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cff-version: 1.2.0
message: 'To cite package "ccva" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'ccva: Climate Change Vulnerability Assessment with Constrained Latent Models'
version: 0.1.1
doi: 10.32614/CRAN.package.ccva
abstract: A general framework for climate change vulnerability assessment (CCVA) of
  any species and any dataset. Variables are tagged as exposure, sensitivity or adaptive
  capacity; each component is computed from its own predictors by a chosen method;
  and the three components are combined into vulnerability. Methods range from the
  rule-based framework of Foden et al. (2013) <https://doi.org/10.1371/journal.pone.0065427>
  and the conventional weighted index of Ofori et al. (2017) <https://doi.org/10.1038/s41598-017-13245-y>
  to shape-constrained additive models ('scam'), latent-variable structural equation
  models ('lavaan') in which vulnerability is inferred from observable proxies, and
  penalised regression ('glmnet') for high-dimensional predictors. Missing traits
  and outcomes are handled by full-information maximum likelihood. Ecological knowledge
  enters as explicit constraints on predictors, on each component's role and on the
  links between components. Diagnostics, validation against observed outcomes, bootstrap
  and weight-based uncertainty, calibrated risk predictions and plots for every step
  are included.
authors:
- family-names: Doosti
  given-names: Hassan
  email: hassan.doosti@mq.edu.au
  affiliation: Macquarie University
repository: https://doostih.r-universe.dev
repository-code: https://github.com/DoostiH/ccva
commit: 2101cc5cbcb7898d23a64db3f37cb763a3fb698a
url: https://github.com/DoostiH/ccva
date-released: '2026-10-23'
contact:
- family-names: Doosti
  given-names: Hassan
  email: hassan.doosti@mq.edu.au
  affiliation: Macquarie University