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R/alpha2theta.R
In GenHMM1d: Goodness-of-Fit for Univariate Hidden Markov Models
Defines functions
alpha2theta
Documented in
alpha2theta
#'@title Transforms unconstrained parameters to constrainted parameters
#'
#'@description This function computes the constrained parameters theta of a univariate distribution from the unconstrainted parameter alpha.
#'
#'@param family distribution name; run the command distributions() for help
#'@param param unconstrained parameters of the univariate distribution
#'
#'@return \item{theta}{constrained parameters}
#'
#'
#'@export
#'@keywords internal
alpha2theta<-function(family,param){
switch(family,
"asymexppower" = { ## [R+, R+, 01]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
theta[3] = 1/(1+exp(-param[3]))
} ,
"asymlaplace" = { ## [R, R+, R+]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2:3] = exp(param[2:3])
} ,
"asympower" = { ## [01, R+, R+]
theta = matrix(0,1,3)
theta[1] = 1/(1+exp(-param[1]))
theta[2:3] = exp(param[2:3])
} ,
"asymt" = { ## [R+, R+, 01, R]
theta = matrix(0,1,4)
theta[1:2] = exp(param[1:2])
theta[3] = 1/(1+exp(-param[3]))
theta[4] = param[4]
} ,
"beard" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"benini" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"benford" = { ## [1 ou 2]
theta = matrix(0,1,1)
if (param[1]>2){
theta[1] = 2
} else if (param[1]<1){
theta[1] = 1
} else {
theta[1] = round(param[1])
}
} ,
"bernoulli" = { ## [01]
theta = matrix(0,1,1)
theta[1] = 1/(1+exp(-param[1]))
} ,
"beta" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"betabinomial" = { ## [N+, R+, R+]
theta = matrix(0,1,2)
#theta[1] = round(param[1])
theta[1:2] = exp(param[1:2])
} ,
"betageometric" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"betanegativebinomial" = { ## [N+, R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"betaburr" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"betaburr7" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"betaexponential" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"betafrechet" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"betagompertz" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"betagumbel" = { ## [R+, R+, R, R+]
theta = matrix(0,1,4)
theta[1:2] = exp(param[1:2])
theta[3] = param[3]
theta[4] = exp(param[4])
} ,
"betagumbel2" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"betalognormal" = { ## [R+, R+, R, R+]
theta = matrix(0,1,4)
theta[1:2] = exp(param[1:2])
theta[3] = param[3]
theta[4] = exp(param[4])
} ,
"betalomax" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"betanormal" = { ## [R+, R+, R, R+]
theta = matrix(0,1,4)
theta[1:2] = exp(param[1:2])
theta[3] = param[3]
theta[4] = exp(param[4])
} ,
"betaprime" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"betaweibull" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"bhattacharjee" = { ## [R, R+, R+]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2:3] = exp(param[2:3])
} ,
"binomial" = { ## [N+, 01]
theta = matrix(0,1,1)
theta[1] = 1 / (1+exp(-param[1]))
} ,
"birnbaumsaunders" = { ## [R+, R+, R]
theta = matrix(0,1,3)
theta[1:2] = exp(param[1:2])
theta[3] = param[3]
} ,
"boxcox" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"burr" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"burr2param" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"cauchy" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"chen" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"chi" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"chisquared" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"clg" = { ## [R+, R+, R]
theta = matrix(0,1,3)
theta[1:2] = exp(param[1:2])
theta[3] = param[3]
} ,
"complementarybeta" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"dagum" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"diffzeta" = { ## [R+, >1]
theta = matrix(0,1,2)
theta[1] = exp(param[1])
theta[2] = 1+exp(-param[2])
} ,
"discretegamma" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"discretelaplace" = { ## [R, 01]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = 1/(1+exp(-param[2]))
} ,
"discretenormal" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"discreteweibull" = { ## [01, R+]
theta = matrix(0,1,2)
theta[1] = 1/(1+exp(-param[1]))
theta[2] = exp(param[2])
} ,
"doubleweibull" = { ## [R+, R, R+]
theta = matrix(0,1,3)
theta[1] = exp(param[1])
theta[2] = param[2]
theta[3] = exp(param[3])
} ,
"ev" = {
## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"exponential" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"exponentialextension" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"exponentialgeometric" = { ## [R+, 01]
theta = matrix(0,1,2)
theta[1] = exp(param[1])
theta[2] = 1/(1+exp(-param[2]))
} ,
"exponentiallogarithmic" = { ## [R+, 01]
theta = matrix(0,1,2)
theta[1] = exp(param[1])
theta[2] = 1/(1+exp(-param[2]))
} ,
"exponentialpoisson" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"exponentialpower" = { ## [R, R+, R+]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2:3] = exp(param[2:3])
} ,
"exponentiatedexponential" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"exponentiatedlogistic" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"exponentiatedweibull" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"F" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"fellerpareto" = { ## [R(mini), R+, R+, R+, R+]
theta = matrix(0,1,5)
theta[1] = param[1]
theta[2:5] = exp(param[2:5])
} ,
"fisk" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"foldednormal" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"frechet" = { ## [R+, R, R+]
theta = matrix(0,1,3)
theta[1] = exp(param[1])
theta[2] = param[2]
theta[3] = exp(param[3])
} ,
"gamma" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"gammapoisson" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"gaussian" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"gev" = { ## [R, R+, R]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = param[3]
} ,
"geninvbeta" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"genlogis" = { ## [R+, R, R+]
theta = matrix(0,1,3)
theta[1] = exp(param[1])
theta[2] = param[2]
theta[3] = exp(param[3])
} ,
"genlogis3" = { ## [R+, R, R+]
theta = matrix(0,1,3)
theta[1] = exp(param[1])
theta[2] = param[2]
theta[3] = exp(param[3])
} ,
"genlogis4" = { ## [R+, R+, R, R+]
theta = matrix(0,1,4)
theta[1:2] = exp(param[1:2])
theta[3] = param[3]
theta[4] = exp(param[4])
} ,
"genpowerweibull" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"geometric" = { ## [01]
theta = matrix(0,1,1)
theta[1] = 1/(1+exp(-param[1]))
} ,
"generalizedhyperbolic" = { ## [R, R+, R+, R, R] [mu, delta, alpha, beta, lambda] (avec alpha^2 > beta^2)
theta = matrix(0,1,5)
theta[1] = param[1]
theta[2] = max( c( 10e-12, exp(param[2]) ) )
theta[3] = max( c( 10e-12, exp(param[3]) ) )
theta[4] = theta[3] * ( exp(2*param[4])-1 ) / ( exp(2*param[4])+1 )
theta[5] = param[5]
} ,
"generalizedlambda" = { ## [R, R+, R, R]
theta = matrix(0,1,4)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = param[3]
theta[4] = param[4]
} ,
"generalizedt" = { ## [R, R+, R+, R+]
theta = matrix(0,1,4)
theta[1] = param[1]
theta[2:4] = exp(param[2:4])
} ,
"gompertz" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"gpd" = { ## [R, R+, R]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = param[3]
} ,
"gumbel" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"gumbel2" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"halfcauchy" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"halflogistic" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"halfnormal" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"halft" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"hjorth" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"hblaplace" = { ## [01, R, R+]
theta = matrix(0,1,3)
theta[1] = 1/(1+exp(-param[1]))
theta[2] = param[2]
theta[3] = exp(param[3])
} ,
"hyperbolic" = { ## [R, R+, R+, R]
theta = matrix(0,1,4)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = exp(param[3])
theta[4] = theta[3] * ( exp(2*param[4])-1 ) / ( exp(2*param[4])+1 )
} ,
"huber" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"hzeta" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"inversebeta" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"inverseburr" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"inversechisquared" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"inverseexponential" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"inverseexpexponential" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"inversegamma" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"inverselomax" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"inverseparalogistic" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"inversepareto" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"inversetransformedgamma" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"inverseweibull" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"kumaraswamy" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"kumaraswamyexponential" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"kumaraswamygamma" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"kumaraswamygumbel" = { ## [R+, R+, R, R+]
