Help for package ClimaRep

Title:

Estimating Climate Analogue Areas

Version:

1.1

Description:

Offers tools to identify the climate analogues of reference polygons and quantifies their transformation under future climate change scenarios. Approaches described in Mingarro and Lobo (2018) <doi:10.32800/abc.2018.41.0333> and Mingarro and Lobo (2022) <doi:10.1017/S037689292100014X>.

License:

MIT + file LICENSE

Encoding:

UTF-8

RoxygenNote:

7.3.3

Suggests:

testthat (≥ 3.0.0)

Imports:

ggplot2, terra, utils, stats, sf, tidyterra

Config/testthat/edition:

NeedsCompilation:

Packaged:

2026-06-22 11:32:38 UTC; mario

Author:

Mario Mingarro

[aut, cre], Gabriel del Barrio

[aut], Jorge M. Lobo

[aut]

Maintainer:

Mario Mingarro <mario_mingarro@mncn.csic.es>

Repository:

CRAN

Date/Publication:

2026-06-22 18:40:02 UTC

ClimaRep: Estimating Climate Analogue Areas

Description

Offers tools to identify the climate analogues of reference polygons and quantifies their transformation under future climate change scenarios. Approaches described in Mingarro and Lobo (2018) doi:10.32800/abc.2018.41.0333 and Mingarro and Lobo (2022) doi:10.1017/S037689292100014X.

Overview

The primary goal of ClimaRep is to quantify how well the climate within specific polygons (sf) represents the broader climate space defined by climate variables (SpatRaster) within a study area (sf). It also provides functions to evaluate how this representativeness changes under projected future climate conditions.

Key Features

The package includes functions for:

Filtering raster climate variables to reduce multicollinearity (vif_filter).
Estimating current climate representativeness (mh_rep).
Estimating changes in climate representativeness under future climate projections (mh_rep_ch).
Estimating climate representativeness overlay (rep_overlay).

More Details

https://github.com/MarioMingarro/ClimaRep

Author(s)

Maintainer: Mario Mingarro mario_mingarro@mncn.csic.es (ORCID)

Authors:

Gabriel del Barrio (ORCID)
Jorge M. Lobo (ORCID)

Multivariate climate representativeness analysis

Description

This function identifies the climate analogues of an input polygon within a defined area, based on the Mahalanobis distance. The function classifies each cell of the study area as a climate analogue or non-analogue of the reference polygon, according to a similarity threshold. A cell is a climate analogue when its multivariate climate conditions, drawn from the reference climate space (climate_variables), fall within the internal climatic variability (th) of each specific input polygon.

Usage

mh_rep(
  polygon,
  col_name,
  climate_variables,
  study_area,
  th = 0.95,
  dir_output = file.path(tempdir(), "ClimaRep"),
  save_raw = FALSE
)

Arguments

polygon

An sf object containing the defined areas. Its CRS will be used as the reference system.

col_name

character. Name of the column in the polygon object that contains unique identifiers for each polygon.

climate_variables

A SpatRaster stack of climate variables.

study_area

An sf object defining the study area. The analysis will be limited to this area.

th

numeric (0-1). Percentile threshold used to define climate analogues. Cells with a Mahalanobis distance below or equal to the th are classified as analogues (default: 0.95).

dir_output

character. Path to the directory where output files will be saved. The function will create subdirectories within this path.

save_raw

logical. If TRUE, saves the intermediate continuous Mahalanobis distance rasters calculated for each polygon before binary classification. The final binary classification rasters are always saved (default: FALSE).

Details

This function performs a multivariate analysis using Mahalanobis distance to identify the climate analogues of input polygons for a single time period.

Key steps:

Ensures that climate_variables and study_area have matching CRSs with polygon by automatically transforming them if needed. The CRS of polygon will be used as the reference system.
Crops and masks the climate_variables raster to the study_area to limit all subsequent calculations to the area of interest.
Calculate the multivariate covariance matrix using climate data from all cells.
For each polygon in the polygon object:
- Crop and mask the climate variables raster (climate_variables) to the boundary of the current polygon.
- Calculate the multivariate mean using the climate data from the previous step. This defines the climate centroid for the current polygon.
- Calculate the Mahalanobis distance for each cell relative to the centroid and covariance matrix.
- Apply the specified threshold (th) to Mahalanobis distances to determine which cells are considered climate analogues This threshold is a percentile of the Mahalanobis distances within the current polygon.
- Classify each cell as analogue = 1 (Mahalanobis distance \le th) or non-analogue = 0 (Mahalanobis distance $>$ th).
Saves the binary classification raster (.tif) and generates a corresponding visualization map (.jpeg) for each polygon. These are saved within the specified output directory (dir_output).

