# 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.

# 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
