# 3.9-1 (2017-04-26)
  * Change: Poission now returns linear predictors, like other families
  * Internal: Changing PROTECT/UNPROTECT to conform to new coding standards

# 3.9-0 (2017-03-16)
  * Deprecated: fir() is now called mfdr()
  * Change: mfdr for Cox and logistic models no longer use the simplistic
    approximation of 3.7-0.  These calculations are much more accurate, but
    more computationally, so these are carried out in C now.
  * Change: mfdr for Cox and logistic models requires the model matrix X now.
  * Internal: Registration of native routines
  * Fixed: std() wasn't matching up column names if one column got dropped

# 3.8-0 (2017-01-06)
  * Change: max.iter now based on total number of iterations for entire path
  * Fixed: Bug when fitting Cox model for single lambda
  * Fixed: std no longer drops dimnames

# 3.7-1 (2016-12-23)
  * Fixed: Various fixes for fir function
  * Fixed: Bug with high dimensional (p > n) Cox models

# 3.7-0 (2016-12-13)
  * New: fir extended to Cox and logistic regression
  * New: summary function for ncvreg and ncvsurv objects
  * Change: Convergence criterion now based on RMSD of linear predictors
  * Change: Additional options and improvements to plot.fir
  * Change: Better display of fir objects
  * Internal: Improved efficiency for Cox models (linear predictor calculation
    now occurs in C, not R)
  * Internal: Reorganized testing suite
  * Fixed: lamNames with single lambda passed
  * Fixed: loss wasn't being returned for gaussian if failure to converge
  * Fixed: perm.ncvreg would return NAs when models were saturated

# 3.6-0 (2016-06-13)
  * New: Exports std() function for standardizing a design matrix
  * Fixed: In predict.cv.ncvsurv
  * Documentation: Added 'quick start' vignette
  * Internal: Improved efficiency for cox models (avoids recalculating linear
    predictors)
  * Internal: Reorganized testing suite
  * Internal: 'survival' package now used for setupLambda in Cox models

# 3.5-2 (2016-04-09)
  * New: Added user interrupt checking
  * Fixed: In ncvsurv with integer penalty factors
  * Fixed: Rare numerical accuracy bug in cv fold assignments
  * Fixed: LOOCV bug introduced by bias-correction feature

# 3.5-1 (2016-02-07)
  * New: Compute bias correction for CV error; this is an experimental
    feature at this point and may change in the future
  * Internal: Replaced AUC function with more efficient version using
    survival package
  * Fixed: Penalty.factor for cv.ncvsurv when some columns may be degenerate

# 3.5-0 (2015-10-27)
  * New: Added function AUC() to calculate cross-validated AUC values
    for ncvsurv models.
  * New: Option to return fitted values from cross-validation folds
    (returnY=TRUE) for cv.ncvreg and cv.ncvsurv.
  * Change: New method for calculation of cross-validation loss in cv.ncvsurv.
  * Change: More accurate calculation for convexMin in the presence of
    unpenalized variables
  * Fixed: Factor-valued y with CV logistic regression
  * Internal: Substantial efficiency improvements throughout for Cox models.
    Coordinate descent redesigned to work in O(n) instead of O(n^2) operations,
    and R code redesigned at various points to avoid the creation of any n x n
    matrices when fitting and cross-validating Cox regression models.
  * Internal: Better double/int type checking for penalty.factor
  * Internal: Modifications to NAMESPACE for compatibility with R 3.3.

# 3.4-0 (2015-05-04)
  * New: Expanded predict function for Cox models.  predict.ncvsurv now
    estimates subject-specific survival functions and medians.
  * New: Plot method for survival curves.
  * New: Option in perm.ncvreg to permute residuals for linear
    regression
  * New: permres function to estimate false inclusion rates based on
    residuals at a specific value of lambda
  * New: Some support for factors in X, y.  It is still recommended that
    users convert X to a numeric matrix prior to fitting in order to ensure that
    predict() methods work properly, but ncvreg will now allow you to pass a
    data frame with factors and handle things appropriately.
  * Fixed: In predict.ncvsurv, when applied to models with saturation issues.
  * Fixed: Small memory leak in ncvsurv.

# 3.3-0 (2015-03-18)
  * New: Support for fitting survival models added (ncvsurv), along
    with predict, plot, and cv.ncvsurv support functions.  Currently, Cox models
    are the only type of survival model implemented.
  * New: Parallelization support for cv.ncvreg (with help from Grant
    Brown)
  * Fixed: In cv.ncvreg, when attempting to use leave-one-out cross-validation
    (thank you to Cajo ter Braak for pointing this out)
  * Removed: ncvreg_fit; it may return in a future version of the package.