It is important to note that Mahalanobis distance is sensitive to collinearity among variables. While the covariance matrix accounts for correlations, it is strongly recommended that the climate_variables are not strongly correlated. Consider performing a collinearity analysis beforehand, perhaps using the vif_filter() function from this package.

Value

Writes the following outputs to disk within subdirectories of dir_output:

Classification (.tif ) rasters: Binary rasters (0 for non-analogue and1 for analogue) for each input polygon are saved in the ⁠Analogues/⁠ subdirectory.
Visualization (.jpeg) maps: Image files visualizing the classification results for each polygon are saved in the ⁠Charts/⁠ subdirectory.
Raw Mahalanobis distance rasters: Optionally saved as .tif files in the ⁠Mh_Raw/⁠ subdirectory if save_raw = TRUE.

Examples

library(terra)
library(sf)
set.seed(2458)
n_cells <- 100 * 100
r_clim_present <- terra::rast(ncols = 100, nrows = 100, nlyrs = 7)
values(r_clim_present) <- c(
   (terra::rowFromCell(r_clim_present, 1:n_cells) * 0.2 + rnorm(n_cells, 0, 3)),
   (terra::rowFromCell(r_clim_present, 1:n_cells) * 0.9 + rnorm(n_cells, 0, 0.2)),
   (terra::colFromCell(r_clim_present, 1:n_cells) * 0.15 + rnorm(n_cells, 0, 2.5)),
   (terra::colFromCell(r_clim_present, 1:n_cells) +
     (terra::rowFromCell(r_clim_present, 1:n_cells)) * 0.1 + rnorm(n_cells, 0, 4)),
   (terra::colFromCell(r_clim_present, 1:n_cells) /
     (terra::rowFromCell(r_clim_present, 1:n_cells)) * 0.1 + rnorm(n_cells, 0, 4)),
   (terra::colFromCell(r_clim_present, 1:n_cells) *
     (terra::rowFromCell(r_clim_present, 1:n_cells) + 0.1 + rnorm(n_cells, 0, 4))),
   (terra::colFromCell(r_clim_present, 1:n_cells) *
     (terra::colFromCell(r_clim_present, 1:n_cells) + 0.1 + rnorm(n_cells, 0, 4)))
)
names(r_clim_present) <- c("varA", "varB", "varC", "varD", "varE", "varF", "varG")
terra::crs(r_clim_present) <- "EPSG:4326"

vif_result <- ClimaRep::vif_filter(r_clim_present, th = 5)
print(vif_result$summary)
r_clim_present_filtered <- vif_result$filtered_raster
hex_grid <- sf::st_sf(
   sf::st_make_grid(
     sf::st_as_sf(
       terra::as.polygons(
         terra::ext(r_clim_present_filtered))),
     square = FALSE)
     )
sf::st_crs(hex_grid) <- "EPSG:4326"
polygons <- hex_grid[sample(nrow(hex_grid), 2), ]
polygons$name <- c("Pol_A", "Pol_B")
study_area_polygon <- sf::st_as_sf(terra::as.polygons(terra::ext(r_clim_present_filtered)))
sf::st_crs(study_area_polygon) <- "EPSG:4326"
terra::plot(r_clim_present_filtered[[1]])
terra::plot(polygons, add = TRUE, color = "transparent", lwd = 3)
terra::plot(study_area_polygon, add = TRUE, col = "transparent", lwd = 3, border = "red")

ClimaRep::mh_rep(
   polygon = polygons,
   col_name = "name",
   climate_variables = r_clim_present_filtered,
   study_area = study_area_polygon,
   th = 0.95,
   dir_output = file.path(tempdir(), "ClimaRep"),
   save_raw = TRUE
   )

Multivariate temporal climate analogous change analysis

Description

This function identifies how the climate analogues of an input polygon change across two time periods (present and future) within a defined area, based on the Mahalanobis distance. This characterises the climate resilience of the polygon: the behaviour of its analogues under a climate-change scenario.

