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stephenslab
mashr:Multivariate Adaptive Shrinkage
Implements the multivariate adaptive shrinkage (mash) method of Urbut et al (2019) <DOI:10.1038/s41588-018-0268-8> for estimating and testing large numbers of effects in many conditions (or many outcomes). Mash takes an empirical Bayes approach to testing and effect estimation; it estimates patterns of similarity among conditions, then exploits these patterns to improve accuracy of the effect estimates. The core linear algebra is implemented in C++ for fast model fitting and posterior computation.
Maintained by Peter Carbonetto. Last updated 5 months ago.
91 stars 11.04 score 624 scripts 3 dependentsmurrayefford
secr:Spatially Explicit Capture-Recapture
Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection.
Maintained by Murray Efford. Last updated 4 days ago.
3 stars 10.06 score 410 scripts 5 dependents