# 3.2-0 (2014-07-12)
  * New: Automatically coerces X to matrix and y to numeric if possible
  * New: Made ncvreg_fit more user-friendly: user no longer has to specify
    lambda, works with coef, predict, plot, etc.
  * Changed: Modified order of arguments for predict so that 'type' comes
    before 'lambda' and 'which'
  * Fixed: Bug in convexMin when used with penalty.factor option
  * Internal: Updated algorithm to 'hybrid' strong/active cycling

# 3.1-0 (2014-02-25)
  * New: Added support for Poisson regression
  * Fixed: Bug in ncvreg_fit that could arise when fitting a model without an
    intercept
  * Fixed: Bug in cv.ncvreg with univariate regression (thank you to Diego
    Franco Saldana for pointing this out)

# 3.0-0 (2014-02-06)
  * New: Added fir, perm.ncvreg, and plot.fir functions for the purposes of
    estimating and displaying false inclusion rates; these are likely to evolve
    over the next few months
  * Fixed: Bug in cv.ncvreg for user-specified lambda sequence
  * Internal: Revised algorithms to incorporate targeted cycling based on strong
    rules
  * Internal: Moved standardization to C
  * Internal: Moved calculation of lambda sequence to C
  * Internal: As a result of the above three changes, ncvreg now runs much
    faster for large p

# 2.7-0 (2013-12-16)
  * New: "vars" and "nvars" options to predict function.
  * Changed: Modified look of summary(cvfit) output.
  * Internal: Modified details of .Call interface.

# 2.6-0 (2013-10-03)
  * New: Introduction of function ncvreg_fit for programmers who want to access
    the internal C routines of ncvreg, bypassing internal standardization and
    processing
  * New: Added vertical.line and col options to plot.cv.ncvreg
  * Fixed: Bug in axis annotations with plot.cv.ncvreg when model is saturated
  * Fixed: Deviance calculation; would return NaN if fitted probabilities of 0
    or 1 occurred for binomial outcomes
  * Fixed: NAMESPACE for coef.cv.ncvreg and predict.cv.ncvreg
  * Internal: .Call now used instead of .C

# 2.5-0 (2013-03-16)
  * New: Options in plot.cv.ncvreg to plot estimates of r-squared,
    signal-to-noise ratio, scale parameter, and prediction error in addition to
    cross-validation error (deviance)
  * New: Summary method for cv.ncvreg which displays the above information at
    lambda.min, the value of lambda minimizing the cross-validation error
  * Fixed: Bug in cv.ncvreg with user-defined lambda values.

# 2.4-0 (2012-10-10)
  * New: penalty.factor option
  * New: coef and predict methods now accept lambda as argument
  * New: logLik method (which in turn allows AIC/BIC)
  * Changed: cv.grpreg now returns full data fit as well as CV errors
  * Fixed: Error in definition/calculation of cross-validation error and
    standard error
  * Fixed: Bug that arose if lambda was scalar (instead of a vector)
  * Fixed: Bug in cv.ncvreg for linear regression -- cross-validation was being
    carried out deterministically (Thank you to Brenton Kenkel for pointing this
    out)
  * Fixed: Intercept for logistic regression was not being calculated for
    lamda=0
  * Internal: standardization more efficient
  * Internal: cdfit_ now returns loss (RSS for gaussian, deviance for binomial)

# 2.3-2 (2011-05-16)
  * Documentation: Fixed formatting error in citation.
  
# 2.3-1 (2011-05-11)
  * Changed: plot.ncvreg: Made the passing of arguments for plot.ncvreg more
    flexible, so that user can pass options concerning both the plot and the
    lines
  * Changed: plot.ncvreg: Changed some of the default settings with respect to
    color (hcl instead of hsv) and line width

# 2.3 (2011-05-06)
  * Documentation: Updated documentation for cv.ncvreg.Rd, which no longer
    agreed with the function usage (this was an oversight in the release of
    version 2.2)

# 2.2 (2011-04-25)
  * New: plot.cv.ncvreg for plotting cv.ncvreg objects
  * Changed: Divorced cross-validation from fitting in cv.ncvreg.  From a user
    perspective, this increases flexibility, although obtaining the model with
    CV-chosen regularization parameter now requires two calls (to ncvreg and
    cv.ncvreg).  The functions, however, are logically separate and involve
    entirely separate methods.
