analogs

The analogs package provides fast, flexible tools for climate analog analyses. It efficiently searches large reference datasets to find locations that best match query sites based on climate similarity and geographic proximity.

Core Functions

The package supports a range of analysis types. The core workhorse function is analog_search(), which implements various methods for selecting and summarizing analogs for each query location. Common query types are supported via simplified wrapper functions:

• analog_impact() predicts ecological state variables using analog impact models (AIMs)
• analog_velocity() finds the k nearest geographic analogs under a hard climate constraint
• analog_similarity() finds the k nearest climate analogs within a geographic constraint
  
• analog_availability() counts analogs that meet both climate and distance thresholds
• analog_intensity() computes distance-weighted aggregations of analog properties

Distance metrics

Geographic distance computations support projected coordinates as well as lon-lat coordinates via great circle distances.

Climate similarity is measured using Euclidean or Mahalanobis distance in climate space. Multivariate climate distances are Euclidean by default, but you can use mahalanobis_transform() to implement Mahalanobis distance based on global spatial covariance, or supply x_cov to any analog function to account for site-specific covariance patterns based on historic temporal climate variability.

Computational optimization

The package is designed to maximize performance in various ways:

• The core search architecture is built in optimized C++.
• Parallel processing is available via the n_threads parameter.
• The package uses a lattice search index to efficiently query large candidate pools.
• For repeated queries against the same candidate pool, build_analog_index() can be used to pre-build a reusable search index.
• Optimal index parameters for your specific data can be identified with tune_index_res(), which is used internally by default.
• Memory-safe queries for large raster datasets are available via tiled_analog_search().
• To increase speed at the cost of some precision, you can adaptively downsample your reference data.

Installation

You can install the development version of analogs from GitHub with:

pak::pak("matthewkling/analogs")

Documentation

See the function documentation linked above for detailed usage examples, or browse the full reference documentation.