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SLOPE (development version)

SLOPE 0.4.2

New Functions

  • ABSLOPE() is a new function that computes the Adaptive Bayesian SLOPE according to Jiang, W., Bogdan, M., Josse, J., Majewski, S., Miasojedow, B., Ročková, V., & TraumaBase® Group. (2021). Adaptive Bayesian SLOPE: Model Selection with Incomplete Data. Journal of Computational and Graphical Statistics, 1-25, doi:10.1080/10618600.2021.1963263.

Major changes

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

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.

SLOPE 0.2.0

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.

Major changes

  • SLOPE_solver(), SLOPE_solver_matlab(), prox_sorted_L1(), and create_lambda() have been deprecated (and will be defunct in the next version of SLOPE)
  • arguments X, fdr, and normalize have been deprecated in SLOPE() and replaced by x, q, scale and center, respectively
  • options "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) respectively
  • ADMM has been implemented as a solver for family = "gaussian"
  • binomial, poisson, and multinomial families are now supported (using family argument in SLOPE())
  • input to lambda is now scaled (divided by) the number of observations (rows) in x
  • predictor screening rules have been implemented and are activated by calling 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 regime
  • most of the code is now written in C++ using the Rcpp and RcppArmadillo packages
  • a new function trainSLOPE() trains SLOPE with repeated k-folds cross-validation
  • a new function caretSLOPE() enables model-tuning using the caret package
  • SLOPE() now fits an entire path of regularization sequences by default
  • the normalize option to SLOPE() has been replaced by scale and center, which allows granular options for standardization
  • sparse matrices (from the Matrix package) can now be used as input
  • there are now five datasets included in the package
  • the introductory vignette has been replaced

Minor changes

  • a new function deviance() returns the deviance from the fit
  • a new function score() can be used to assess model performance against new data
  • a new function plotDiagnostics() has been included to visualize data from the solver (if diagnostics = TRUE in the call to SLOPE())
  • OSCAR-type penalty sequences can be used by setting lambda = "oscar" in the call to SLOPE()`
  • the test suite for the package has been greatly extended