distributions3 0.2.1

• New generics is_discrete() and is_continous() with methods for all distribution objects
  in the package. The is_discrete() methods return TRUE for every distribution that is discrete
  on the entire support and FALSE otherwise. Analogously, is_continuous() returns TRUE for
  every distribution that is continuous on the entire support and FALSE otherwise. Thus, for
  mixed discrete-continuous distributions both methods should yield FALSE (#90).
• New logical argument elementwise = NULL in apply_dpqr() and hence inherited in
  cdf(), pdf(), log_pdf(), and quantile(). It provides type-safety when
  applying one of the functions to a vector of distributions d to a numeric
  argument x where both d and x are of length n > 1. By setting elementwise = TRUE
  the function is applied element-by-element, also yielding a vector of length n.
  By setting elementwise = FALSE the function is applied for all combinations
  yielding an n-by-n matrix. The default elementwise = NULL corresponds to FALSE
  if d and x are of different lengths and TRUE if the are of the same length
  n > 1 (#87).
• Extended support for various count data distributions, now enompassing both the Poisson
  and negative binomial distributions along with various adjustments for zero counts
  (hurdle, inflation, and truncation, respectively). More details are provided in the
  following items (#86).
• New d/p/q/r functions for hnbinom, zinbinom, ztnbinom, and ztpois similar
  to the corresponding nbinom and pois functions from base R.
• New HurdleNegativeBinomial(), ZINegativeBinomial(), ZTNegativeBinomial(), and
  ZTPoisson() distribution constructors along with the corresponding S3 methods for the
  "usual" generics (except skewness() and kurtosis()).
• New prodist() methods for extracting the fitted/predicted probability distributions from
  models estimated by hurdle(), zeroinfl(), and zerotrunc() objects from either the
  pscl package or the countreg package.
• Added argument prodist(..., sigma = "ML") to the lm method for extracting the
  fitted/predicted probability distribution from a linear regression model. In the previous
  version the prodist() method always used the least-squares estimate of the error variance
  (= residual sum of squares divided by the residual degrees of freedom, n - k), as also
  reported by the summary() method. Now the default is to use the maximum-likelihood estimate
  instead (divided by the number of observations, n) which is consistent with the logLik()
  method. The previous behavior can be obtained by specifying sigma = "OLS" (#91).
• Similarly to the lm method the glm method prodist(..., dispersion = NULL) now, by
  default, uses the dispersion estimate that matches the logLik() output. This is based
  on the deviance divided by the number of observations, n. Alternatively,
  dispersion = "Chisquared" uses the estimate employed in the summary() method,
  based on the Chi-squared statistic divided by the residual degrees of freedom, n - k.
• Small improvements in methods for various distribution objects: Added support() method
  for GEV-based distributions (GEV(), GP(), Gumbel(), Frechet()). Added a
  random() method for the Tukey() distribution (using the inversion method).