person(given = "Simon",
family = "Leonard",
role = c("aut", "cre"),
email = "simon.leonard@univ-rennes1.fr",
comment = c(ORCID = "0000-0002-6437-5800"))
Flexible Dotplot and Pacman plot for multimodal data.
ggplot2, grDevices, stats, FactoMineR, scales, reshape2,
ggdendro, grImport2, grid, gridExtra, sisal, dendextend, shiny,
shinydashboard, shinyWidgets, ggforce, colourpicker, htmltools,
bsplus, DT, magrittr
Simon Leonard [aut, cre] (<https://orcid.org/0000-0002-6437-5800>)

Generalized Additive Models with Flexible Response Functions
person("Elmar", "Spiegel", role = c("aut", "cre"), email = "espiege@uni-goettingen.de")
graphics, MASS, Matrix, scam, splines, stats
Standard generalized additive models assume a response function,
which induces an assumption on the shape of the distribution of the
response. However, miss-specifying the response function results in biased
estimates. Therefore in Spiegel et al. (2017)
<doi:10.1007/s11222-017-9799-6> we propose to estimate the response function
jointly with the covariate effects. This package provides the underlying
functions to estimate these generalized additive models with flexible
response functions. The estimation is based on an iterative algorithm. In
the outer loop the response function is estimated, while in the inner loop
the covariate effects are determined. For the response function a strictly
monotone P-spline is used while the covariate effects are estimated based on
a modified Fisher-Scoring algorithm. Overall the estimation relies on the
'mgcv'-package.

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

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

c(person("Stephen", "Oswald", role = c("aut", "cre"),
email = "steve.oswald@psu.edu"))
Model selection tools and 'selfStart' functions to fit parametric curves in 'nls', 'nlsList' and 'nlme' frameworks.

Agnese M. Di Brisco [aut, cre], Roberto Ascari [aut, cre], Sonia Migliorati [aut], Andrea Ongaro [aut]
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.
methods, Rcpp (>= 0.12.0), rstan (>= 2.18.1), rstantools (>=
2.0.0), loo, bayesplot, ggplot2, Formula, utils, grDevices