Interface to the 'Metabolights' RESTful API
Access to the 'Metabolights' RESTful API <https://www.ebi.ac.uk/metabolights/index>. Retrieve elements of publically available 'Metabolights' studies.

covr, testthat, tibble, knitr, rmarkdown, tidyr

Interface to the 'Metabolights' REST API
Access to the 'Metabolights' REST API <https://www.ebi.ac.uk/metabolights/index>. Retrieve elements of publicly available 'Metabolights' studies.

A collection of functions for analysing metabolomics data
A collection of functions to aid in the statistical
analysis of metabolomic data

A collection of functions to aid in the statistical analysis of metabolomic data

person(given = "Xikun",
family = "Han",
role = c("cre", "aut"),
email = "hanxikun2017@gmail.com")
Tools to preprocess, analyse, and visualize metabolomics data.
We included a set of functions for sample and metabolite quality control,
outlier detection, missing value imputation, dimensional reduction, normalization,
data integration, regression, metabolite annotation, enrichment analysis,
and visualization of data and results. The package is designed to be a comprehensive R package
that can be easily used by researchers with basic R programming skills.
The framework designed here is versatile and is extensible to other various methods.
ggplot2, data.table, plotROC, utils, stats
ggthemes, knitr, rmarkdown, testthat (>= 3.0.0), lme4, nlme,
broom, reshape2, impute, M3C, FNN, RColorBrewer, readxl,
survival, future, pbapply, future.apply, progressr, ggrepel,
here, genuMet, ggstatsplot, cowplot, pROC, BiocStyle, MASS,
xgboost
Software, Metabolomics, MassSpectrometry, Regression,
Normalization, QualityControl

Bayesian Meta-Analysis Using Basic Uncertain Pooling
Contains functions that allow Bayesian meta-analysis (1) with binomial data, counts(y) and total counts (n) or, (2) with user-supplied point estimates and associated variances.   Case (1) provides an analysis based on the logit transformation of the sample proportion. This methodology is also appropriate for combining data from sample surveys and related sources. The functions can  calculate the corresponding similarity matrix. More details can be found in Cahoy and Sedransk (2023), Cahoy and Sedransk (2022)  <doi:10.1007/s42519-018-0027-2>, Evans and Sedransk (2001) <doi:10.1093/biomet/88.3.643>, and Malec and Sedransk (1992) <doi:10.1093/biomet/79.3.593>.