Type:	Package
Title:	Scalable and Flexible Derivation of Custom-Time Bioclimatic and Environmental Summary Variables
Version:	0.4.1
Maintainer:	Gonzalo E. Pinilla-Buitrago <gepinillab@gmail.com>
Description:	Provides an efficient tool for creating custom-time bioclimatic and derived environmental summary variables from user-supplied raster data for user-defined timeframes. The package overcomes computational bottlenecks by automatically switching between an in-memory framework using the 'terra' package to maximize speed for smaller datasets, and an on-disk tiling framework for rasters that exceed available RAM, leveraging 'exactextractr' and 'Rfast' to process data in chunks. The core functions, derive_bioclim() and derive_statistics(), offer a unified interface with flexibility for custom time periods beyond standard quarters and the use of fixed temporal indices, facilitating the creation of temporally-matched environmental variables for ecological and biogeographical modeling. Visit the package website https://gepinillab.github.io/fastbioclim/ to find tutorials in English and Spanish.
License:	GPL (≥ 3)
URL:	https://gepinillab.github.io/fastbioclim/
BugReports:	https://github.com/gepinillab/fastbioclim/issues
Encoding:	UTF-8
RoxygenNote:	7.3.2
Depends:	R (≥ 4.1)
Imports:	exactextractr, future.apply, glue, progressr, purrr, qs2, Rfast, sf, terra (≥ 1.7-0)
Suggests:	mockery, testthat (≥ 3.0.0), withr
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2026-03-09 21:35:32 UTC; Gonzalo
Author:	Gonzalo E. Pinilla-Buitrago [aut, cre], Luis Osorio-Olvera [aut]
Repository:	CRAN
Date/Publication:	2026-03-10 09:40:26 UTC

Tiled, Out-of-Core Time Series Aggregation (Internal)

Description

Tiled, Out-of-Core Time Series Aggregation (Internal)

Usage

aggregate_fast(
  paths,
  index,
  output_names,
  user_region,
  tile_degrees,
  output_dir,
  verbose,
  aggregation_type = "mean"
)

In-Memory Time Series Aggregation (Internal)

Description

In-Memory Time Series Aggregation (Internal)

Usage

aggregate_terra(
  x,
  index,
  output_names,
  output_dir,
  gdal_opt,
  overwrite,
  verbose,
  aggregation_type = "mean"
)

bio01_fast: Mean Temperature

Description

Calculates mean temperature across all temporal units (usually 12 months).

Usage

bio01_fast(tavg, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio01" (the calculated mean) and "cell" (the IDs).

bio01_terra: Mean Temperature

Description

Calculates mean temperature across all temporal units (layers).

Usage

bio01_terra(tavg)

Arguments

Value

A single-layer 'SpatRaster' with the calculated mean temperature, named "bio01".

bio02_fast: Mean Diurnal Range

Description

Calculates the mean of the diurnal temperature ranges (tmax - tmin) across all temporal units.

Usage

bio02_fast(tmin, tmax, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio02" (mean diurnal range) and "cell".

bio02_terra: Mean Diurnal Range

Description

Calculates the mean of the diurnal temperature range (tmax - tmin) across all temporal units (layers).

Usage

bio02_terra(tmin, tmax)

Arguments

Value

A single-layer 'SpatRaster' with the mean diurnal range, named "bio02".

bio03_fast: Isothermality

Description

Calculates Isothermality representing the ratio of mean diurnal range to temperature range: (bio02 / bio07) * 100.

Usage

bio03_fast(bio02V, bio07V, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio03" and "cell".

bio03_terra: Isothermality

Description

Calculates Isothermality, defined as (bio02 / bio07) * 100.

Usage

bio03_terra(bio02, bio07)

Arguments

Value

A single-layer 'SpatRaster' with the calculated isothermality, named "bio03".

bio04_fast: Temperature Seasonality (Std Dev * 100)

Description

Calculates Temperature Seasonality, defined as the standard deviation of average temperatures across all temporal units (e.g., 12 months), multiplied by 100.

Usage

bio04_fast(tavg, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio04" (seasonality) and "cell".

bio04_terra: Temperature Seasonality (Std Dev * 100)

Description

Calculates the standard deviation of average temperatures across all layers, multiplied by 100.

Usage

bio04_terra(tavg)

Arguments

Value

A single-layer 'SpatRaster' with the temperature seasonality, named "bio04".

bio05_fast: Max Temperature of Warmest Unit

Description

Identifies the maximum temperature of the warmest temporal unit. If 'index_vector' is 'NULL', it calculates the row-wise maximum. If 'index_vector' is provided, it extracts the value from the specific column index for each row.

Usage

bio05_fast(tmax, cell, index_vector = NULL)

Arguments

Details

This function calculates bio05 following the standard definition used by WorldClim (Hijmans et al., 2005) and ANUCLIM 6.1 (Xu & Hutchinson, 2013), which is the single highest value from all maximum temperature layers (a "max of maxes"). It does not use mean temperature to first identify the warmest month.

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio05" (the maximum temperature) and "cell".

References

Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. and Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25(15), 1965-1978.

O'Donnell, M. S., & Ignizio, D. A. (2012). Bioclimatic predictors for supporting ecological applications in the conterminous United States. U.S. Geological Survey Data Series 691.

Xu, T., & Hutchinson, M. F. (2013). New developments and applications in the ANUCLIM spatial climatic and bioclimatic modelling package. Environmental Modelling & Software, 40, 267-279.

bio05_terra: Max Temperature of Warmest Unit

Description

Identifies the maximum temperature of the warmest temporal unit (layer).

Usage

bio05_terra(tmax, warmest_unit = NULL)

Arguments

Details

This function calculates bio05 following the standard definition used by WorldClim (Hijmans et al., 2005) and ANUCLIM 6.1 (Xu & Hutchinson, 2013), which is the single highest value from all maximum temperature layers (a "max of maxes"). It does not use mean temperature to first identify the warmest month.

Value

A single-layer 'SpatRaster' with the maximum temperature, named "bio05".

References

Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. and Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25(15), 1965-1978.

O'Donnell, M. S., & Ignizio, D. A. (2012). Bioclimatic predictors for supporting ecological applications in the conterminous United States. U.S. Geological Survey Data Series 691.

