In the previous tutorial we looked at multivariate distributions and how to use the d/p/q/r functions to evaluate multiple points of these. This tutorial concludes the ‘Basic’ set of tutorials with the use of ‘listing’ in distr6.
Currently distr6 includes 5 listing functions:
listDistributions(), listDecorators(), listWrappers(), listKernels(), listSpecialSets().
These are essentially ways of listing the R6 classes implemented in the
R6 API. We will only discuss the listDistributions()
function here as the others cover classes that we haven’t discussed yet,
but hopefully are intuitive to you and have a similar interface to
listDistributions().
listDistributions() is used to list every implemented
probability distribution in distr6. The default parameters return a
data.table of every implemented distribution including their traits and
the package that their d/p/q/r methods are implemented in. For example a
distribution with package stats means that the d/p/q/r
methods interface the R stats functions whereas a distribution with
package distr6 means the methods are written in distr6.
We’ve added the head() method just to save space.
head(listDistributions())
#> ShortName ClassName Type ValueSupport VariateForm Package Tags
#> <char> <char> <char> <char> <char> <char> <char>
#> 1: Arc Arcsine ℝ continuous univariate - limits
#> 2: Arrdist Arrdist ℝ^K discrete univariate -
#> 3: Bern Bernoulli ℕ0 discrete univariate stats
#> 4: Beta Beta ℝ+ continuous univariate stats
#> 5: BetaNC BetaNoncentral ℝ+ continuous univariate stats
#> 6: Binom Binomial ℕ0 discrete univariate stats limits
#> Alias
#> <char>
#> 1: AS
#> 2: AD
#> 3: B
#> 4: BT
#> 5: BTNC
#> 6: BM, BinTo see a simplified version of the list, add the
simplify = TRUE argument
head(listDistributions(simplify = TRUE))
#> [1] "Arcsine" "Arrdist" "Bernoulli" "Beta"
#> [5] "BetaNoncentral" "Binomial"Finally this list can be filtered to help you find a distribution with specific traits, for example for a multivariate distribution
head(listDistributions(filter = list(variateform = "multivariate")))
#> ShortName ClassName Type ValueSupport VariateForm Package
#> <char> <char> <char> <char> <char> <char>
#> 1: Diri Dirichlet [0,1]^K continuous multivariate extraDistr
#> 2: EmpMV EmpiricalMV ℝ discrete multivariate -
#> 3: Multinom Multinomial ℕ0^K discrete multivariate extraDistr
#> 4: MultiNorm MultivariateNormal ℝ^K continuous multivariate -
#> Tags Alias
#> <char> <char>
#> 1: DRC
#> 2: EMV
#> 3: limits MN
#> 4: locscale MVN
# The trait name is case-insensitive
head(listDistributions(filter = list(VaRiAtEfORm = "multivariate")))
#> ShortName ClassName Type ValueSupport VariateForm Package
#> <char> <char> <char> <char> <char> <char>
#> 1: Diri Dirichlet [0,1]^K continuous multivariate extraDistr
#> 2: EmpMV EmpiricalMV ℝ discrete multivariate -
#> 3: Multinom Multinomial ℕ0^K discrete multivariate extraDistr
#> 4: MultiNorm MultivariateNormal ℝ^K continuous multivariate -
#> Tags Alias
#> <char> <char>
#> 1: DRC
#> 2: EMV
#> 3: limits MN
#> 4: locscale MVNOr for multiple filters:
head(listDistributions(filter = list(variateform = "multivariate", package = "extraDistr")))
#> ShortName ClassName Type ValueSupport VariateForm Package Tags
#> <char> <char> <char> <char> <char> <char> <char>
#> 1: Diri Dirichlet [0,1]^K continuous multivariate extraDistr
#> 2: Multinom Multinomial ℕ0^K discrete multivariate extraDistr limits
#> Alias
#> <char>
#> 1: DRC
#> 2: MNIn this tutorial we looked at the listDistributions()
function so that you don’t have to remember which are implemented in
distr6. In this set of tutorials we covered the basics of constructing a
(univariate) distribution, getting and setting parameters, evaluating
statistical results and extending all this to multivariate
distributions. The next set of tutorials cover more advanced
functionality including decorators for numeric results, wrappers for
composite distributions, custom distributions, S3 functionality, and
symbolic sets.