`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.

- 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.

- 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()`

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

- 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

`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`

.

- 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).

- 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.

- 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.

- 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.

- 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).

- 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.

- 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.

- A few examples in
`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)- 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

- 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