Xu, T., & Hutchinson, M. F. (2013). New developments and applications in the ANUCLIM spatial climatic and bioclimatic modelling package. Environmental Modelling & Software, 40, 267-279.

bio06_fast: Min Temperature of Coldest Unit

Description

Identifies the minimum temperature of the coldest temporal unit. If 'index_vector' is 'NULL', it calculates the row-wise minimum. If 'index_vector' is provided, it extracts the value from the specific column index for each row.

Usage

bio06_fast(tmin, cell, index_vector = NULL)

Arguments

Details

This function calculates bio06 following the standard definition used by WorldClim (Hijmans et al., 2005) and ANUCLIM 6.1 (Xu & Hutchinson, 2013), which is the single lowest value from all minimum temperature layers (a "min of mins"). It does not use mean temperature to first identify the coldest month.

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio06" (the minimum temperature) and "cell".

References

Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. and Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25(15), 1965-1978.

O'Donnell, M. S., & Ignizio, D. A. (2012). Bioclimatic predictors for supporting ecological applications in the conterminous United States. U.S. Geological Survey Data Series 691.

Xu, T., & Hutchinson, M. F. (2013). New developments and applications in the ANUCLIM spatial climatic and bioclimatic modelling package. Environmental Modelling & Software, 40, 267-279.

bio06_terra: Min Temperature of Coldest Unit

Description

Identifies the minimum temperature of the coldest temporal unit (layer).

Usage

bio06_terra(tmin, coldest_unit = NULL)

Arguments

Details

This function calculates bio06 following the standard definition used by WorldClim (Hijmans et al., 2005; O'Donnell & Ignizio, 2012) and ANUCLIM 6.1 (Xu & Hutchinson, 2013), which is the single lowest value from all minimum temperature layers (a "min of mins"). It does not use mean temperature to first identify the coldest month.

Value

A single-layer 'SpatRaster' with the minimum temperature, named "bio06".

References

Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. and Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25(15), 1965-1978.

O'Donnell, M. S., & Ignizio, D. A. (2012). Bioclimatic predictors for supporting ecological applications in the conterminous United States. U.S. Geological Survey Data Series 691.

Xu, T., & Hutchinson, M. F. (2013). New developments and applications in the ANUCLIM spatial climatic and bioclimatic modelling package. Environmental Modelling & Software, 40, 267-279.

bio07_fast: Temperature Range (bio05 - bio06)

Description

Calculates the Temperature Range, defined as the difference between the Maximum Temperature of the Warmest Unit (bio05) and the Minimum Temperature of the Coldest Unit (bio06).

Usage

bio07_fast(bio05V, bio06V, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio07" (annual range) and "cell".

bio07_terra: Temperature Range (bio05 - bio06)

Description

Calculates the difference between the Maximum Temperature of the Warmest Unit (bio05) and the Minimum Temperature of the Coldest Unit (bio06).

Usage

bio07_terra(bio05, bio06)

Arguments

Value

A single-layer 'SpatRaster' with the temperature annual range, named "bio07".

bio08_fast: Mean Temperature of Wettest Period

Description

Calculates the mean temperature of the specific rolling period identified as the wettest (highest precipitation).

Usage

bio08_fast(tperiod, pperiod_max_idx, period_length, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio08" (mean temperature of wettest period) and "cell".

bio08_terra: Mean Temperature of Wettest Period

Description

Calculates the mean temperature of the specific rolling period identified as the wettest.

Usage

bio08_terra(tmp, wettest_period)

Arguments

Value

A single-layer 'SpatRaster' containing the mean temperature of the wettest period, named "bio08".

bio09_fast: Mean Temperature of Driest Period

Description

Calculates the mean temperature of the specific rolling period identified as the driest (lowest precipitation).

Usage

bio09_fast(tperiod, pperiod_min_idx, period_length, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio09" (mean temperature of driest period) and "cell".

bio09_terra: Mean Temperature of Driest Period

Description

Calculates the mean temperature of the specific rolling period identified as the driest.

Usage

bio09_terra(tmp, driest_period)

Arguments

Value

A single-layer 'SpatRaster' containing the mean temperature of the driest period, named "bio09".

bio10_fast: Mean Temperature of Warmest Period

Description

Calculates the mean temperature of the specific rolling period identified as the warmest (highest temperature).

Usage

bio10_fast(tperiod, tperiod_max_idx, period_length, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio10" (mean temperature of warmest period) and "cell".

bio10_terra: Mean Temperature of Warmest Period

Description

Calculates the mean temperature of the specific rolling period identified as the warmest.

Usage

bio10_terra(tmp, warmest_period)

Arguments

Value

A single-layer 'SpatRaster' containing the mean temperature of the warmest period, named "bio10".

bio11_fast: Mean Temperature of Coldest Period

Description

Calculates the mean temperature of the specific rolling period identified as the coldest (lowest temperature).

Usage

bio11_fast(tperiod, tperiod_min_idx, period_length, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio11" (mean temperature of coldest period) and "cell".

bio11_terra: Mean Temperature of Coldest Period

Description

Calculates the mean temperature of the specific rolling period identified as the coldest.

Usage

bio11_terra(tmp, coldest_period)

Arguments

Value

A single-layer 'SpatRaster' containing the mean temperature of the coldest period, named "bio11".

bio12_fast: Total Precipitation

Description

Calculates the total precipitation (sum) across all temporal units (e.g., 12 months).

Usage

bio12_fast(prcp, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio12" (total precipitation) and "cell".

bio12_terra: Total Precipitation

Description

Calculates the total precipitation (sum) across all temporal units (layers).

Usage

bio12_terra(prcp)

Arguments

Value

A single-layer 'SpatRaster' with the total precipitation, named "bio12".

bio13_fast: Precipitation of Wettest Unit

Description

Identifies the precipitation of the wettest temporal unit (e.g., month). If 'index_vector' is 'NULL', it calculates the row-wise maximum. If 'index_vector' is provided, it extracts the value from the specific column index for each row.

Usage

bio13_fast(prcp, cell, index_vector = NULL)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio13" (precipitation of wettest unit) and "cell".

bio13_terra: Precipitation of Wettest Unit

Description

Identifies the precipitation of the wettest temporal unit (layer).