theta = matrix(0,1,4)
theta[1:2] = exp(param[1:2])
theta[3] = param[3]
theta[4] = exp(param[4])
} ,
"kumaraswamyhalfnormal" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"kumaraswamyloglogistic" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"kumaraswamynormal" = { ## [R, R+, R+, R+]
theta = matrix(0,1,4)
theta[1] = param[1]
theta[2:4] = exp(param[2:4])
} ,
"kumaraswamyweibull" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"laplace" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"levy" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"linearfailurerate" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"lindley" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"libbynovickbeta" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"logcauchy" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"loggamma" = { ## [R, R+, R+]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2:3] = exp(param[2:3])
} ,
"loggumbel" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"loglaplace" = { ## [R, R+, R+]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2:3] = exp(param[2:3])
} ,
"loglog" = { ## [R+, 01]
theta = matrix(0,1,2)
theta[1] = exp(param[1])
theta[2] = 1+exp(-param[2])
} ,
"loglogistic" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"lognormal" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"lognormal3" = { ## [R, R+, R]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = param[3]
} ,
"logistic" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"logisticexponential" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"logisticrayleigh" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"logseries" = { ## [01]
theta = matrix(0,1,1)
theta[1] = 1/(1+exp(-param[1]))
} ,
"lomax" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"makeham" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"maxwell" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"mcgilllaplace" = { ## [R, R+, R+]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2:3] = exp(param[2:3])
} ,
"moexponential" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"moweibull" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"nakagami" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"ncchisquared" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"ncF" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"negativebinomial" = { ## [N+, 01]
theta = matrix(0,1,1)
theta[1] = 1/(1+exp(-param[1]))
} ,
"normalinversegaussian" = { ## [R, R+, R+, R]
theta = matrix(0,1,4)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = exp(param[3])
theta[4] = theta[3] * ( exp(2*param[4])-1 ) / ( exp(2*param[4])+1 )
} ,
"nsbeta" = { ## [R+, R+, R(min), R(maxi)]
theta = matrix(0,1,4)
theta[1:2] = exp(param[1:2])
theta[3:4] = param[3:4]
} ,
"paralogistic" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"pareto" = { ## [R+, R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"paretopositivestable" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"pareto1" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"pareto2" = { ## [R, R+, R+]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2:3] = exp(param[2:3])
} ,
"pareto3" = { ## [R, R+, R+]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2:3] = exp(param[2:3])
} ,
"pareto4" = { ## [R, R+, R+, R+]
theta = matrix(0,1,4)
theta[1] = param[1]
theta[2:4] = exp(param[2:4])
} ,
"perks" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"pctalaplace" = { ## [R+, R]
theta = matrix(0,1,2)
theta[1] = exp(param[1])
theta[2] = param[2]
} ,
"poisson" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"power1" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"power2" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"powerdistribution" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"powerexponential" = { ## [R, R+, R+]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2:3] = exp(param[2:3])
} ,
"rayleigh" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"reflectedgamma" = { ## [R+, R, R+]
theta = matrix(0,1,3)
theta[1] = exp(param[1])
theta[2] = param[2]
theta[3] = exp(param[3])
} ,