The function classifies each cell as Stable, Lost, or Novel depending on whether it is a climate analogue of the polygon in the present period, the future period, or both.

A cell is a climate analogue when its multivariate climate conditions, drawn from the reference climate space (present_climate_variables and future_climate_variables), fall within the internal variability (th) of each specific polygon.

Usage

mh_rep_ch(
  polygon,
  col_name,
  present_climate_variables,
  future_climate_variables,
  study_area,
  th = 0.95,
  model,
  year,
  dir_output = file.path(tempdir(), "ClimaRep"),
  save_raw = FALSE,
  cov_type = c("present", "pooled")
)

Arguments

polygon

An sf object containing the defined areas. Its CRS will be used as the reference system.

col_name

character. Name of the column in the polygon object that contains unique identifiers for each polygon.

present_climate_variables

A SpatRaster stack of climate variables representing current conditions.

future_climate_variables

A SpatRaster stack containing the same climate variables as present_climate_variables but representing future projected conditions.

study_area

A single sf polygon.

th

numeric (0-1). Percentile threshold used to define climate analogues Cells with a Mahalanobis distance below or equal to the th are classified as analogues (default: 0.95).

model

character. Name or identifier of the climate model used (e.g., "MIROC6"). This parameter is used in output filenames and subdirectory names, allowing for better file management.

year

character. Year or period of future climate data (e.g., "2070"). This parameter is used in output filenames and subdirectory names, allowing for better file management.

dir_output

character. Path to the directory where output files will be saved. The function will create subdirectories within this path.

save_raw

cov_type

character. Reference covariance used for the Mahalanobis metric, applied to both periods so distances are comparable under a single threshold. "present" (default) uses the present climate covariance only: the present footprint (Stable + Lost) is then identical to that of mh_rep(), and the future is projected into a fixed present reference space. "pooled" uses the combined present + future covariance: more lenient to extrapolation, but the reference space (and hence the present footprint) depends on the future scenario.

Details

This function extends the approach used in mh_rep to assess changes in the climate analogues of a polygon over time. While mh_rep() identifies analogues in a single scenario, mh_rep_ch() adapts this by using the centroid (mean) of the present polygon together with a single reference covariance matrix applied to both periods, so that present and future Mahalanobis distances are comparable under one threshold. The reference covariance is selected with cov_type: "present" (default) uses the present-climate covariance only, which makes the present footprint (Stable + Lost) identical to that of mh_rep(); "pooled" uses the combined present + future covariance, which is more lenient to extrapolation but makes the reference space depend on the future scenario. Here are the key steps:

Checking CRS: Ensures that future_climate_variables, climate_variables, and study_area have matching CRSs with polygon by automatically transforming them if needed. The CRS of polygon will be used as the reference system.
Crops and masks the climate_variables and future_climate_variables raster to the study_area to limit all subsequent calculations to the area of interest.
Calculate a single multivariate covariance matrix used as the metric for Mahalanobis distance in both periods, ensuring that present and future distances are directly comparable under the same threshold. By default (cov_type = "present") this is the covariance of the present climate; with cov_type = "pooled" it is the covariance of present and future cells combined.
For each polygon in the polygon object:
- Crop and mask the present climate variables raster (present_climate_variables) to the boundary of the current polygon.
- Calculate the multivariate mean using the climate data from the previous step. This defines the climate centroid for the current polygon. Calculate the Mahalanobis distance for each cell relative to the centroid using the reference covariance matrix selected by cov_type. This results in a Mahalanobis distance raster for the present period and another for the future period.
- Apply the specified threshold (th) to Mahalanobis distances to determine which cells are considered analogue. This threshold is a percentile of the Mahalanobis distances within the current polygon.
- Classify each cells, for both present and future periods, as analogue = 1 (Mahalanobis distance \le th) or non-analogue = 0 (Mahalanobis distance $>$ th).
Compares the binary analogues of each cell between the present and future periods and determines cells where conditions are:
- 0: Non-analogue: Cells that are outside the defined Mahalanobis threshold in both present and future periods.
- 1: Stable: Cells that are a climate analogue of the polygon in both present and future periods.
- 2: Lost: Cells that are a climate analogue in the present period but not in the future period.
- 3: Novel: Cells that are not a climate analogue in the present period but are one in the future period.
Saves the classification raster (.tif) and generates a corresponding visualization map (.jpeg) for each polygon. These are saved within the specified output directory (dir_output). All files are saved using the model and year parameters for better file management.