Usage

bio13_terra(prcp, wettest_unit = NULL)

Arguments

Value

A single-layer 'SpatRaster' with the precipitation of the wettest unit, named "bio13".

bio14_fast: Precipitation of Driest Unit

Description

Identifies the precipitation of the driest temporal unit (e.g., month). If 'index_vector' is 'NULL', it calculates the row-wise minimum. If 'index_vector' is provided, it extracts the value from the specific column index for each row.

Usage

bio14_fast(prcp, cell, index_vector = NULL)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio14" (precipitation of driest unit) and "cell".

bio14_terra: Precipitation of Driest Unit

Description

Identifies the precipitation of the driest temporal unit (layer).

Usage

bio14_terra(prcp, driest_unit = NULL)

Arguments

Value

A single-layer 'SpatRaster' with the precipitation of the driest unit, named "bio14".

bio15_fast: Precipitation Seasonality (CV)

Description

Calculates the Coefficient of Variation (CV) of precipitation. The formula used is: '(StandardDeviation / (Mean + 1)) * 100'. (The "+1" is added to the mean to avoid division by zero in completely arid areas).

Usage

bio15_fast(prcp, bio12V, n_units, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio15" (Precipitation Seasonality) and "cell".

bio15_terra: Precipitation Seasonality (CV)

Description

Calculates the Coefficient of Variation (CV) of precipitation across all layers.

Usage

bio15_terra(prcp)

Arguments

Value

A single-layer 'SpatRaster' with the precipitation seasonality, named "bio15".

Note

The formula adds 1 to the mean to avoid division by zero in arid areas.

bio16_fast: Precipitation of Wettest Period

Description

Calculates the total precipitation of the specific rolling period identified as the wettest (highest precipitation).

Usage

bio16_fast(pperiod, pperiod_max_idx, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio16" (precipitation of wettest period) and "cell".

bio16_terra: Precipitation of Wettest Period

Description

Calculates the total precipitation of the specific rolling period identified as the wettest.

Usage

bio16_terra(wet, wettest_period)

Arguments

Value

A single-layer 'SpatRaster' with the precipitation of the wettest period, named "bio16".

bio17_fast: Precipitation of Driest Period

Description

Calculates the total precipitation of the specific rolling period identified as the driest (lowest precipitation).

Usage

bio17_fast(pperiod, pperiod_min_idx, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio17" (precipitation of driest period) and "cell".

bio17_terra: Precipitation of Driest Period

Description

Calculates the total precipitation of the specific rolling period identified as the driest.

Usage

bio17_terra(wet, driest_period)

Arguments

Value

A single-layer 'SpatRaster' with the precipitation of the driest period, named "bio17".

bio18_fast: Precipitation of Warmest Period

Description

Calculates the total precipitation of the specific rolling period identified as the warmest (highest temperature).

Usage

bio18_fast(pperiod, tperiod_max_idx, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio18" (precipitation of warmest period) and "cell".

bio18_terra: Precipitation of Warmest Period

Description

Calculates the total precipitation of the specific rolling period identified as the warmest.

Usage

bio18_terra(wet, warmest_period)

Arguments

Value

A single-layer 'SpatRaster' with the precipitation of the warmest period, named "bio18".

bio19_fast: Precipitation of Coldest Period

Description

Calculates the total precipitation of the specific rolling period identified as the coldest (lowest temperature).

Usage

bio19_fast(pperiod, tperiod_min_idx, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio19" (precipitation of coldest period) and "cell".

bio19_terra: Precipitation of Coldest Period

Description

Calculates the total precipitation of the specific rolling period identified as the coldest.

Usage

bio19_terra(wet, coldest_period)

Arguments

Value

A single-layer 'SpatRaster' with the precipitation of the coldest period, named "bio19".

bio20_fast: Mean Radiation

Description

Calculates mean solar radiation across all temporal units (usually 12 months).

Usage

bio20_fast(srad, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio20" (the calculated mean) and "cell" (the IDs).

bio20_terra: Mean Radiation

Description

Calculates mean solar radiation across all temporal units (layers).

Usage

bio20_terra(srad)

Arguments

Value

A single-layer 'SpatRaster' with the mean solar radiation, named "bio20".

#' @title bio21_fast: Highest Radiation Unit

Description

Identifies the highest solar radiation of the temporal unit with the highest value. If 'index_vector' is 'NULL', it calculates the row-wise maximum. If 'index_vector' is provided, it extracts the value from the specific column index for each row.

Usage

bio21_fast(srad, cell, index_vector = NULL)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio21" (the maximum solar radiation) and "cell".

bio21_terra: Highest Moisture Unit

Description

Identifies the highest solar radiation of the unit with the highest value.

Usage

bio21_terra(srad, high_rad_unit = NULL)

Arguments

Value

A single-layer 'SpatRaster' with the maximum solar radiation, named "bio21".

bio22_fast: Lowest Radiation Unit

Description

Identifies the lowest solar radiation of the temporal unit with the lowest value. If 'index_vector' is 'NULL', it calculates the row-wise minimum. If 'index_vector' is provided, it extracts the value from the specific column index for each row.

Usage

bio22_fast(srad, cell, index_vector = NULL)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio22" (the minimum solar radiation) and "cell".

bio22_terra: Lowest Radiation Unit

Description

Identifies the lowest solar radiation of the unit with the lowest value.

Usage

bio22_terra(srad, low_rad_unit = NULL)

Arguments

Value

A single-layer 'SpatRaster' with the minimum solar radiation, named "bio22".

bio23_fast: Radiation Seasonality (CV)

Description

Calculates the Coefficient of Variation (CV) of solar radiation. The formula used is: '(StandardDeviation / (Mean + 1)) * 100'. (The "+1" is added to the mean to avoid division by zero).

Usage

bio23_fast(srad, n_units, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio23" (Solar Radiation Seasonality) and "cell".

bio23_terra: Radiation Seasonality (CV)

Description

Calculates the Coefficient of Variation (CV) of solar radiation across all layers.

