function
.path2string
for usage in
GGMpathStats
function
- Inclusion hidden function
.pathAndStats
for usage in
GGMpathStats
function
- Inclusion hidden function
.cvl
for usage in
optPenalty.LOOCVauto
function
- Inclusion hidden function
.lambdaNullDist
for usage in
GGMblockNullPenalty
function
- Inclusion hidden function
.blockTestStat
for usage in
GGMblockTest
function
- Inclusion function that expresses the covariance between a pair of
variables as a sum of path weights:
GGMpathStats
- Inclusion function that determines the optimal penalty parameter
value by application of the Brent algorithm to the LOOCV log-likelihood:
optPenalty.LOOCVauto
- Inclusion function that generates the distribution of the penalty
parameter under the null hypothesis of block independence:
GGMblockNullPenalty
- Inclusion function that performs a permutation test for block
structure in the precision matrix:
GGMblockTest
- Inclusion wrapper function:
fullMontyS
Bug fixes:
- Corrected small error in
evaluateSfit
function. The
dir
argument was not properly used previously.
Adjustments:
- New features updated
optPenalty.aLOOCV
function:
- For scalar matrix targets the complete solution path depends on only
1 eigendecomposition and 1 matrix inversion. Meaning: the function is
sped up somewhat by lifting redundant inversions out of
for
loops.
- Optional graph now plots the approximated LOOCV negative
log-likelihood instead of ln(approximated LOOCV negative
log-likelihood).
- Legend in optional graph has been adapated accordingly.
- New features updated
optPenalty.LOOCV
function:
- Optional graph now plots the LOOCV negative log-likelihood instead
of ln(LOOCV negative log-likelihood).
- Legend in optional graph has been adapated accordingly.
- New features updated
default.target
function:
- Inclusion new default target option:
type = DIAES
.
Gives diagonal matrix with inverse of average of eigenvalues of S as
entries.
- New features updated
GGMnetworkStats
function:
- Now also assesses (and returns a logical) if graph/network is
chordal.
- Now also includes assesment of the eigenvalue centrality.
- Now includes option to have list or table output.
- New features updated
ridgePathS
function:
- Sped up considerably for rotation equivariant alternative estimator.
By avoidance of redundant eigendecompositions and inversions.
- Now catches breakdown due to rounding preculiarities when
plotType = "pcor"
.
- New features updated
sparsify
function:
- Inclusion new thresholding function
top
: retainment of
top elements based on absolute partial correlation.
- Inclusion output option: When
output = "light"
, only
the (matrix) positions of the zero and non-zero elements are
returned.
- Function no longer dependent on GeneNet; now makes direct use of fdrtool.
- Function now also prints some general information on the number of
edges retained.
Version 1.3
Additions:
- Inclusion hidden function
.ridgeSi
for usage in
conditionNumberPlot
function.
- Inclusion hidden function
.eigShrink
for usage in
(a.o.) ridgeS
function.
- Inclusion function calculating various network statistics from a
sparse matrix:
GGMnetworkStats
- Inclusion function for visual inspection fit of regularized
precision matrix to sample covariance matrix:
evaluateSfit
- Inclusion function for visualization of regularization paths:
ridgePathS
- Inclusion function for default target matrix generation:
default.target
Adjustments and name changes:
- New features updated
evaluateS
function:
- The printed output of the
evaluateS
function is now
aligned
- Calculation spectral condition number has been improved
conditionNumber
function now called
conditionNumberPlot
. The updated function has new features:
- Main plot can now be obtained with either the spectral (l2) or the
(approximation to) l1 condition number
- Main plot can now be amended with plot of the relative distance to
the set of singular matrices
- The title of the main plot can now be suppressed
- One can now obtain numeric output from the function: lambdas and
condition numbers
- New features updated
sparsify
function:
- Changed
type = c("threshold", "localFDR")
to
threshold = c("absValue", "localFDR")
(clarifying
nomenclature)
- Changed
threshold
to absValueCut
(clarifying nomenclature)
- Will now output both sparsified partial correlation/standardized
precision matrix and the sparsified precison matrix, when input consists
of the unstandardized precision matrix
- New features updated
ridgeS
function:
- Contains an improved evaluation of the target matrix possibly being
a null matrix
- Now evaluates if a rotation equivariant alternative estimator ensues
for a given target matrix
- When rotation equivariant alternative estimator ensues, computation
is sped up considerably by circumventing the matrix square root
optPenaltyCV
function now called
optPenalty.LOOCV
, for sake of (naming) consistency. The
updated function has new features:
targetScale
option has been removed
- Replaced
log
in optional graph by ln
- Visual layout of optional graph now more in line with
recommendations by Tufte (regarding data-ink ratio)
- New features updated
optPenalty.aLOOCV
function:
- Replaced
log
in optional graph by ln
- Visual layout of optional graph now more in line with
recommendations by Tufte (regarding data-ink ratio)
- Computation optimal penalty in
conditionNumberPlot
,
optPenalty.aLOOCV
and optPenalty.LOOCV
functions sped up considerably for rotation equivariant alternative
estimator. By usage new ridgeS and avoidance of redundant
eigendecompositions
- Default target in
ridgeS
,
conditionNumberPlot
, optPenalty.aLOOCV
and
optPenalty.LOOCV
now \code{“DAIE” option from
default.target
Version 1.2
Additions:
- Inclusion function for ML estimation of the sample covariance
matrix:
covML
- Inclusion function for approximate leave-one-out cross-validation:
optPenalty.aLOOCV
- Inclusion function
conditionNumber
to visualize the
spectral condition number over the regularization path
- Inclusion function
evaluateS
to evaluate basic
properties of a covariance matrix
- Inclusion function
KLdiv
that calculates the
Kullback-Leibler divergence between two normal distributions
- Inclusion option to suppress on-screen output in
sparsify
function
Bug fixes:
- Corrected small error in
optPenaltyCV
function
Adjustments:
- Both
optPenaltyCV
and optPenalty.aLOOCV
now utilize covML
instead of cov
- Default output option in
optPenaltyCV
(as in
optPenalty.aLOOCV
) is now light