"rice" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"scaledchisquared" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"schabe" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"simplex" = { ## [01, R+]
theta = matrix(0,1,2)
theta[1] = 1/(1+exp(-param[1]))
theta[2] = exp(param[2])
} ,
"skewedlaplace" = { ## [R, R+, R+]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2:3] = exp(param[2:3])
} ,
"skewedt" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"skewedtfourparam" = { ## [R, R+, R, R+(<25)]
theta = matrix(0,1,4)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = param[3]
theta[4] = 25/(1+exp(-param[4]))
} ,
"skewednormal" = { ## [R, R+, R, R]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = param[3]
} ,
"skewedgeneralizedt" = { ## [R, R+, -1+1, R+(>1), R+(>1)]
theta = matrix(0,1,4)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = (exp(2*param[3])-1)/(exp(2*param[3])+1)
theta[4] = 1+exp(-param[4])
} ,
"skewedexponentialpower" = { ## [R, R+, R, R+]
theta = matrix(0,1,4)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = param[3]
theta[4] = max( c( 10e-12, exp(param[4]) ) )
} ,
"slash" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"stacy" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"t" = { ## [R, R+, R+ (<25)]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = 25/(1+exp(-param[3]))
} ,
"tobit" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"topple" = { ## [01]
theta = matrix(0,1,1)
theta[1] = 1/(1+exp(-param[1]))
} ,
"transformedbeta" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"transformedgamma" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"truncatednormal" = { ## [R, R+, R(min), R(max)]
theta = matrix(0,1,4)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3:4] = param[3:4]
} ,
"truncatedpareto" = { ## [R+(mini), R+(maxi), R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"twosidedpower" = { ## [01, R+]
theta = matrix(0,1,2)
theta[1] = 1/(1+exp(-param[1]))
theta[2] = exp(param[2])
} ,
"wald" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"weibull" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"xie" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"yules" = { ## [R+] >0.5
theta = matrix(0,1,1)
theta[1] = 0.5+exp(-param[1])
} ,
)
return(theta)
}
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Defines functions alpha2theta
Documented in alpha2theta
#'@title Transforms unconstrained parameters to constrainted parameters
#'
#'@description This function computes the constrained parameters theta of a univariate distribution from the unconstrainted parameter alpha.
#'
#'@param family distribution name; run the command distributions() for help
#'@param param unconstrained parameters of the univariate distribution
#'
#'@return \item{theta}{constrained parameters}
#'
#'
#'@export
#'@keywords internal
alpha2theta<-function(family,param){
switch(family,
"asymexppower" = { ## [R+, R+, 01]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
theta[3] = 1/(1+exp(-param[3]))
} ,
"asymlaplace" = { ## [R, R+, R+]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2:3] = exp(param[2:3])
} ,
"asympower" = { ## [01, R+, R+]
theta = matrix(0,1,3)
theta[1] = 1/(1+exp(-param[1]))
theta[2:3] = exp(param[2:3])
} ,
"asymt" = { ## [R+, R+, 01, R]
theta = matrix(0,1,4)
theta[1:2] = exp(param[1:2])
theta[3] = 1/(1+exp(-param[3]))
theta[4] = param[4]
} ,
"beard" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"benini" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"benford" = { ## [1 ou 2]
theta = matrix(0,1,1)
if (param[1]>2){
theta[1] = 2
} else if (param[1]<1){
theta[1] = 1
} else {
theta[1] = round(param[1])
}
} ,
"bernoulli" = { ## [01]
theta = matrix(0,1,1)
theta[1] = 1/(1+exp(-param[1]))
} ,
"beta" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"betabinomial" = { ## [N+, R+, R+]
theta = matrix(0,1,2)
#theta[1] = round(param[1])
theta[1:2] = exp(param[1:2])
} ,
"betageometric" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"betanegativebinomial" = { ## [N+, R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"betaburr" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"betaburr7" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"betaexponential" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"betafrechet" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"betagompertz" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"betagumbel" = { ## [R+, R+, R, R+]
theta = matrix(0,1,4)
theta[1:2] = exp(param[1:2])
theta[3] = param[3]
theta[4] = exp(param[4])
} ,