Usage

bio23_terra(srad)

Arguments

Value

A single-layer 'SpatRaster' with the solar radiation seasonality, named "bio23".

bio24_fast: Radiation of Wettest Period

Description

Calculates the mean solar radiation of the specific rolling period identified as the wettest (highest precipitation).

Usage

bio24_fast(speriod, pperiod_max_idx, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio24" (mean solar radiation of wettest period) and "cell".

bio24_terra: Radiation of Wettest Period

Description

Calculates the mean solar radiation of the specific rolling period identified as the wettest.

Usage

bio24_terra(prad, wettest_period)

Arguments

Value

A single-layer 'SpatRaster' with the solar radiation of the wettest period, named "bio24".

bio25_fast: Radiation of Driest Period

Description

Calculates the mean solar radiation of the specific rolling period identified as the driest (lowest precipitation).

Usage

bio25_fast(speriod, pperiod_min_idx, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio25" (mean solar radiation of driest period) and "cell".

bio25_terra: Radiation of Driest Period

Description

Calculates the mean solar radiation of the specific rolling period identified as the driest.

Usage

bio25_terra(prad, driest_period)

Arguments

Value

A single-layer 'SpatRaster' with the solar radiation of the driest period, named "bio25".

bio26_fast: Radiation of Warmest Period

Description

Calculates the mean solar radiation of the specific rolling period identified as the warmest (highest temperature).

Usage

bio26_fast(speriod, tperiod_max_idx, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio26" (mean solar radiation of warmest period) and "cell".

bio26_terra: Radiation of Warmest Period

Description

Calculates the mean solar radiation of the specific rolling period identified as the warmest.

Usage

bio26_terra(prad, warmest_period)

Arguments

Value

A single-layer 'SpatRaster' with the solar radiation of the warmest period, named "bio26".

bio27_fast: Radiation of Coldest Period

Description

Calculates the mean solar radiation of the specific rolling period identified as the coldest (lowest temperature).

Usage

bio27_fast(speriod, tperiod_min_idx, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio27" (mean solar radiation of coldest period) and "cell".

bio27_terra: Radiation of Coldest Period

Description

Calculates the mean solar radiation of the specific rolling period identified as the coldest.

Usage

bio27_terra(prad, coldest_period)

Arguments

Value

A single-layer 'SpatRaster' with the solar radiation of the coldest period, named "bio27".

bio28_fast: Mean Moisture

Description

Calculates mean moisture across all temporal units (usually 12 months).

Usage

bio28_fast(mois, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio28" (the calculated mean) and "cell" (the IDs).

bio28_terra: Mean Moisture

Description

Calculates mean moisture across all temporal units (layers).

Usage

bio28_terra(mois)

Arguments

Value

A single-layer 'SpatRaster' with the mean moisture, named "bio28".

bio29_fast: Highest Moisture Unit

Description

Identifies the highest moisture of the temporal unit with the highest value. If 'index_vector' is 'NULL', it calculates the row-wise maximum. If 'index_vector' is provided, it extracts the value from the specific column index for each row.

Usage

bio29_fast(mois, cell, index_vector = NULL)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio29" (the maximum moisture) and "cell".

bio29_terra: Highest Moisture Unit

Description

Identifies the highest moisture of the unit with the highest value.

Usage

bio29_terra(mois, high_mois_unit = NULL)

Arguments

Value

A single-layer 'SpatRaster' with the maximum moisture, named "bio29".

bio30_fast: Lowest Moisture Unit

Description

Identifies the lowest moisture of the temporal unit with the lowest value. If 'index_vector' is 'NULL', it calculates the row-wise minimum. If 'index_vector' is provided, it extracts the value from the specific column index for each row.

Usage

bio30_fast(mois, cell, index_vector = NULL)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio30" (the minimum moisture) and "cell".

bio30_terra: Lowest Moisture Unit

Description

Identifies the lowest moisture of the unit with the lowest value.

Usage

bio30_terra(mois, low_mois_unit = NULL)

Arguments

Value

A single-layer 'SpatRaster' with the minimum moisture, named "bio30".

bio31_fast: Moisture Seasonality (Std Dev * 100)

Description

Calculates Moisture Seasonality, defined as the standard deviation of moisture values across all temporal units (e.g., 12 months), multiplied by 100.

Usage

bio31_fast(mois, n_units, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio31" (Moisture Seasonality) and "cell".

bio31_terra: Moisture Seasonality (Std Dev * 100)

Description

Calculates the standard deviation of moisture across all layers, multiplied by 100.

Usage

bio31_terra(mois)

Arguments

Value

A single-layer 'SpatRaster' with the moisture seasonality, named "bio31".

bio32_fast: Mean Moisture of Most Moist Period

Description

Calculates the mean moisture of the specific rolling period identified as the most moist (highest moisture).

Usage

bio32_fast(speriod, speriod_max_idx, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio32" (mean moisture of most moist period) and "cell".

bio32_terra: Mean Moisture of Most Moist Period

Description

Calculates the mean moisture of the specific rolling period identified as the most moist.

Usage

bio32_terra(pmois, high_mois_period)

Arguments

Value

A single-layer 'SpatRaster' with the moisture of the most moist period, named "bio32".

bio33_fast: Mean Moisture of Least Moist Period

Description

Calculates the mean moisture of the specific rolling period identified as the least moist (lowest moisture).

Usage

bio33_fast(speriod, speriod_min_idx, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio33" (mean moisture of least moist period) and "cell".

bio33_terra: Mean Moisture of Least Moist Period

Description

Calculates the mean moisture of the specific rolling period identified as the least moist.

Usage

bio33_terra(pmois, low_mois_period)

Arguments

Value

A single-layer 'SpatRaster' with the moisture of the least moist period, named "bio33".

bio34_fast: Mean Moisture of Warmest Period

Description

Calculates the mean moisture of the specific rolling period identified as the warmest (highest temperature).

Usage

bio34_fast(speriod, tperiod_max_idx, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio34" (mean moisture of warmest period) and "cell".

bio34_terra: Mean Moisture of Warmest Period

Description

Calculates the mean moisture of the specific rolling period identified as the warmest.

Usage

bio34_terra(pmois, warmest_period)

Arguments

Value

A single-layer 'SpatRaster' with the moisture of the warmest period, named "bio34".

bio35_fast: Mean Moisture of Coldest Period

Description

Calculates the mean moisture of the specific rolling period identified as the coldest (lowest temperature).

