Prepare R
This guide checks tidymass 2.0.10 and the current source of its companion packages. Some packages declare R 4.1 as a minimum, but current masstools requires R ≥ 4.5. Use R 4.5 or a later compatible release satisfying the full dependency set, with a writable user library and your preferred IDE.
Source compilation on Windows needs a toolchain matching R; macOS may need command-line development tools and package-specific system libraries. Diagnose the first substantive installation error before adding dependencies. Docker/ProteoWizard is an additional requirement for relevant raw-format conversion, not for reading an existing peak table.
Install
The commands use official GitHub sources. Dependency metadata can also reference GitLab and Bioconductor, so those sources must be reachable. Installation accesses the network and may compile packages.
if (!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes")
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
remotes::install_github("tidymass/tidymass")
# Additional packages when your analysis needs them:
remotes::install_github("tidymass/massdatabase")
remotes::install_github("tidymass/massconverter")
# Standalone legacy normalization interface:
# remotes::install_github("jaspershen/MetNormalizer")
library(tidymass)
sessionInfo()
Record versions and diagnose problems
Restart R, load tidymass, and inspect packageVersion(), .libPaths() and sessionInfo(). The umbrella version is not the version of every companion package. Use package::function() to resolve overlapping function names.
Run tidymass::check_tidymass_version(packages = "all") to inspect updates and update_tidymass() only when you intend to upgrade. Mirrors can differ and network failures can leave version information incomplete. Preserve a lockfile/environment record and analysis code before upgrading an active or published project, then validate the new environment.
Begin an analysis
Learn the data structure in massdataset or follow the complete workflow for raw files. Each package chapter gives its scope, an example and its exported API. Run data-dependent examples only after preparing the specified inputs. Example parameters are not a validated universal method.