It is important to note that Mahalanobis distance assumes is sensitive to collinearity among variables. While the covariance matrix accounts for correlations, it is strongly recommended that the climate variables (present_climate_variables) are not strongly correlated. Consider performing a collinearity analysis beforehand, perhaps using the vif_filter() function from this package.

Value

Writes the following outputs to disk within subdirectories of dir_output:

Classification (.tif) change rasters: Change category rasters (0 for Non-analogue, 1 for Stable, 2 for Lost and 3 for Novel) for each input polygon are saved in the ⁠Change/⁠ subdirectory.
Visualization (.jpeg) maps: Image files visualizing the change classification results for each polygon are saved in the ⁠Charts/⁠ subdirectory.
Raw Mahalanobis distance rasters: Optionally, they are saved as .tif files in the ⁠Mh_Raw_Pre/⁠ and ⁠Mh_Raw_Fut/⁠ subdirectories if save_raw = TRUE.

Examples

library(terra)
library(sf)
set.seed(2458)
n_cells <- 100 * 100
r_clim_present <- terra::rast(ncols = 100, nrows = 100, nlyrs = 7)
values(r_clim_present) <- c(
   (terra::rowFromCell(r_clim_present, 1:n_cells) * 0.2 + rnorm(n_cells, 0, 3)),
   (terra::rowFromCell(r_clim_present, 1:n_cells) * 0.9 + rnorm(n_cells, 0, 0.2)),
   (terra::colFromCell(r_clim_present, 1:n_cells) * 0.15 + rnorm(n_cells, 0, 2.5)),
   (terra::colFromCell(r_clim_present, 1:n_cells) +
     (terra::rowFromCell(r_clim_present, 1:n_cells)) * 0.1 + rnorm(n_cells, 0, 4)),
   (terra::colFromCell(r_clim_present, 1:n_cells) /
     (terra::rowFromCell(r_clim_present, 1:n_cells)) * 0.1 + rnorm(n_cells, 0, 4)),
   (terra::colFromCell(r_clim_present, 1:n_cells) *
     (terra::rowFromCell(r_clim_present, 1:n_cells) + 0.1 + rnorm(n_cells, 0, 4))),
   (terra::colFromCell(r_clim_present, 1:n_cells) *
     (terra::colFromCell(r_clim_present, 1:n_cells) + 0.1 + rnorm(n_cells, 0, 4)))
)
names(r_clim_present) <- c("varA", "varB", "varC", "varD", "varE", "varF", "varG")
terra::crs(r_clim_present) <- "EPSG:4326"

vif_result <- ClimaRep::vif_filter(r_clim_present, th = 5)
print(vif_result$summary)
r_clim_present_filtered <- vif_result$filtered_raster
r_clim_future <- r_clim_present_filtered + 2
names(r_clim_future) <- names(r_clim_present_filtered)
hex_grid <- sf::st_sf(
   sf::st_make_grid(
     sf::st_as_sf(
       terra::as.polygons(
         terra::ext(r_clim_present_filtered))),
     square = FALSE))
sf::st_crs(hex_grid) <- "EPSG:4326"
polygons <- hex_grid[sample(nrow(hex_grid), 2), ]
polygons$name <- c("Pol_1", "Pol_2")
study_area_polygon <- sf::st_as_sf(terra::as.polygons(terra::ext(r_clim_present_filtered)))
sf::st_crs(study_area_polygon) <- "EPSG:4326"
terra::plot(r_clim_present_filtered[[1]])
terra::plot(polygons, add = TRUE, color = "transparent", lwd = 3)
terra::plot(study_area_polygon, add = TRUE, col = "transparent", lwd = 3, border = "red")

ClimaRep::mh_rep_ch(
   polygon = polygons,
   col_name = "name",
   present_climate_variables = r_clim_present_filtered,
   future_climate_variables = r_clim_future,
   study_area = study_area_polygon,
   th = 0.95,
   model = "ExampleModel",
   year = "2070",
   dir_output = file.path(tempdir(), "ClimaRepChange"),
   save_raw = TRUE,
   cov_type = "present")

Overlay ClimaRep classifications into cumulative counts (climate redundancy)

Description

Combines multiple single-layer rasters (tif), outputs from ClimaRep::mh_rep() or ClimaRep::mh_rep_ch() for different input polygons, into a multi-layered SpatRaster, depending on the type of input.