Usage

bio35_fast(speriod, tperiod_min_idx, cell)

Arguments

Value

A **matrix** with dimensions 'c(N, 2)', where N is the number of input cells. The columns are named "bio35" (mean moisture of coldest period) and "cell".

bio35_terra: Mean Moisture of Coldest Period

Description

Calculates the mean moisture of the specific rolling period identified as the coldest.

Usage

bio35_terra(pmois, coldest_period)

Arguments

Value

A single-layer 'SpatRaster' with the moisture of the coldest period, named "bio35".

Tiled, Out-of-Core Bioclimatic Variable Calculation

Description

Internal function to calculate bioclimatic variables for very large datasets by processing them in tiles. It reads data from file paths using 'exactextractr' and performs calculations with 'Rfast'.

Usage

bioclim_fast(
  bios,
  n_units,
  tmin_path = NULL,
  tmax_path = NULL,
  prcp_path = NULL,
  tavg_path = NULL,
  srad_path = NULL,
  mois_path = NULL,
  period_length = 3,
  circular = TRUE,
  user_region = NULL,
  tile_degrees = 5,
  output_dir = tempdir(),
  verbose = TRUE,
  ...
)

Arguments

Value

Character string: Path to the temporary directory containing intermediate '.qs2' files, to be used by an assembly function.

See Also

The user-facing wrapper function 'derive_bioclim()'.

In-Memory Bioclimatic Variable Calculation

Description

Internal function to calculate bioclimatic variables using 'terra' functions. It is designed for datasets that can fit into RAM.

Usage

bioclim_terra(
  bios,
  tmin = NULL,
  tmax = NULL,
  tavg = NULL,
  prcp = NULL,
  srad = NULL,
  mois = NULL,
  period_length = 3,
  circular = TRUE,
  gdal_opt = c("COMPRESS=DEFLATE", "PREDICTOR=3", "NUM_THREADS=ALL_CPUS"),
  overwrite = FALSE,
  output_dir = tempdir(),
  verbose = TRUE,
  ...
)

Arguments

Value

A 'terra::SpatRaster' object pointing to the newly created files.

See Also

The user-facing wrapper function 'derive_bioclim()'.

Print Bioclimatic Variable Names

Description

This function prints the names of bioclimatic variables based on the specified indices.

Usage

bionames(bios = 1:35)

Arguments

Value

None. Prints the names of the selected bioclimatic variables to the console.

Examples

bionames()           # Print all bioclimatic variable names
bionames(c(1, 5, 12)) # Print names for variables 1, 5, and 12

Calculate Averages for SpatRasters

Description

Calculates temporal averages for a multi-layer SpatRaster. This function serves as a smart wrapper, automatically selecting between an in-memory ('terra') or out-of-core ('tiled') workflow based on data size.

Usage

calculate_average(
  x,
  index,
  output_names = NULL,
  output_dir = tempdir(),
  user_region = NULL,
  method = c("auto", "tiled", "terra"),
  tile_degrees = 5,
  gdal_opt = c("COMPRESS=DEFLATE", "PREDICTOR=3", "NUM_THREADS=ALL_CPUS"),
  overwrite = FALSE,
  verbose = TRUE
)

Arguments

Value

A 'terra::SpatRaster' object pointing to the newly created files, with the following characteristics:

• **Number of layers:** The number of layers will be equal to the number of unique values in the 'index' argument.

• **Layer names:** Layer names are determined by the ‘output_names' argument. If 'NULL', they will be generated automatically (e.g., ’avg_unit_01', 'avg_unit_02', etc.).

• **Extent:** If 'user_region' is provided, the extent of the output raster will be clipped to match that region. Otherwise, the extent will be the same as the input raster 'x'.

Examples

# The example raster "prcp.tif" is included in the package's `inst/extdata` directory.
# Load example data from Lesotho (Montlhy time series from 2016-01 to 2020-12)
raster_path <- system.file("extdata", "prcp.tif", package = "fastbioclim")
# Load the SpatRaster from the file
prcp_ts <- terra::rast(raster_path)
# The data has 60 layers (5 years of monthly data), so we create an
# index to group layers by month (1 to 12).
monthly_index <- rep(1:12, times = 5)
# Define a temporary directory for the output files
output_dir <- file.path(tempdir(), "monthly_prcp_avg")
# Run the calculate_average function
monthly_avg <- calculate_average(
  x = prcp_ts,
  index = monthly_index,
  output_names = "prcp_avg",
  output_dir = output_dir,
  overwrite = TRUE,
  verbose = FALSE
)
# Print the resulting SpatRaster summary
print(monthly_avg)
# Clean up the created files
unlink(output_dir, recursive = TRUE)

Calculate Rolling Temporal Averages for SpatRasters

Description

Calculates temporal summaries for each time unit over a moving window of cycles. This function is designed for time series where fundamental time **units** (e.g., months) are grouped into repeating **cycles** (e.g., years).

Usage

calculate_roll(
  x,
  window_size,
  freq = 12,
  step = 1,
  fun = "mean",
  name_template = "{prefix}_w{start_window}-{end_window}_u{idx_unit}",
  output_prefix = "output",
  output_dir = tempdir(),
  user_region = NULL,
  method = c("auto", "tiled", "terra"),
  tile_degrees = 5,
  gdal_opt = c("COMPRESS=DEFLATE", "PREDICTOR=3", "NUM_THREADS=ALL_CPUS"),
  overwrite = FALSE,
  verbose = TRUE
)

Arguments

Value

A 'terra::SpatRaster' object pointing to the newly created files, with the following characteristics:

• **Number of layers:** The number of layers is determined by the number of rolling windows processed (controlled by 'window_size' and 'step') multiplied by the cycle frequency ('freq').

• **Layer names:** Layer names are constructed based on the ‘name_template' argument, incorporating the window range and unit index (e.g., ’output_w01-20_u01').

• **Extent:** If 'user_region' is provided, the extent of the output raster will be clipped to match that region. Otherwise, the extent will be the same as the input raster 'x'.

