Changes in version 0.3.0                        

  - mlxs_glm() now moves to float64 on the cpu where necessary to
    compute more accurate estimates.
  - New mlxs_glm_control() function.
  - mlxs_lm() and mlxs_glm() now reject rank-deficient x. A bug which
    meant we calculated qr(x) twice has now been fixed.
  - mlxs_lm(), mlxs_lm_fit(), and mlxs_glm_control() now expose rank_tol
    to tune rank-deficiency detection. Set rank_tol = FALSE to skip rank
    checks entirely.
  - Bugfix: mlxs_lm() now drops unused factor levels.
  - New bread(), estfun() and hatvalues() methods for mlxs_lm to allow
    for sandwich-style robust standard errors.
  - More mlxs_lm methods now return base R objects by default,
    controllable via the output argument.
  - confint.mlxs_lm() and confint.mlxs_glm() can now return bootstrap
    confidence intervals. So can the respective summary() methods.
  - Speedups for some augment(), predict() and summary() methods.

                        Changes in version 0.2.0                        

  - Added mlxs_prcomp(), a prcomp()-style PCA interface with exact and
    randomized truncated MLX-backed decomposition paths. Benchmarks show
    this greatly outperforms base R prcomp() and other specialised
    packages for fast PCA.
  - Reworked mlxs_glmnet(). It can now outperform glmnet::glmnet() for
    large problems (roughly n x p > 5,000,000).
  - Added mlxs_cv_glmnet() as a cross-validation wrapper for the
    MLX-backed elastic-net path fits, analogous to glmnet::cv.glmnet().
  - Export mlxs_lm_fit() so advanced users can call the MLX-backed QR
    solver directly.

                        Changes in version 0.1.0                        

  - Initial version.