This function handles inputs from both ClimaRep::mh_rep() (which primarily contains Analogue cells) and ClimaRep::mh_rep_ch() (which includes Stable, Lost, and Novel cells). The output layers consistently represent counts of each input. When mh_rep() outputs are aggregated, the resulting single-layer count is the climate redundancy: the number of reference polygons for which each cell is a climate analogue.

change = FALSE (default): inputs are treated as outputs of ClimaRep::mh_rep(). Only Analogue cells (value 1) are counted, producing a single-layer cumulative count raster (climate redundancy).
change = TRUE: inputs are treated as outputs of ClimaRep::mh_rep_ch(). Lost (2), Stable (1) and Novel (3) cells are counted separately, producing a three-layer SpatRaster ready for RGB plotting.

Usage

rep_overlay(
  folder_path,
  output_dir = file.path(tempdir(), "ClimaRep_overlay"),
  change = FALSE
)

Arguments

folder_path

character. The path to the directory containing the classification rasters (.tif) generated by ClimaRep::mh_rep() or ClimaRep::mh_rep_ch(). These rasters should primarily contain the categories: 1 (Stable/Analogue), 2 (Lost), and 3 (Novel). Category 0 (Non-analogue) will be ignored for the RGB output.

output_dir

character. Path to the directory where the output file will be saved.

change

logical. If TRUE, processes inputs as outputs from ClimaRep::mh_rep_ch() (Lost/Stable/Novel three-band RGB output). If FALSE (default), processes inputs as outputs from ClimaRep::mh_rep().

Details

This function aggregates the per-polygon binary or categorical rasters produced by mh_rep or mh_rep_ch into cumulative count layers across all input polygons. It is designed as a post-processing step to summarise where, and how often, a given category occurs in space when many polygons have been analysed.

The behaviour is controlled by the change argument:

change = FALSE (default): inputs are treated as outputs of ClimaRep::mh_rep(). For each input raster, a binary layer is created where cells with value 1 (Analogue) are set to 1 and all other values to 0. These layers are summed across all rasters to produce a single-layer cumulative count of how many polygons classify each cell as a climate analogue (climate redundancy).
change = TRUE: inputs are treated as outputs of ClimaRep::mh_rep_ch(). Three independent cumulative count layers are produced, one for each change category: cells coded 2 (Lost), 1 (Stable), and 3 (Novel). Cells coded 0 (non-analogue) are ignored. The three count layers are stacked in the fixed order Lost (Red), Stable (Green), Novel (Blue), so the resulting SpatRaster is ready for direct RGB visualization with terra::plotRGB(), where colour mixtures intuitively reflect the spatial agreement among change types.

Output structure within output_dir:

ClimaRep_overlay.tif: the final multi-layer (or single-layer) SpatRaster of cumulative counts.
⁠individual_bands/⁠: only created when change = TRUE. Contains one .tif per RGB channel lost_count_R.tif, stable_count_G.tif, novel_count_B.tif for users who prefer to handle each band separately.

Value

Writes the following outputs within the directory specified by output_dir: When ClimaRep::mh_rep() results are used, the output layers consistently represent counts for Analogue categories across all input rasters. When ClimaRep::mh_rep_ch() results are used, the output layers consistently represent counts for Lost (Red), Stable (Green), and Novel (Blue) categories across all input rasters. Designed for direct RGB plotting.

A multi-layered SpatRaster (ClimaRep_overlay.tif) for RGB visualization.
Individual .tif files for each band (Lost, Stable, Novel) in ⁠individual_bands/⁠ subdirectory.
⁠Additional note⁠: The ClimaRep::mh_rep() function analyzes a single period. When its output is used, analogues cells are all categorized as Stable (value 1), while other categories (Lost and Novel) have a value of zero.

Examples

ClimaRep_overlay <- ClimaRep::rep_overlay(folder_path = system.file("extdata",
                                                                    package = "ClimaRep"),
                                         output_dir = file.path(tempdir(), "rep_overlay_output"),
                                         change = FALSE)
terra::plot(ClimaRep_overlay)

Filter SpatRaster layers based on Variance Inflation Factor (VIF)

Description

This function iteratively filters layers from a SpatRaster object by removing the one with the highest Variance Inflation Factor (VIF) that exceeds a specified threshold (th).

