SLOPE 0.5.1

Minor Changes

• Website updated to bootstrap 5-based pkgdown theme.
• Updated e-mail of maintainer.
• Dependencies on checkmate and mice were dropped.
• Update sparse matrix coercion to avoid deprecated functionality in the Matrix
  package.

SLOPE 0.5.0

Major changes

• plot.SLOPE(), plot.trainSLOPE() and plotDiagnostics() have been
  reimplemented in ggplot2.

Deprecated Functions

• caretSLOPE() has been deprecated and will be made defunct in version
  0.6.0.

SLOPE 0.4.1

Bug Fixes

• The C++ standard library memory was added to a source file to fix
  compilation errors on some systems.

SLOPE 0.4.0

New Functions

• 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.

Major changes

• The parametrization for OSCAR models have been corrected and changed. As a
  result, 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.

Minor Changes

• The q parameter is no longer allowed to be smaller than 1e-6 to avoid
  constructions of regularization paths with infinite lambda values.
• The lambda argument in SLOPE() now also allowed the input "lasso" to
  obtain the standard lasso.
• The performance of trainSLOPE()

Vignettes

• A new vignette has been added to compare algorithms for the proximal
  operator.

Bug Fixes

• For very small numbers of observations (10 or so), the regularization weights
  for lambda = "gaussian" were incorrectly computed, increasing and then
  decreasing. This is now fixed and regularization weights in this case are now
  always non-increasing.
• Misclassification error was previously computed incorrectly in trainSLOPE()
  for multinomial models (thanks @jakubkala and @KrystynaGrzesiak)
• Performance of trainSLOPE() was previously hampered by erroneous
  refitting of the models, which has been fixed now (thanks @jakubkala and
  @KrystynaGrzesiak)

Deprecated and Defunct

• yvar argument in plotDiagnostics() that was previously deprecated is
  now defunct.
• Using missclass for the measure argument in trainSLOPE() has been
  deprecated in favor of misclass.

SLOPE 0.3.3

Bug fixes

• Fixed first coefficient missing from plot if no intercept was used in
  the call to SLOPE().
• Fixed incorrect results when intercept = FALSE and family = "gaussian"
  (#13, thanks, Patrick Tardivel).

SLOPE 0.3.2

Minor changes

• Added 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.

Bug fixes

• Fixed premature stopping of the solver for the first step of the
  regularization path (the null model).
• Actually fix UBSAN/ASAN sanitizer warnings by modifying code for
  FISTA solver.

SLOPE 0.3.1

Bug fixes

• Fixed package build breaking on solaris because of missing STL namespace
  specifier for std::sqrt() in src/SLOPE.cpp.
• Fixed erroneous scaling of absolute tolerance in stopping criteria for
  the ADMM solvers. Thanks, @straw-boy.
• Fixed sanitizer warning from CRAN checks.

SLOPE 0.3.0

Major changes

• Scaling of 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".
• The sigma argument is deprecated in favor of alpha in SLOPE(),
  coef.SLOPE(), and predict.SLOPE().
• The n_sigma argument is deprecated in favor of path_length in SLOPE()
• The lambda_min_ratio argument is deprecated in favor of alpha_min_ratio in
  SLOPE()
• The default for argument lambda in SLOPE() has changed from "gaussian"
  to "bh".
• Functions and arguments deprecated in 0.2.0 are now defunct and have
  been removed from the package.
• 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).

Minor changes

• Default 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.
• A warning is now thrown if the maximum number of passes was reached
  anywhere along the path (and prints where as well).
• If the 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.

Bug fixes

• Plotting models that are completely sparse no longer throws an error.
• rho instead of 1 is now used in the factorization part for
  the ADMM solver.

SLOPE 0.2.1

Minor changes

• A few examples in deviance() and SLOPE() that were taking
  too long to execute have been removed or modified.