Distributions

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

Problematic Distributions

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.

Discrete distributions

Continuous distributions