Authors@R: c(person(given = "Thomas", family = "Debray", role = c("aut", "cre"),
person(given = "Valentijn", family = "de Jong", role = "aut",
Description: Meta-analysis of diagnostic and prognostic modeling studies. Summarize estimates of prognostic factors,
diagnostic test accuracy and prediction model performance. Validate, update and
combine published prediction models. Develop new prediction models with data from multiple studies.
Imports: metafor (>= 2.0.0), mvtnorm, lme4, plyr, methods, pROC,
Suggests: runjags, rjags, logistf (>= 1.23), testthat (>= 1.0.2)
Valentijn de Jong [aut] (<https://orcid.org/0000-0001-9921-3468>)

Title: Meta-Analysis of Diagnosis and Prognosis Research Studies
Description: Facilitate meta-analysis of diagnosis and prognosis research studies. It includes functions to  summarize multiple estimates of prediction model discrimination and calibration performance, as described by Debray et al. (2019) <doi:10.1177/0962280218785504>. It also includes functions to evaluate funnel plot asymmetry, as described by Debray et al. (2018) <doi:10.1002/jrsm.1266>. Finally, the package provides functions for developing multivariable prediction models from datasets with clustering.

Description: Facilitate frequentist and Bayesian meta-analysis of diagnosis and prognosis research studies. It includes functions to  summarize multiple estimates of prediction model discrimination and calibration performance (Debray et al., 2019) <doi:10.1177/0962280218785504>. It also includes functions to evaluate funnel plot asymmetry (Debray et al., 2018) <doi:10.1002/jrsm.1266>. Finally, the package provides functions for developing multivariable prediction models from datasets with clustering (de Jong et al., 2021) <doi:10.1002/sim.8981>.
Suggests: runjags, rjags, ggmcmc, logistf (>= 1.23), testthat (>=

Imports: metafor (>= 2.0.0), mvtnorm, lme4, dplyr, plyr, methods, pROC,
Suggests: coda, runjags, rjags, ggmcmc, logistf (>= 1.23), testthat (>=

Title: Multi Environment Trials Analysis
Description: Performs stability analysis of multi-environment
trial data using parametric and non-parametric methods. Parametric
methods includes Additive Main Effects and Multiplicative Interaction
Ecovalence by Wricke (1965), Genotype plus Genotype-Environment (GGE)
geometric adaptability index by Mohammadi & Amri (2008)
<doi:10.1007/s10681-007-9600-6>, joint regression analysis by Eberhart
genotypic confidence index by Annicchiarico (1992), Murakami & Cruz's
(POLAR) statistics by Doring et al. (2015)
variation by Doring & Reckling (2018) <doi:10.1016/j.eja.2018.06.007>,
stability variance by Shukla (1972) <doi:10.1038/hdy.1972.87>,
weighted average of absolute scores by Olivoto et al. (2019a)
<doi:10.2134/agronj2019.03.0220>, and multi-trait stability index by
Non-parametric methods includes superiority index by Lin & Binns
(1988) <doi:10.4141/cjps88-018>, nonparametric measures of phenotypic
statistic by Fox et al. (1990) <doi:10.1007/BF00040364>. Functions for
computing biometrical analysis such as path analysis, canonical
correlation, partial correlation, clustering analysis, and tools for
inspecting, manipulating, summarizing and plotting typical
multi-environment trial data are also provided.
Imports: cowplot, dplyr (>= 1.0.0), GGally, ggforce, ggplot2 (>=
3.3.0), ggrepel, grid, lme4, lmerTest, magrittr, mathjaxr,
methods, progress, purrr, rlang (>= 0.1.2), tibble, tidyr,
Author: Tiago Olivoto [aut, cre, cph] (<https://orcid.org/0000-0002-0241-9636>)