Examples

# The example raster "prcp.tif" is included in the package's `inst/extdata` directory.
# Load example data from Lesotho (Montlhy time series from 2016-01 to 2020-12)
raster_path <- system.file("extdata", "prcp.tif", package = "fastbioclim")
# Load the SpatRaster from the file
prcp_ts <- terra::rast(raster_path)
# The data has 60 layers (5 years of monthly data).
# We want to calculate a 3-year rolling average on monthly data.
# Therefore, the window size is 3 (number of years) 
# and the lenght of the cycle is 12 (number of months).
# We also want a moving window of one month, so the number of steps is 1.
n_years <- 3
n_months <- 12
n_steps <- 1
# Define a temporary directory for the output files
output_dir <- file.path(tempdir(), "roll_prcp_avg")
# Run the calculate_average function
roll_avg <- calculate_roll(
  x = prcp_ts,
  window_size = n_years,
  freq = n_months,
  step = n_steps,
  fun = "mean",
  output_prefix = "prcp_roll_avg",
  output_dir = output_dir,
  overwrite = TRUE,
  verbose = FALSE
)
# Print the resulting SpatRaster summary of 36 layesr (n_year * n_months)
print(roll_avg)
# Clean up the created files
unlink(output_dir, recursive = TRUE)

Calculate Sums for SpatRasters

Description

Calculates temporal sums for a multi-layer SpatRaster. This function serves as a smart wrapper, automatically selecting between an in-memory ('terra') or out-of-core ('tiled') workflow based on data size.

Usage

calculate_sum(
  x,
  index,
  output_names = NULL,
  output_dir = tempdir(),
  user_region = NULL,
  method = c("auto", "tiled", "terra"),
  tile_degrees = 5,
  gdal_opt = c("COMPRESS=DEFLATE", "PREDICTOR=3", "NUM_THREADS=ALL_CPUS"),
  overwrite = FALSE,
  verbose = TRUE
)

Arguments

Value

A 'terra::SpatRaster' object pointing to the newly created files, with the following characteristics:

• **Number of layers:** The number of layers will be equal to the number of unique values in the 'index' argument.

• **Layer names:** Layer names are determined by the ‘output_names' argument. If 'NULL', they will be generated automatically (e.g., ’sum_unit_01', 'sum_unit_02', etc.).

• **Extent:** If 'user_region' is provided, the extent of the output raster will be clipped to match that region. Otherwise, the extent will be the same as the input raster 'x'.

Examples

# The example raster "prcp.tif" is included in the package's `inst/extdata` directory.
# Load example data from Lesotho (Monthly time series from 2016-01 to 2020-12)
raster_path <- system.file("extdata", "prcp.tif", package = "fastbioclim")
# Load the SpatRaster from the file
prcp_ts <- terra::rast(raster_path)
# To calculate the total annual precipitation, we group the 60 monthly layers by year.
annual_index <- rep(2016:2020, each = 12)
# Define a temporary directory for the output files
output_dir <- file.path(tempdir(), "annual_prcp_sum")
# Run the calculate_sum function
annual_sum <- calculate_sum(
  x = prcp_ts,
  index = annual_index,
  output_names = "prcp_sum",
  output_dir = output_dir,
  overwrite = TRUE,
  verbose = FALSE
)
# Print the resulting SpatRaster summary (should have 5 layers)
print(annual_sum)
# Clean up the created files
unlink(output_dir, recursive = TRUE)

Standardize Environmental Variable Input Format

Description

Converts data frames, matrices, or vectors into a consistent matrix format with preserved column/row names for downstream processing.

Usage

check_evar(evar)

Arguments

Value

Matrix with column names preserved. If input was a vector, returns 1-row matrix with vector names as column names.

Validate Climate Input Rasters

Description

Performs rigorous checks on a set of climate raster files to ensure they are suitable for bioclimatic variable calculation.

Usage

check_rasters(..., verbose = TRUE, check_nas = TRUE)

Arguments

Details

This function checks for two main sources of error: 1. **Geometric Inconsistency**: Ensures all input rasters share the exact same coordinate reference system (CRS), extent, and resolution. 2. **NA Mismatch**: Checks if the pattern of NA values is consistent across all layers of all provided climate variables. Mismatched NAs can lead to silent errors in calculations. This check can be time-consuming for very large rasters.

Value

A list containing a summary of the validation:

Calculate Sliding Periods for Temporal Analysis

Description

Defines sliding periods of a specified length for temporal calculations, handling wrap-around cases for circular data.

Usage

compute_periods(n_units, period_length, circular = TRUE)

Arguments

Value

List where each element contains 'period_length' consecutive unit indices.

Create an Inverse Cell ID Translation Function

Description

Generates a function to translate cell IDs from a source raster grid to a target raster grid, considering potential offsets and different dimensions. Handles cases where the target grid is a subset (e.g., cropped/masked) of the source grid.

Usage

define_translate(ncol_src, ncol_tgt, row_offset, col_offset)

Arguments

Value

A function that takes a vector of source cell IDs ('cell_src') and returns a vector of corresponding target cell IDs. Cells falling outside the target grid bounds will have 'NA_integer_' as their target ID.

Derive Comprehensive Bioclimatic Variables

Description

Calculates up to 35 bioclimatic variables from average monthly climate SpatRasters (or other temporal units). This function serves as a smart wrapper that automatically selects the most efficient processing workflow (in-memory vs. tiled) based on data size and user-defined region of interest.

Usage

derive_bioclim(
  bios,
  tmin = NULL,
  tmax = NULL,
  prcp = NULL,
  tavg = NULL,
  srad = NULL,
  mois = NULL,
  output_dir = tempdir(),
  period_length = 3,
  circular = TRUE,
  user_region = NULL,
  method = c("auto", "tiled", "terra"),
  tile_degrees = 5,
  gdal_opt = c("COMPRESS=DEFLATE", "PREDICTOR=3", "NUM_THREADS=ALL_CPUS"),
  overwrite = FALSE,
  verbose = TRUE,
  ...
)

Arguments

Details

This function unifies two processing backends. The 'method' argument controls which is used:

• '"auto"': (Default) Intelligently chooses between "terra" and "tiled" based on estimated memory requirements.

• '"terra"': Forces the fast, in-memory workflow. May fail on very large datasets.