"betagumbel2" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"betalognormal" = { ## [R+, R+, R, R+]
theta = matrix(0,1,4)
theta[1:2] = exp(param[1:2])
theta[3] = param[3]
theta[4] = exp(param[4])
} ,
"betalomax" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"betanormal" = { ## [R+, R+, R, R+]
theta = matrix(0,1,4)
theta[1:2] = exp(param[1:2])
theta[3] = param[3]
theta[4] = exp(param[4])
} ,
"betaprime" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"betaweibull" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"bhattacharjee" = { ## [R, R+, R+]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2:3] = exp(param[2:3])
} ,
"binomial" = { ## [N+, 01]
theta = matrix(0,1,1)
theta[1] = 1 / (1+exp(-param[1]))
} ,
"birnbaumsaunders" = { ## [R+, R+, R]
theta = matrix(0,1,3)
theta[1:2] = exp(param[1:2])
theta[3] = param[3]
} ,
"boxcox" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"burr" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"burr2param" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"cauchy" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"chen" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"chi" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"chisquared" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"clg" = { ## [R+, R+, R]
theta = matrix(0,1,3)
theta[1:2] = exp(param[1:2])
theta[3] = param[3]
} ,
"complementarybeta" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"dagum" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"diffzeta" = { ## [R+, >1]
theta = matrix(0,1,2)
theta[1] = exp(param[1])
theta[2] = 1+exp(-param[2])
} ,
"discretegamma" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"discretelaplace" = { ## [R, 01]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = 1/(1+exp(-param[2]))
} ,
"discretenormal" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"discreteweibull" = { ## [01, R+]
theta = matrix(0,1,2)
theta[1] = 1/(1+exp(-param[1]))
theta[2] = exp(param[2])
} ,
"doubleweibull" = { ## [R+, R, R+]
theta = matrix(0,1,3)
theta[1] = exp(param[1])
theta[2] = param[2]
theta[3] = exp(param[3])
} ,
"ev" = {
## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"exponential" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"exponentialextension" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"exponentialgeometric" = { ## [R+, 01]
theta = matrix(0,1,2)
theta[1] = exp(param[1])
theta[2] = 1/(1+exp(-param[2]))
} ,
"exponentiallogarithmic" = { ## [R+, 01]
theta = matrix(0,1,2)
theta[1] = exp(param[1])
theta[2] = 1/(1+exp(-param[2]))
} ,
"exponentialpoisson" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"exponentialpower" = { ## [R, R+, R+]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2:3] = exp(param[2:3])
} ,
"exponentiatedexponential" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"exponentiatedlogistic" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"exponentiatedweibull" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"F" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"fellerpareto" = { ## [R(mini), R+, R+, R+, R+]
theta = matrix(0,1,5)
theta[1] = param[1]
theta[2:5] = exp(param[2:5])
} ,
"fisk" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"foldednormal" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"frechet" = { ## [R+, R, R+]
theta = matrix(0,1,3)
theta[1] = exp(param[1])
theta[2] = param[2]
theta[3] = exp(param[3])
} ,
"gamma" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"gammapoisson" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"gaussian" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"gev" = { ## [R, R+, R]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = param[3]
} ,
"geninvbeta" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"genlogis" = { ## [R+, R, R+]
theta = matrix(0,1,3)
theta[1] = exp(param[1])
theta[2] = param[2]
theta[3] = exp(param[3])
} ,
"genlogis3" = { ## [R+, R, R+]
theta = matrix(0,1,3)
theta[1] = exp(param[1])
theta[2] = param[2]
theta[3] = exp(param[3])
} ,
"genlogis4" = { ## [R+, R+, R, R+]
theta = matrix(0,1,4)
theta[1:2] = exp(param[1:2])
theta[3] = param[3]
theta[4] = exp(param[4])
} ,
"genpowerweibull" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"geometric" = { ## [01]
theta = matrix(0,1,1)
theta[1] = 1/(1+exp(-param[1]))
} ,
"generalizedhyperbolic" = { ## [R, R+, R+, R, R] [mu, delta, alpha, beta, lambda] (avec alpha^2 > beta^2)
theta = matrix(0,1,5)
theta[1] = param[1]
theta[2] = max( c( 10e-12, exp(param[2]) ) )
theta[3] = max( c( 10e-12, exp(param[3]) ) )
