Title: Tools to Fit Flexible Parametric Curves
Description: selfStart functions and model selection tools to fit
parametric curves in nls, nlsList and nlme frameworks.

Description: selfStart functions and model selection tools to fit parametric curves in nls, nlsList and nlme frameworks.

Authors@R: c(person("Stephen", "Oswald", role = c("aut", "cre"),
Description: Model selection tools and 'selfStart' functions to fit parametric curves in 'nls', 'nlsList' and 'nlme' frameworks.

Title: Regression Models for Bounded Responses
Author: Agnese M. Di Brisco [aut, cre], Roberto Ascari [aut, cre], Sonia Migliorati [aut], Andrea Ongaro [aut]
Description: Functions to fit regression models for bounded responses, such as rates and proportions. Available models are the flexible beta (Migliorati, S., Di Brisco, A. M., Ongaro, A. (2018) <doi:10.1214/17-BA1079>), the variance-inflated beta (Di Brisco, A. M., Migliorati, S., Ongaro, A. (2020) <doi:10.1177/1471082X18821213>), and the beta one (Ferrari, S.L.P., and Cribari-Neto, F. (2004) <doi:10.1080/0266476042000214501>). Inference is dealt with a Bayesian approach based on the Hamiltonian Monte Carlo (HMC) algorithm (Gelman, A.; Carlin, J. B.; Stern, H. S. and Rubin, D. B. (2014) <doi:10.1201/b16018>). Besides, functions to compute residuals, posterior predictives, goodness-of-fit measures, convergence diagnostics, and graphical representations are provided.
Imports: methods, Rcpp (>= 0.12.0), rstan (>= 2.18.1), rstantools (>=
2.0.0), loo, bayesplot, ggplot2, Formula, utils, grDevices
Packaged: 2020-10-29 14:11:04 UTC; roberto.ascari

Description: Functions to fit regression models for bounded responses (e.g., proportions and rates) and binomial data. Available models are the flexible beta (Migliorati, S., Di Brisco, A. M., Ongaro, A. (2018) <doi:10.1214/17-BA1079>), the variance-inflated beta (Di Brisco, A. M., Migliorati, S., Ongaro, A. (2020) <doi:10.1177/1471082X18821213>), the beta (Ferrari, S.L.P., and Cribari-Neto, F. (2004) <doi:10.1080/0266476042000214501>), the flexible beta-binomial (Ascari, R. and Migliorati, S. (2021) <doi:10.1002/sim.9005>), the beta-binomial, and the binomial one. Inference is dealt with a Bayesian approach based on the Hamiltonian Monte Carlo (HMC) algorithm (Gelman, A.; Carlin, J. B.; Stern, H. S. and Rubin, D. B. (2014) <doi:10.1201/b16018>). Besides, functions to compute residuals, posterior predictives, goodness-of-fit measures, convergence diagnostics, and graphical representations are provided.
2.0.0), loo, bayesplot, ggplot2, Formula, utils, grDevices,
Packaged: 2022-01-17 15:54:26 UTC; roberto.ascari