• '"tiled"': Forces the memory-safe, out-of-core workflow. Ideal for very large datasets. Requires that the input SpatRasters point to files on disk.

Period-based variables (e.g., BIO8, BIO10) are calculated using a moving window defined by 'period_length'.

Value

A 'terra::SpatRaster' object pointing to the newly created bioclimatic variable rasters, with the following characteristics:

• **Number of layers:** The number of layers is equal to the number of unique variables requested in the 'bios' argument.

• **Layer names:** Layer names are standardized (e.g., 'bio01', 'bio12') corresponding to the requested variable numbers.

• **Extent:** If a 'user_region' is provided, the extent of the output raster will be clipped to match that region. Otherwise, the extent will be the same as the input rasters.

The returned object contains the following calculated variables:

bio01

Mean Temperature

bio02

Mean Diurnal Range

bio03

Isothermality

bio04

Temperature Seasonality

bio05

Max Temperature of Warmest Unit

bio06

Min Temperature of Coldest Unit

bio07

Temperature Range

bio08

Mean Temperature of Wettest Period

bio09

Mean Temperature of Driest Period

bio10

Mean Temperature of Warmest Period

bio11

Mean Temperature of Coldest Period

bio12

Precipitation Sum

bio13

Precipitation of Wettest Unit

bio14

Precipitation of Driest Unit

bio15

Precipitation Seasonality

bio16

Precipitation of Wettest Period

bio17

Precipitation of Driest Period

bio18

Precipitation of Warmest Period

bio19

Precipitation of Coldest Period

bio20

Mean Radiation

bio21

Highest Radiation Unit

bio22

Lowest Radiation Unit

bio23

Radiation Seasonality

bio24

Radiation of Wettest Period

bio25

Radiation of Driest Period

bio26

Radiation of Warmest Period

bio27

Radiation of Coldest Period

bio28*

Mean Moisture

bio29*

Highest Moisture Unit

bio30*

Lowest Moisture Unit

bio31*

Moisture Seasonality

bio32*

Mean Moisture of Most Moist Period

bio33*

Mean Moisture of Least Moist Period

bio34*

Mean Moisture of Warmest Period

bio35*

Mean Moisture of Coldest Period

Static Indices

For advanced use cases, such as time-series analysis or defining specific seasons, you can provide pre-calculated index rasters to override the dynamic calculations. These are passed as named 'SpatRaster' objects via the '...' argument (e.g., 'warmest_period = my_warmest_idx_rast'). The wrapper function automatically handles passing them to the appropriate workflow.

When using the "tiled" workflow, these static index rasters **must** be file-backed (i.e., not held entirely in memory). Supported static indices include:

• 'warmest_unit', 'coldest_unit', 'wettest_unit', 'driest_unit'

• 'high_rad_unit', 'low_rad_unit', 'high_mois_unit', 'low_mois_unit'

• 'warmest_period', 'coldest_period', 'wettest_period', 'driest_period'

• 'high_mois_period', 'low_mois_period'

Note

*The original moisture variables proposed in the ANUCLIM manual are based on the Moisture Index (MI). However, this function allows users to calculate moisture-based bioclimatic variables using other units of moisture as inputs, offering greater flexibility in input data usage.

References

O’Donnell, M. S., & Ignizio, D. A. (2012). Bioclimatic predictors for supporting ecological applications in the conterminous United States. ANUCLIM 6.1 User Guide. Centre for Resource and Environmental Studies, The Australian National University.

Examples

# The example raster "prcp.tif" is included in the package's `inst/extdata` directory.
# Load example data from Lesotho (Montlhy time series from 2016-01 to 2020-12)
raster_path <- system.file("extdata", "prcp.tif", package = "fastbioclim")
# Load the SpatRaster from the file
prcp_ts <- terra::rast(raster_path)
# The data has 60 layers (5 years of monthly data), so we create an
# index to group layers by month (1 to 12).
monthly_index <- rep(1:12, times = 5)
# Define a temporary directory for the output files
output_dir <- file.path(tempdir(), "prcp_bios")
# Run the calculate_average function
monthly_avg <- calculate_average(
  x = prcp_ts,
  index = monthly_index,
  output_names = "prcp_avg",
  output_dir = output_dir,
  overwrite = TRUE,
  verbose = FALSE
)
# Once the monthly averaged is obtained, we can use it to obtain bioclimatic variables based
# just in precipitation (bios 12, 13, 14, 15, 16, 17).
prcp_bios <- derive_bioclim(
  bios = 12:17,
  prcp = monthly_avg,
  output_dir = output_dir,
  overwrite = TRUE,
  verbose = FALSE
)
# Print the resulting SpatRaster summary with the four requested layers
print(prcp_bios)
# Clean up the created files
unlink(output_dir, recursive = TRUE)

Derive Custom Summary Statistics from Climate Variables

Description

Calculates a wide range of custom summary statistics for a primary climate variable, with options for interactions with a second variable. This function serves as a smart wrapper that automatically selects the most efficient processing workflow (in-memory vs. tiled).

Usage

derive_statistics(
  variable,
  stats = c("mean", "max", "min"),
  inter_variable = NULL,
  inter_stats = NULL,
  output_prefix = "var",
  suffix_inter_max = "inter_high",
  suffix_inter_min = "inter_low",
  output_dir = tempdir(),
  period_length = 3,
  period_stats = "mean",
  circular = TRUE,
  user_region = NULL,
  method = c("auto", "tiled", "terra"),
  tile_degrees = 5,
  gdal_opt = c("COMPRESS=DEFLATE", "PREDICTOR=3", "NUM_THREADS=ALL_CPUS"),
  overwrite = FALSE,
  verbose = TRUE,
  ...
)

Arguments

Details

This function provides a flexible alternative to 'derive_bioclim()' for any multi-layer climate variable (e.g., wind speed, humidity). It unifies two processing backends, controlled by the 'method' argument:

• '"auto"': (Default) Intelligently chooses between "terra" and "tiled".

• '"terra"': Forces the fast, in-memory workflow.

• '"tiled"': Forces the memory-safe, out-of-core workflow. Requires that all input SpatRasters point to files on disk.

