Packaged: Wed Sep 17 20:24:44 2008; ritz

Packaged: Mon Nov  3 21:10:33 2008; ritz

Packaged: Mon Nov 10 18:34:56 2008; ritz

Packaged: Thu Nov 13 08:23:18 2008; ritz

Packaged: Sun Feb  8 10:34:16 2009; ritz

Author: Christian Ritz <ritz@bioassay.dk>, Jens Strebig
Description: Analysis of one or multiple curves with focus on
concentration-response, dose-response and time-response curves
used, for example in biology, environmental sciences, medicine,
Packaged: Tue Mar 24 08:16:36 2009; ritz

Packaged: Wed Mar 25 08:20:30 2009; ritz

Depends: R (>= 2.0.0), alr3, lattice, magic, MASS, nlme, plotrix

Depends: R (>= 2.0.0), lattice, magic, MASS, nlme, plotrix, methods

Depends: R (>= 2.0.0), alr3, lattice, magic, MASS, nlme, plotrix,

Depends: R (>= 2.0.0), alr3, gtools, lattice, magic, MASS, nlme,

Description: Analysis of dose-response curves in biology, environmental

Title: Analysis of dose-response curve data
Author: Christian Ritz <ritz@bioassay.dk>, Jens Strebig <jcs@life.ku.dk>
Depends: R (>= 2.0.0), car, gtools, MASS, magic, methods, plotrix,
Description: Analysis of dose-response curves in biology, environmental sciences, medicine, pharmacology, toxicology

Author: Christian Ritz <ritz@bioassay.dk>, Jens C. Strebig <streibig@bioassay.dk>
Imports: car, gtools, multcomp, plotrix, scales
Description: Analysis of dose-response data is made available through a suite of flexible and versatile model fitting and after-fitting functions.

Description: Fit logistic functions to observed dose-response data and evaluate
goodness of fit measures. See Malyutina A., Tang J., and Pessia A. (2021)
Author: Alberto Pessia [aut, cre] (<https://orcid.org/0000-0001-8607-9191>),

person("Alberto", "Pessia", , "dev@albertopessia.com", role = c("aut", "cre"),
person("Alina", "Malyutina", role = "ctb",
Description: Fit logistic functions to observed dose-response continuous
data and evaluate goodness-of-fit measures. See Malyutina A., Tang J.,
Suggests: knitr, rmarkdown, spelling, testthat (>= 3.1.0)

Title: Dose Response Models for Bayesian Model Averaging
Description: Fits (longitudinal) dose-response models utilizing a Bayesian model
averaging approach as outlined in Gould (2019) <doi:10.1002/bimj.201700211>
for both continuous and binary responses.  Functions
for plotting and calculating various posterior quantities
(e.g. posterior mean, quantiles, probability of minimum efficacious dose,
etc.) are also implemented.  Copyright Eli Lilly and Company (2019).
Imports: coda, dplyr (>= 1.0.0), ggplot2, graphics, purrr, rootSolve,
Suggests: testthat (>= 3.0), fs (>= 1.5), knitr, rmarkdown, tibble,