Interface to 'Altair' <https://altair-viz.github.io>, which itself 
  is a 'Python' interface to 'Vega-Lite' <https://vega.github.io/vega-lite/>.
  This package uses the 'Reticulate' framework 
  <https://rstudio.github.io/reticulate/> to manage the interface between R
  and 'Python'.

This is a simple and powerful package to create, render, preview,
    and deploy documentation websites for 'R' packages. It is a lightweight and
    flexible alternative to 'pkgdown', with support for many documentation
    generators, including 'Quarto', 'Docute', 'Docsify', and 'MkDocs'.

Inference of protein complex states from quantitative proteomics data. The package takes information on known stable protein interactions (i.e. protein components of the same complex) and assesses how protein quantitative ratios change between different conditions. It reports protein pairs for which relative protein quantities to each other have been significantly altered in the tested condition.

Alternative and fast algorithms for the analysis of receiver operating 
  characteristics curves (ROC curves) as described in Thomas et al. (2017) 
  <doi:10.1186/s41512-017-0017-y> and Thomas et al. (2023) <doi:10.1016/j.ajogmf.2023.101110>.

Provides a number of functions to access the 
    National Energy Research Laboratory Alternate Fuel Locator API 
    <https://developer.nrel.gov/docs/transportation/alt-fuel-stations-v1/>. 
    The Alternate Fuel Locator shows the location of alternate fuel stations in the United States 
    and Canada. This package also includes the data from the US Department of Energy Alternate Fuel
    database as a data set.

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>)
 - tests and measures for between-study heterogeneity
   (Lin et al., 2017 <doi:10.1111/biom.12543>;
    Wang et al., 2022 <doi:10.1002/sim.9261>);
 - 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 combining standardized mean differences and odds ratios
   (Jing et al., 2023 <doi:10.1080/10543406.2022.2105345>);
 - 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>).