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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).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).d
/p
/q
/r
functions for hnbinom
, zinbinom
,
ztnbinom
, and ztpois
similar to the
corresponding nbinom
and pois
functions from
base R.HurdleNegativeBinomial()
,
ZINegativeBinomial()
, ZTNegativeBinomial()
,
and ZTPoisson()
distribution constructors along with the
corresponding S3 methods for the “usual” generics (except
skewness()
and kurtosis()
).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.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).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.support()
method for GEV-based distributions
(GEV()
, GP()
, Gumbel()
,
Frechet()
). Added a random()
method for the
Tukey()
distribution (using the inversion method).apply_dpqr()
helps to apply the standard
d
/p
/q
/r
functions
available in base R and many packages. The accompanying manual page
provides some worked examples and further guidance.distributions3
to go from basic probability theory to probabilistic regression models.
Illustrated with Poisson GLMs for the number of goals per team in the
2018 FIFA World Cup explained by the teams’ ability differences.
(#74)prodist()
to extract fitted
(in-sample) or predicted (out-of-sample) probability distributions from
model objects like lm
, glm
, or
arima
. (#83)distributions3
for CRANNEWS.md
file to track changes to the
package.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.