Nothing
plot.SLOPE()
, plot.trainSLOPE()
and plotDiagnostics()
have been
reimplemented in ggplot2.caretSLOPE()
has been deprecated and will be made defunct in version
0.6.0.sortedL1Prox()
is a new function that computes the proximal operator for the
sorted L1 norm (the penalty term in SLOPE).regularizationWeights()
is a new function that returns the penalty weights
(lambda sequence) for SLOPE or OSCAR.SLOPE()
gains two arguments: theta1
and theta2
to control the
behavior using the parametrization from L. W. Zhong and J. T. Kwok, “Efficient
sparse modeling with automatic feature grouping,” IEEE Transactions on Neural
Networks and Learning Systems, vol. 23, no. 9, pp. 1436–1447, Sep. 2012, doi:
10.1109/TNNLS.2012.2200262. q
is no longer used with OSCAR models. Thanks,
Nuno Eusebio.SLOPE()
has gained a new argument, prox_method
, which allows the user to
select prox algorithm to use. There is no an additional algorithm in the
package, based on the PAVA algorithm used in isotonic regression, that
can be used. Note that this addition is mostly of academic interest and
does not need to be changed by the user.q
parameter is no longer allowed to be smaller than 1e-6
to avoid
constructions of regularization paths with infinite lambda
values.lambda
argument in SLOPE()
now also allowed the input "lasso"
to
obtain the standard lasso.trainSLOPE()
lambda = "gaussian"
were incorrectly computed, increasing and then
decreasing. This is now fixed and regularization weights in this case are now
always non-increasing.trainSLOPE()
for multinomial models (thanks @jakubkala and @KrystynaGrzesiak)trainSLOPE()
was previously hampered by erroneous
refitting of the models, which has been fixed now (thanks @jakubkala and
@KrystynaGrzesiak)yvar
argument in plotDiagnostics()
that was previously deprecated is
now defunct.missclass
for the measure
argument in trainSLOPE()
has been
deprecated in favor of misclass
.SLOPE()
.intercept = FALSE
and family = "gaussian"
(#13, thanks, Patrick Tardivel).tol_rel_coef_change
argument to SLOPE()
as a convergence
criterion for the FISTA solver that sets a tolerance for the relative
change in coefficients across iterations.std::sqrt()
in src/SLOPE.cpp
.alpha
(previously sigma
) is now invariant to the
number of observations, which is achieved by scaling
the penalty part of the objective by the square root of the number of
observations if scale = "l2"
and the number of observations if
scale = "sd"
or "none"
. No scaling is applied when scale = "l1"
.sigma
argument is deprecated in favor of alpha
in SLOPE()
,
coef.SLOPE()
, and predict.SLOPE()
.n_sigma
argument is deprecated in favor of path_length
in SLOPE()
lambda_min_ratio
argument is deprecated in favor of alpha_min_ratio
in
SLOPE()
lambda
in SLOPE()
has changed from "gaussian"
to "bh"
.scale = "sd"
now scales with the population standard deviation rather
than the sample standard deviation, i.e. the scaling factor now used
is the number of observations (and not the number of observations minus one
as before).path_length
has changed from 100 to 20.plot.SLOPE()
has gained an argument x_variable
that controls what is
plotted on the x axis.max_variables
criterion is hit, the solution path returned
will now include also the last solution (which was not the case
before). Thanks, @straw-boy.rho
instead of 1
is now used in the factorization part for
the ADMM solver.deviance()
and SLOPE()
that were taking
too long to execute have been removed or modified.This version of SLOPE represents a major change to the package. We have merged functionality from the owl package into this package, which means there are several changes to the API, including deprecated functions.
SLOPE_solver()
, SLOPE_solver_matlab()
, prox_sorted_L1()
,
and create_lambda()
have been deprecated (and will be defunct in the
next version of SLOPE)X
, fdr
, and normalize
have been deprecated
in SLOPE()
and replaced by x
, q
, scale
and center
, respectively"default"
and "matlab"
to argument
solver
in SLOPE()
have been deprecated and replaced with "fista"
and "admm"
, which uses the accelerated proximal gradient method
FISTA and alternating direction of multipliers method (ADMM)
respectivelyfamily = "gaussian"
family
argument in SLOPE()
)lambda
is now scaled (divided by) the number of observations (rows)
in x
screen = TRUE
in SLOPE()
. The type of algorithm can also
be set via screen_alg
.SLOPE()
now returns an object of class "SLOPE"
(and an additional
class depending on input to family
in SLOPE()
SLOPE
objects gain coef()
and plot()
methods.SLOPE
now uses screening rules to speed up model fitting in the
high-dimensional regimetrainSLOPE()
trains SLOPE with repeated k-folds
cross-validationcaretSLOPE()
enables model-tuning using the
caret packageSLOPE()
now fits an entire path of regularization sequences by defaultnormalize
option to SLOPE()
has been replaced by scale
and
center
, which allows granular options for standardizationdeviance()
returns the deviance from the fitscore()
can be used to assess model performance against
new dataplotDiagnostics()
has been included to visualize
data from the solver (if diagnostics = TRUE
in the call to SLOPE()
)lambda = "oscar" in the call to
SLOPE()`Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.