c(person(given = "Lifeng", family = "Lin",
role = c("aut", "cre"), email = "linl@stat.fsu.edu",
comment = c(ORCID = "0000-0002-3562-9816")),
person(given = "Linyu", family = "Shi", role = c("ctb")),
person(given = "Kristine J.", family = "Rosenberger", role = c("ctb")),
person(given = "Haitao", family = "Chu",
role = c("aut"), email = "chux0051@umn.edu",
comment = c(ORCID = "0000-0003-0932-598X")))
Provides alternative statistical methods for meta-analysis, including:
- bivariate generalized linear mixed models for synthesizing odds ratios, relative risks, and risk differences
(Chu et al., 2012 <doi:10.1177/0962280210393712>)
- heterogeneity tests and measures that are robust to outliers
(Lin et al., 2017 <doi:10.1111/biom.12543>);
- measures, tests, and visualization tools for publication bias or small-study effects
(Lin and Chu, 2018 <doi:10.1111/biom.12817>; Lin, 2019 <doi:10.1002/jrsm.1340>;
Lin, 2020 <doi:10.1177/0962280220910172>; Shi et al., 2020 <doi:10.1002/jrsm.1415>);
- meta-analysis of diagnostic tests for synthesizing sensitivities, specificities, etc.
(Reitsma et al., 2005 <doi:10.1016/j.jclinepi.2005.02.022>;
Chu and Cole, 2006 <doi:10.1016/j.jclinepi.2006.06.011>);
- meta-analysis methods for synthesizing proportions
(Lin and Chu, 2020 <doi:10.1097/ede.0000000000001232>);
- models for multivariate meta-analysis
(Lin and Chu, 2018 <doi:10.1002/jrsm.1293>);
- measures of inconsistency degrees of freedom in Bayesian network meta-analysis
(Lin, 2020 <doi:10.1080/10543406.2020.1852247>).
Lifeng Lin [aut, cre] (<https://orcid.org/0000-0002-3562-9816>),
Linyu Shi [ctb],
Kristine J. Rosenberger [ctb],
Haitao Chu [aut] (<https://orcid.org/0000-0003-0932-598X>)

rjags (>= 4-6), coda, graphics, grDevices, lme4, Matrix,
metafor (>= 3.0-2), methods, stats, utils
Provides alternative statistical methods for meta-analysis, including:
- bivariate generalized linear mixed models for synthesizing odds ratios, relative risks,
and risk differences
(Chu et al., 2012 <doi:10.1177/0962280210393712>)
- heterogeneity tests and measures that are robust to outliers
(Lin et al., 2017 <doi:10.1111/biom.12543>);
- measures, tests, and visualization tools for publication bias or small-study effects
(Lin and Chu, 2018 <doi:10.1111/biom.12817>;
Lin, 2019 <doi:10.1002/jrsm.1340>;
Lin, 2020 <doi:10.1177/0962280220910172>;
Shi et al., 2020 <doi:10.1002/jrsm.1415>);
- meta-analysis of diagnostic tests for synthesizing sensitivities, specificities, etc.
(Reitsma et al., 2005 <doi:10.1016/j.jclinepi.2005.02.022>;
Chu and Cole, 2006 <doi:10.1016/j.jclinepi.2006.06.011>);
- meta-analysis methods for synthesizing proportions
(Lin and Chu, 2020 <doi:10.1097/ede.0000000000001232>);
- models for multivariate meta-analysis, measures of inconsistency degrees of freedom
in Bayesian network meta-analysis, and predictive P-score
(Lin and Chu, 2018 <doi:10.1002/jrsm.1293>;
Lin, 2020 <doi:10.1080/10543406.2020.1852247>;
Rosenberger et al., 2021 <doi:10.1186/s12874-021-01397-5>).