Column-wise MLE of some univariate distributions.
colbeta.est(x, tol = 1e-07, maxiters = 100, parallel = FALSE)
collogitnorm.est(x)
colunitweibull.est(x, tol = 1e-07, maxiters = 100, parallel = FALSE)
colsimplex.est(x, tol = 1e-07)A numerical matrix with data. Each column refers to a different vector of observations of the same distribution. The values must by percentages, exluding 0 and 1,
The tolerance value to terminate the Newton-Fisher algorithm.
The maximum number of iterations to implement.
Do you want to calculations to take place in parallel? The default value is FALSE
A matrix with two or three columns. The first one or the first two contain the parameter(s) of the distribution and the second or third column the relevant log-likelihood.
For each column, the same distribution is fitted and its parameters and log-likelihood are computed.
N.L. Johnson, S. Kotz \& N. Balakrishnan (1994). Continuous Univariate Distributions, Volume 1 (2nd Edition).
N.L. Johnson, S. Kotz \& N. Balakrishnan (1970). Distributions in statistics: continuous univariate distributions, Volume 2.
J. Mazucheli, A. F. B. Menezes, L. B. Fernandes, R. P. de Oliveira & M. E. Ghitany (2020). The unit-Weibull distribution as an alternative to the Kumaraswamy distribution for the modeling of quantiles conditional on covariates. Journal of Applied Statistics, DOI:10.1080/02664763.2019.1657813.
# NOT RUN {
x <- matrix( rbeta(200, 3, 4), ncol = 4 )
a <- colbeta.est(x)
# }
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