Usage

vif_filter(x, th = 5)

Arguments

x

A SpatRaster object containing the layers (variables) to filter. Must contain two or more layers.

th

A numeric value specifying the Variance Inflation Factor (VIF) threshold. Layers whose VIF exceeds this threshold are candidates for removal in each iteration (default: 5).

Details

This function implements a common iterative procedure to reduce multicollinearity among raster layers by removing variables with a high Variance Inflation Factor (VIF). The VIF for a specific predictor indicates how much the variance of its estimated coefficient is inflated due to its linear relationships with all other predictors in the model. A high VIF value suggests a high degree of collinearity with other predictors (values exceeding 5 or 10 are often considered problematic; see O'Brien, 2007; Legendre & Legendre, 2012).

The filtering process is fully automated and robust:

Validates the input and converts the SpatRaster to a data.frame for calculations.
In each step, the function attempts to calculate VIF efficiently using matrix inversion. If perfect collinearity is detected (resulting in a singular matrix that cannot be inverted), the function automatically switches to a more robust method based on linear regressions to handle the situation without an error.
The function identifies the variable with the highest VIF among the remaining variables. If its VIF is greater than the threshold (th), that variable is removed. The process repeats until all remaining variables are below the threshold or until only one variable remains.

The output is a list containing two main components:

SpatRaster object with the variables that were retained after the filtering process.
A list with a detailed summary of the process, including the names of the kept and excluded variables, the original Pearson's correlation matrix, and the final VIF values for the retained variables.

The internal VIF calculation includes checks to handle potential numerical instability, such as columns with zero or near-zero variance and cases of perfect collinearity among variables, which could otherwise lead to errors (e.g., infinite VIFs). Variables identified as having infinite VIF due to perfect collinearity are prioritized for removal.

References: O’Brien (2007) A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673–690. https://doi.org/10.1007/s11135-006-9018-6 Legendre and Legendre (2012) Numerical Ecology (3a ed.). Elsevier, Netherlands.

Value

A list object containing the filtered SpatRaster and a summary of the filtering process.

Examples

library(terra)

set.seed(2458)
n_cells <- 100 * 100
r_clim <- terra::rast(ncols = 100, nrows = 100, nlyrs = 7)
values(r_clim) <- c(
  (rowFromCell(r_clim, 1:n_cells) * 0.2 + rnorm(n_cells, 0, 3)),
  (rowFromCell(r_clim, 1:n_cells) * 0.9 + rnorm(n_cells, 0, 0.2)),
  (colFromCell(r_clim, 1:n_cells) * 0.15 + rnorm(n_cells, 0, 2.5)),
  (colFromCell(r_clim, 1:n_cells) +
    (rowFromCell(r_clim, 1:n_cells)) * 0.1 + rnorm(n_cells, 0, 4)),
  (colFromCell(r_clim, 1:n_cells) /
    (rowFromCell(r_clim, 1:n_cells)) * 0.1 + rnorm(n_cells, 0, 4)),
  (colFromCell(r_clim, 1:n_cells) *
    (rowFromCell(r_clim, 1:n_cells) + 0.1 + rnorm(n_cells, 0, 4))),
  (colFromCell(r_clim, 1:n_cells) *
    (colFromCell(r_clim, 1:n_cells) + 0.1 + rnorm(n_cells, 0, 4))))
names(r_clim) <- c("varA", "varB", "varC", "varD", "varE", "varF", "varG")
terra::crs(r_clim) <- "EPSG:4326"
terra::plot(r_clim)

vif_result <- ClimaRep::vif_filter(r_clim, th = 5)
print(vif_result$summary)
r_clim_filtered <- vif_result$filtered_raster
terra::plot(r_clim_filtered)

Package {ClimaRep}

ClimaRep: Estimating Climate Analogue Areas

Description

Overview

Key Features

More Details

Author(s)

Multivariate climate representativeness analysis

Description

Usage

Arguments

Details

Value

Examples

Multivariate temporal climate analogous change analysis

Description

Usage

Arguments

Details

Value

Examples

Overlay ClimaRep classifications into cumulative counts (climate redundancy)

Description

Usage

Arguments

Details

Value

Examples

Filter SpatRaster layers based on Variance Inflation Factor (VIF)

Description

Usage

Arguments

Details

Value

Examples