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rmi-pacta
r2dii.match:Tools to Match Corporate Lending Portfolios with Climate Data
These tools implement in R a fundamental part of the software 'PACTA' (Paris Agreement Capital Transition Assessment), which is a free tool that calculates the alignment between financial portfolios and climate scenarios (<https://www.transitionmonitor.com/>). Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals. This package matches data from corporate lending portfolios to asset level data from market-intelligence databases (e.g. power plant capacities, emission factors, etc.). This is the first step to assess if a financial portfolio aligns with climate goals.
Maintained by Jacob Kastl. Last updated 15 days ago.
7 stars 7.56 score 118 scripts 2 dependents
joshkatz
needs:Attaches and Installs Packages
A simple function for easier package loading and auto-installation.
Maintained by Josh Katz. Last updated 9 years ago.
75 stars 5.99 score 178 scriptsrmi-pacta
pacta.loanbook:Easily Install and Load PACTA for Banks Packages
PACTA (Paris Agreement Capital Transition Assessment) for Banks is a tool that allows banks to calculate the climate alignment of their corporate lending portfolios. This package is designed to make it easy to install and load multiple PACTA for Banks packages in a single step. It also provides thorough documentation - the PACTA for Banks cookbook at <https://rmi-pacta.github.io/pacta.loanbook/articles/cookbook_overview.html> - on how to run a PACTA for Banks analysis. This covers prerequisites for the analysis, the separate steps of running the analysis, the interpretation of PACTA for Banks results, and advanced use cases.
Maintained by Jacob Kastl. Last updated 15 days ago.
1 stars 5.73 score 12 scripts
bioc
Macarron:Prioritization of potentially bioactive metabolic features from epidemiological and environmental metabolomics datasets
Macarron is a workflow for the prioritization of potentially bioactive metabolites from metabolomics experiments. Prioritization integrates strengths of evidences of bioactivity such as covariation with a known metabolite, abundance relative to a known metabolite and association with an environmental or phenotypic indicator of bioactivity. Broadly, the workflow consists of stratified clustering of metabolic spectral features which co-vary in abundance in a condition, transfer of functional annotations, estimation of relative abundance and differential abundance analysis to identify associations between features and phenotype/condition.
Maintained by Sagun Maharjan. Last updated 5 months ago.
sequencingmetabolomicscoveragefunctionalpredictionclustering
4.41 score 13 scripts