The implemented distributions are found in univariateML_models
.
library("univariateML")
univariateML_models
## [1] "beta" "betapr" "binom" "burr" "cauchy"
## [6] "dunif" "exp" "fatigue" "gamma" "ged"
## [11] "geom" "gompertz" "gumbel" "invburr" "invgamma"
## [16] "invgauss" "invweibull" "kumar" "laplace" "lgamma"
## [21] "lgser" "llogis" "lnorm" "logis" "logitnorm"
## [26] "lomax" "naka" "nbinom" "norm" "paralogis"
## [31] "pareto" "pois" "power" "rayleigh" "sged"
## [36] "snorm" "sstd" "std" "unif" "weibull"
## [41] "zip" "zipf"
This package follows a naming convention for the ml***
functions. To access the
documentation of the distribution associated with an ml***
function, write package::d***
.
For instance, to find the documentation for the log-gamma distribution write
?actuar::dlgamma
Additional information about the models can found in univariateML_metadata
.
univariateML_metadata[["mllgser"]]
## $model
## [1] "Logarithmic series"
##
## $density
## [1] "extraDistr::dlgser"
##
## $support
## Object of class Intervals
## 1 interval over Z:
## [1, Inf)
##
## $names
## [1] "theta"
##
## $default
## [1] 0.9
From the metadata you can read that
mllgser
estimates the parameters N
and s
.extraDistr::dlgser
.Some estimation procedures will fail under certain circumstances. Sometimes due to numerical problems, but also because the maximum likelihood estimator fails to exist. Here is a possibly non-exhaustive list of known problematic distributions.
extraDistr
.b
parameter tends towards 0, the Gompertz tends towards an exponential. A failing estimation indicates the exponential has a better fit.shape1*shape2
converges to a constant while shape2
tends to infinity.