theta[4] = theta[3] * ( exp(2*param[4])-1 ) / ( exp(2*param[4])+1 )
theta[5] = param[5]
} ,
"generalizedlambda" = { ## [R, R+, R, R]
theta = matrix(0,1,4)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = param[3]
theta[4] = param[4]
} ,
"generalizedt" = { ## [R, R+, R+, R+]
theta = matrix(0,1,4)
theta[1] = param[1]
theta[2:4] = exp(param[2:4])
} ,
"gompertz" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"gpd" = { ## [R, R+, R]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = param[3]
} ,
"gumbel" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"gumbel2" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"halfcauchy" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"halflogistic" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"halfnormal" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"halft" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"hjorth" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"hblaplace" = { ## [01, R, R+]
theta = matrix(0,1,3)
theta[1] = 1/(1+exp(-param[1]))
theta[2] = param[2]
theta[3] = exp(param[3])
} ,
"hyperbolic" = { ## [R, R+, R+, R]
theta = matrix(0,1,4)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = exp(param[3])
theta[4] = theta[3] * ( exp(2*param[4])-1 ) / ( exp(2*param[4])+1 )
} ,
"huber" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"hzeta" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"inversebeta" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"inverseburr" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"inversechisquared" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"inverseexponential" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"inverseexpexponential" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"inversegamma" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"inverselomax" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"inverseparalogistic" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"inversepareto" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"inversetransformedgamma" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"inverseweibull" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"kumaraswamy" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"kumaraswamyexponential" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"kumaraswamygamma" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"kumaraswamygumbel" = { ## [R+, R+, R, R+]
theta = matrix(0,1,4)
theta[1:2] = exp(param[1:2])
theta[3] = param[3]
theta[4] = exp(param[4])
} ,
"kumaraswamyhalfnormal" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"kumaraswamyloglogistic" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"kumaraswamynormal" = { ## [R, R+, R+, R+]
theta = matrix(0,1,4)
theta[1] = param[1]
theta[2:4] = exp(param[2:4])
} ,
"kumaraswamyweibull" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"laplace" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"levy" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"linearfailurerate" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"lindley" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"libbynovickbeta" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"logcauchy" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"loggamma" = { ## [R, R+, R+]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2:3] = exp(param[2:3])
} ,
"loggumbel" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"loglaplace" = { ## [R, R+, R+]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2:3] = exp(param[2:3])
} ,
"loglog" = { ## [R+, 01]
theta = matrix(0,1,2)
theta[1] = exp(param[1])
theta[2] = 1+exp(-param[2])
} ,
"loglogistic" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"lognormal" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"lognormal3" = { ## [R, R+, R]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = param[3]
} ,
"logistic" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"logisticexponential" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"logisticrayleigh" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"logseries" = { ## [01]
theta = matrix(0,1,1)
theta[1] = 1/(1+exp(-param[1]))
} ,
"lomax" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"makeham" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"maxwell" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"mcgilllaplace" = { ## [R, R+, R+]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2:3] = exp(param[2:3])
} ,
