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Packaged: 2021-06-09 12:50:02 UTC; melinaribaud

Title: ZOIP Distribution, ZOIP Regression, ZOIP Mixed Regression
Description: The ZOIP distribution (Zeros Ones Inflated Proportional) is a proportional data distribution inflated with zeros and/or ones, this distribution is defined on the most known proportional data distributions, the beta and simplex distribution, Jørgensen and Barndorff-Nielsen (1991) <doi:10.1016/0047-259X(91)90008-P>, also allows it to have different parameterizations of the beta distribution, Ferrari and Cribari-Neto (2004) <doi:10.1080/0266476042000214501>, Rigby and Stasinopoulos (2005) <doi:10.18637/jss.v023.i07>. The ZOIP distribution has four parameters, two of which correspond to the proportion of zeros and ones, and the other two correspond to the distribution of the proportional data of your choice. The 'ZOIP' package allows adjustments of regression models for fixed and mixed effects for proportional data inflated with zeros and/or ones.
Authors@R: person("Juan Camilo", "Diaz Zapata", email = "jucdiazza@unal.edu.co", role = c("aut", "cre"))
Imports: rmutil, boot, numDeriv, GHQp, stats
Author: Juan Camilo Diaz Zapata [aut, cre]
Maintainer: Juan Camilo Diaz Zapata <jucdiazza@unal.edu.co>

Title: Dynamic Plots for Time Series Forecasting
Description: Combines a forecast of a time series, using the function forecast(), with the dynamic plots from dygraphs.
Depends: R (>= 3.0.2), forecast, dygraphs, stats

Authors@R: c(person("Leah F.","South",role=c("aut","cre"),email="leah.south@hdr.qut.edu.au",comment = c(ORCID = "0000-0002-5646-2963")))
Description: Zero-variance control variates (ZV-CV, Mira et al. (2013) <doi:10.1007/s11222-012-9344-6>) is a post-processing method to reduce the variance of Monte Carlo estimators of expectations using the derivatives of the log target. Once the derivatives are available, the only additional computational effort is in solving a linear regression problem. Recently, this method has been extended to higher dimensions using regularisation (South et al., 2018 <arXiv:1811.05073>). This package can be used to easily perform ZV-CV or regularised ZV-CV when a set of samples, derivatives and function evaluations are available. Additional functions for applying ZV-CV to two estimators for the normalising constant of the posterior distribution in Bayesian statistics are also supplied.
Imports: Rcpp (>= 0.11.0), glmnet, abind, mvtnorm, partitions, stats
Author: Leah F. South [aut, cre] (<https://orcid.org/0000-0002-5646-2963>)
Maintainer: Leah F. South <leah.south@hdr.qut.edu.au>