NEWS.md

polywog package news

polywog 0.4-1 (April 2018)

• Dependency changes:

  • Occupational prestige data for the examples is now drawn from carData package (formerly car)

• Other minor changes to namespace calls to pass CRAN checks

polywog 0.4-0 (April 2014)

• predVals() has been rewritten to compute fitted values according to the "observed value" approach advocated in the following article:

  Hanmer, M. J. and Ozan Kalkan, K. (2013), Behind the Curve: Clarifying the Best Approach to Calculating Predicted Probabilities and Marginal Effects from Limited Dependent Variable Models. American Journal of Political Science, 57: 263--277. doi: 10.1111/j.1540-5907.2012.00602.x

  For more details, see ?predVals.

• k-fold cross-validation can now be performed in parallel for adaptive LASSO models, controlled via the .parallel argument of polywog(). Parallel computation of bootstrap iterations is now handled via the .parallel argument of bootPolywog() or control.bp().

• Adds model.matrix and model.frame methods for objects of class "polywog"

• Polynomial expansions of the design matrix are now handled in C++, and obsolete functions polym2() and rawpoly() have been removed

• New arguments of polywog():

  • lambda, nlambda, and lambda.min.ratio for finer control of the sequence of penalization factor values examined
  • foldid for direct specification of cross-validation folds (only available when fitting via the adaptive LASSO)
  • thresh and maxit for finer control of the convergence criterion, replacing old argument scad.maxit

• Dependency changes:

  • All dependencies imported instead of attached to the search path, except miscTools which must be attached to provide the margEff generic
  • glmnet 1.9-5 required (for parallel cross-validation)
  • ncvreg 2.4-0 required (for bug fix in cv.ncvreg)
  • iterators and Rcpp required
  • car no longer required (but still suggested)
  • matrixStats and games no longer required

polywog 0.3-0 (January 2013)

• polywog() now has argument unpenalized to exclude some terms from the adaptive LASSO penalty

• bootPolywog() now has argument maxtries to control failure when a non-collinear bootstrap model matrix cannot be found

• bootPolywog() now has argument min.prop to ensure a minimum amount of variation in the bootstrapped response variable in binary models

• The fitted.values element of "polywog" objects is now on the response scale instead of the link scale (i.e., transformed to probabilities when family = "binomial")

• Fixed bug where the polywog.fit element of cv.polywog() output would not contain fitted values

• Fixed bug that sometimes caused predVals() to fail unexpectedly

polywog 0.2-0 (June 2012)

• New function cv.polywog() to select both the polynomial degree and the penalization parameter by cross-validation

• New method margEff.polywog() to compute observation-wise and average marginal effects from a fitted model

• varNames element of a "polywog" object is now a character vector rather than a list (and is generated more safely)

• "polyTerms" attribute of matrix returned by polym2() is now a matrix rather than a data frame

• predict.polywog() now works correctly when newdata is a model frame

polywog 0.1-0 (May 2012)

• Initial release