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Changelog

Source: NEWS.md

SLOPE 1.0.1

Bug Fixes

  • Fixed a test error on the m1-san platform.

SLOPE 1.0.0

CRAN release: 2025-06-30

This update of SLOPE brings an entirely different C++ implementation of the underlying package based on the C++ library libslope. It comes with several large and breaking changes with respect to the previous version of the package.

We realized that this may throw off some users, and hope that you will be patient with dealing with the large number of breaking changes.

Breaking Changes

  • The caretSLOPE() function that was deprecated has now been removed from the package.
  • Fields unique, violations, and active_sets are no longer stored in the SLOPE object. These fields were typically only used for debugging purposes.
  • The prox_method and method arguments in SLOPE() and sortedL1Prox(), respectively, have been removed. The proximal operator is now always computed using the fast stack-based algorithm. There was never any reason to use the slower PAVA algorithm.
  • The ADMM solver has been removed from the package. Calling SLOPE() with solver = "admm" will now throws a warning and the value will be automatically set to "auto".
  • alpha is now scaled by n (the number of observations) and differences with respect to the type of scaling are no longer taken into account.
  • The object coefficients from SLOPE() is now a list of sparse matrices (rather than a three-dimensional array as before). Now it contains only the coefficients and not the intercepts. The intercepts are instead stored in intercepts in the returned object and are always present even if intercept = FALSE.
  • The behavior of coef.SLOPE() has changed somewhat, and if simplify = FALSE, then the returned object is now instead a list of sparse matrices (rather than a three-dimensional array as before).
  • The default value of q in SLOPE() has changed from 0.1 * min(1, NROW(x) / NCOL(x)) to 0.1.
  • Arguments sigma, n_sigma, and lambda_min_ratio in SLOPE() that were previously deprecated have been removed.
  • SLOPE() now internally solves the problem normalized by scaling with the number of observations, which means that values returned in deviance and prmals and duals if diagnostics = TRUE are now scaled by n.
  • path_length in SLOPE() now defaults to 100 (previously 20).
  • tol_dev_ratio in SLOPE() now defaults to 0.999 (previously 0.995).
  • Plots from plot.SLOPE() now use base R graphics rather than ggplot2. This means that the plots are more difficult to customize but plot much more faster when there are many variables and significantly reduces the dependency load of the package. For plots of trained SLOPE objects, which used to be faceted on the q parameter, the user now needs to use the standard base R graphics API to facet plots via par(mfrow = c(1, 2)) or similar.

Deprecated Functionality

  • Arguments tol_rel_gap, tol_infeas, tol_abs, tol_rel, tol_rel_coef in SLOPE() are now deprecated. The solvers now all rely on the same tolerance criterion, which is set by tol and uses the duality gap normalized by the current primal value.
  • Arguments screen and screen_alg are now deprecated and have no effect. Feature screening is always used. These arguments were only used for debugging.
  • The argument verbosity in SLOPE() is now defunct and has no effect.
  • The argument prox_method in SLOPE() and sortedL1Prox() is now defunct and has no effect.

New Features

  • Centering x in SLOPE() is now allowed again, even when the matrix is sparse.
  • Out-of-memory matrices are now allowed through the bigmemory package. Only support for dense matrices is available at the moment.
  • Centers and scales can now be specified manually by providing vectors to center and scale in SLOPE().
  • A new solver based on a hybrid method of proximal gradient descent and coordinate descent is available and used by default by the Gaussian and binomial families. Use it by specifying solver = "hybrid".
  • Solver can now be set to "auto", in which case the package automatically chooses a solver.
  • The returned duality gaps when diagnostics = TRUE are now true duality gaps, computed by guaranteeing that the dual variable is feasible (which was not the case previously).
  • scale in SLOPE() gains a new option "max_abs" which scales the columns of x by their maximum absolute value.
  • When alpha = "estimate", there is a now an iteration limit in case the algorithm does not converge to one set of features. Thanks @RomanParzer.
  • plot.SLOPE() gains a new argument magnitudes, which causes the plot to only show the magnitudes of the coefficients (which helps if you want to visualize cluster structure).
  • plot.SLOPE() gains a new argument add_labels, which add numbers for the coefficients to the plot. Set to FALSE by default.
  • Relaxed SLOPE models can now be fit by specifying gamma in SLOPE().
  • plot.trainedSLOPE() gains a new argument index, to select which of the hyperparameter combinations to plot for.
  • There’s a new function plotClusters(), which allows plotting the cluster structure in SLOPE. Thanks, 1!
  • SLOPE() gains a new argument cd_type, to control the type of coordinate descent used for the hybrid solver, with options "cyclical" and "permuted".

Bug Fixes

  • Return correct model when training for AUC in trainSLOPE().

Performance Improvements

The new hybrid algorithm that’s implemented in libslope and now used in the package constitutes a major upgrade in terms of performance.

  • The solver is now much more memory-efficient and can avoid copies of the design matrix entirely by normalizing the columns just-in-time. This is the standard behavior. Future versions of the package will allow the user to specify whether to copy (and modify) the design matrix or not.

Dependencies

We have made an effort to reduce the footprint of the package and reduce the number of dependencies.

  • The package now relies on Eigen (through RcppEigen) rather than Armadillo, which means that there is no longer any reliance on BLAS and LAPACK libraries.
  • The dependency on ggplot2 is removed.
  • The vdiffr, tidyr, dplyr, bench, scales, and glmnet packages in the Suggests field that were used for testing are now removed.

SLOPE 0.5.2

CRAN release: 2025-02-01

Bug Fixes

  • Fixed bug when computing regularization weights for type "gaussian" when the number of observations is less than the number of variables.

SLOPE 0.5.1

CRAN release: 2024-07-09

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

CRAN release: 2022-06-09

Major changes

Deprecated Functions

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

SLOPE 0.4.1

CRAN release: 2022-03-14

Bug Fixes

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

SLOPE 0.4.0

CRAN release: 2021-12-10

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 1)
  • Performance of trainSLOPE() was previously hampered by erroneous refitting of the models, which has been fixed now (thanks @jakubkala and

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

CRAN release: 2021-03-17

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

CRAN release: 2020-07-10

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, 2.
  • Fixed sanitizer warning from CRAN checks.

SLOPE 0.3.0

CRAN release: 2020-07-02

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,

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

CRAN release: 2020-04-16

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 toSLOPE()`
  • the test suite for the package has been greatly extended