Meta-Analysis of Diagnosis and Prognosis Research Studies
c(person(given = "Thomas", family = "Debray", role = c("aut", "cre"),
email = "thomas.debray@gmail.com", comment="https://orcid.org/0000-0002-1790-2719"),
person(given = "Valentijn", family = "de Jong", role = "aut",
email="Valentijn.M.T.de.Jong@gmail.com", comment="https://orcid.org/0000-0001-9921-3468"))
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.

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>.
runjags, rjags, ggmcmc, logistf (>= 1.23), testthat (>=
1.0.2)

metafor (>= 2.0.0), mvtnorm, lme4, dplyr, plyr, methods, pROC,
ggplot2
coda, runjags, rjags, ggmcmc, logistf (>= 1.23), testthat (>=
1.0.2)

person(given = "Tiago",
family = "Olivoto",
role = c("aut", "cre", "cph"),
email = "tiagoolivoto@gmail.com",
comment = c(ORCID = "0000-0002-0241-9636"))
Performs stability analysis of multi-environment
trial data using parametric and non-parametric methods. Parametric
methods includes Additive Main Effects and Multiplicative Interaction
(AMMI) analysis by Gauch (2013) <doi:10.2135/cropsci2013.04.0241>,
Ecovalence by Wricke (1965), Genotype plus Genotype-Environment (GGE)
biplot analysis by Yan & Kang (2003) <doi:10.1201/9781420040371>,
geometric adaptability index by Mohammadi & Amri (2008)
<doi:10.1007/s10681-007-9600-6>, joint regression analysis by Eberhart
& Russel (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>,
genotypic confidence index by Annicchiarico (1992), Murakami & Cruz's
(2004) method <doi:10.12702/1984-7033.v04n01a02>, power law residuals
(POLAR) statistics by Doring et al. (2015)
<doi:10.1016/j.fcr.2015.08.005>, scale-adjusted coefficient of
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
Olivoto et al. (2019b) <doi:10.2134/agronj2019.03.0221>.
Non-parametric methods includes superiority index by Lin & Binns
(1988) <doi:10.4141/cjps88-018>, nonparametric measures of phenotypic
stability by Huehn (1990)
<https://link.springer.com/article/10.1007/BF00024241>, TOP third
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.
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,
tidyselect (>= 1.0.0)
Tiago Olivoto [aut, cre, cph] (<https://orcid.org/0000-0002-0241-9636>)