"moexponential" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"moweibull" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"nakagami" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"ncchisquared" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"ncF" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"negativebinomial" = { ## [N+, 01]
theta = matrix(0,1,1)
theta[1] = 1/(1+exp(-param[1]))
} ,
"normalinversegaussian" = { ## [R, R+, R+, R]
theta = matrix(0,1,4)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = exp(param[3])
theta[4] = theta[3] * ( exp(2*param[4])-1 ) / ( exp(2*param[4])+1 )
} ,
"nsbeta" = { ## [R+, R+, R(min), R(maxi)]
theta = matrix(0,1,4)
theta[1:2] = exp(param[1:2])
theta[3:4] = param[3:4]
} ,
"paralogistic" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"pareto" = { ## [R+, R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"paretopositivestable" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"pareto1" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"pareto2" = { ## [R, R+, R+]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2:3] = exp(param[2:3])
} ,
"pareto3" = { ## [R, R+, R+]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2:3] = exp(param[2:3])
} ,
"pareto4" = { ## [R, R+, R+, R+]
theta = matrix(0,1,4)
theta[1] = param[1]
theta[2:4] = exp(param[2:4])
} ,
"perks" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"pctalaplace" = { ## [R+, R]
theta = matrix(0,1,2)
theta[1] = exp(param[1])
theta[2] = param[2]
} ,
"poisson" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"power1" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"power2" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"powerdistribution" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"powerexponential" = { ## [R, R+, R+]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2:3] = exp(param[2:3])
} ,
"rayleigh" = { ## [R+]
theta = matrix(0,1,1)
theta[1] = exp(param[1])
} ,
"reflectedgamma" = { ## [R+, R, R+]
theta = matrix(0,1,3)
theta[1] = exp(param[1])
theta[2] = param[2]
theta[3] = exp(param[3])
} ,
"rice" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"scaledchisquared" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"schabe" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"simplex" = { ## [01, R+]
theta = matrix(0,1,2)
theta[1] = 1/(1+exp(-param[1]))
theta[2] = exp(param[2])
} ,
"skewedlaplace" = { ## [R, R+, R+]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2:3] = exp(param[2:3])
} ,
"skewedt" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"skewedtfourparam" = { ## [R, R+, R, R+(<25)]
theta = matrix(0,1,4)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = param[3]
theta[4] = 25/(1+exp(-param[4]))
} ,
"skewednormal" = { ## [R, R+, R, R]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = param[3]
} ,
"skewedgeneralizedt" = { ## [R, R+, -1+1, R+(>1), R+(>1)]
theta = matrix(0,1,4)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = (exp(2*param[3])-1)/(exp(2*param[3])+1)
theta[4] = 1+exp(-param[4])
} ,
"skewedexponentialpower" = { ## [R, R+, R, R+]
theta = matrix(0,1,4)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = param[3]
theta[4] = max( c( 10e-12, exp(param[4]) ) )
} ,
"slash" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"stacy" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"t" = { ## [R, R+, R+ (<25)]
theta = matrix(0,1,3)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3] = 25/(1+exp(-param[3]))
} ,
"tobit" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"topple" = { ## [01]
theta = matrix(0,1,1)
theta[1] = 1/(1+exp(-param[1]))
} ,
"transformedbeta" = { ## [R+, R+, R+, R+]
theta = matrix(0,1,4)
theta[1:4] = exp(param[1:4])
} ,
"transformedgamma" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"truncatednormal" = { ## [R, R+, R(min), R(max)]
theta = matrix(0,1,4)
theta[1] = param[1]
theta[2] = exp(param[2])
theta[3:4] = param[3:4]
} ,
"truncatedpareto" = { ## [R+(mini), R+(maxi), R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"twosidedpower" = { ## [01, R+]
theta = matrix(0,1,2)
theta[1] = 1/(1+exp(-param[1]))
theta[2] = exp(param[2])
} ,
"wald" = { ## [R, R+]
theta = matrix(0,1,2)
theta[1] = param[1]
theta[2] = exp(param[2])
} ,
"weibull" = { ## [R+, R+]
theta = matrix(0,1,2)
theta[1:2] = exp(param[1:2])
} ,
"xie" = { ## [R+, R+, R+]
theta = matrix(0,1,3)
theta[1:3] = exp(param[1:3])
} ,
"yules" = { ## [R+] >0.5
theta = matrix(0,1,1)
theta[1] = 0.5+exp(-param[1])
} ,
)
return(theta)
}
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