Value

A 'terra::SpatRaster' object pointing to the newly created summary rasters, with the following characteristics:

• **Number of layers:** The number of layers is equal to the total number of statistics requested in the 'stats' and 'inter_stats' arguments.

• **Layer names:** Layer names are constructed by combining the ‘output_prefix' with the name of each statistic (e.g., ’prefix_mean', 'prefix_max'). For interactive statistics, the names are 'prefix_suffix_inter_high' and 'prefix_suffix_inter_low', where the suffixes are controlled by the 'suffix_inter_max' and 'suffix_inter_min' arguments.

• **Extent:** If a 'user_region' is provided, the extent of the output raster will be clipped to match that region. Otherwise, the extent will be the same as the input 'variable' raster.

Static Indices

For advanced control, provide pre-calculated index rasters as named 'SpatRaster' objects via the '...' argument (e.g., 'max_unit = max_idx_rast'). Supported indices: 'max_unit', 'min_unit', 'max_period', 'min_period', 'max_interactive', 'min_interactive'.

Examples

# The example raster "prcp.tif" is included in the package's `inst/extdata` directory.
# Load example data from Lesotho (Montlhy time series from 2016-01 to 2020-12)
raster_path <- system.file("extdata", "prcp.tif", package = "fastbioclim")
# Load the SpatRaster from the file
prcp_ts <- terra::rast(raster_path)
# The data has 60 layers (5 years of monthly data), so we create an
# index to group layers by month (1 to 12).
monthly_index <- rep(1:12, times = 5)
# Define a temporary directory for the output files
output_dir <- file.path(tempdir(), "prcp_stats")
# Run the calculate_average function
monthly_avg <- calculate_average(
  x = prcp_ts,
  index = monthly_index,
  output_names = "prcp_avg",
  output_dir = output_dir,
  overwrite = TRUE,
  verbose = FALSE
)
# Once the monthly averaged is obtained, we can use it to derive statictics of precipitation
# Here we will obtain four summary statitics: mean, maximum, minimum, and standard deviation
prcp_stats <- derive_statistics(
  variable = monthly_avg,
  stats = c("mean", "max", "min", "stdev"),
  prefix_variable = "prcp",
  output_dir = output_dir,
  overwrite = TRUE
)
# Print the resulting SpatRaster summary with the four requested layers
print(prcp_stats)
# Clean up the created files
unlink(output_dir, recursive = TRUE)

Create Moving Window Summaries of a SpatRaster

Description

This internal helper function calculates moving window summaries (periods) across the layers of a SpatRaster. It generates a new SpatRaster where each layer represents the sum of the layers from the original raster that fall within a specific window. The 'circular' argument controls whether the windows "wrap around" from the end of the time series to the beginning.

Usage

get_window(x, period, circular)

Arguments

Value

A 'terra::SpatRaster' object where each layer is the pixel-wise sum for a corresponding period.

• If 'circular = TRUE', the output has the same number of layers as the input 'x'.

• If 'circular = FALSE', the output has 'nlyr(x) - period + 1' layers.

Tiled, Out-of-Core Rolling Time Series Averaging (Internal)

Description

Tiled, Out-of-Core Rolling Time Series Averaging (Internal)

Usage

roll_fast(
  paths,
  window_size,
  freq,
  step,
  fun,
  output_names_list,
  user_region,
  tile_degrees,
  output_dir,
  verbose
)

In-Memory Rolling Time Series Averaging (Internal)

Description

In-Memory Rolling Time Series Averaging (Internal)

Usage

roll_terra(
  x,
  window_size,
  freq,
  step,
  fun,
  output_names_list,
  output_dir,
  gdal_opt,
  overwrite,
  verbose
)

Tiled, Out-of-Core Custom Variable Summarization

Description

Internal function to calculate custom summary statistics for very large datasets by processing them in tiles.

Usage

stats_fast(
  variable_path,
  n_units,
  stats = c("mean", "max", "min"),
  period_length = 3,
  period_stats = "mean",
  circular = TRUE,
  inter_variable_path = NULL,
  inter_stats = NULL,
  max_unit_path = NULL,
  min_unit_path = NULL,
  max_period_path = NULL,
  min_period_path = NULL,
  max_interactive_path = NULL,
  min_interactive_path = NULL,
  output_prefix = "var",
  suffix_inter_max = "inter_high",
  suffix_inter_min = "inter_low",
  user_region = NULL,
  tile_degrees = 5,
  output_dir = tempdir(),
  write_raw_vars = FALSE,
  verbose = TRUE,
  ...
)

Arguments

Value

Character string: Path to the temporary directory containing intermediate '.qs2' files.

See Also

The user-facing wrapper function 'derive_statistics()'.

In-Memory Custom Variable Summarization

Description

Internal function to calculate custom summary statistics using 'terra' functions. Designed for datasets that fit into RAM.

Usage

stats_terra(
  variable,
  stats = c("mean", "max", "min", "cv_cli", "max_period", "min_period"),
  period_length = 3,
  period_stats = "mean",
  circular = TRUE,
  inter_variable = NULL,
  inter_stats = c("max_inter", "min_inter"),
  max_unit = NULL,
  min_unit = NULL,
  max_period = NULL,
  min_period = NULL,
  max_interactive = NULL,
  min_interactive = NULL,
  output_prefix = "var",
  suffix_inter_max = "inter_high",
  suffix_inter_min = "inter_low",
  gdal_opt = c("COMPRESS=DEFLATE", "PREDICTOR=3", "NUM_THREADS=ALL_CPUS"),
  overwrite = FALSE,
  output_dir = tempdir(),
  verbose = TRUE,
  ...
)

Arguments

Value

A 'terra::SpatRaster' object containing the calculated summary layers.

See Also

The user-facing wrapper function 'derived_statistics()'.

Calculate Temporal Period Aggregates

Description

Computes aggregates (sums or mean) over defined temporal periods and identifies min/max periods.

Usage

var_periods(variable, periodos, n_units, period_length, stat)

Arguments

Value

Matrix with period sums ('num_periods' columns named "period_X"), "min_idx", and "max_idx" columns.

Export Bioclimatic Variables to GeoTIFF

Description

Assembles intermediate '.qs2' files into a full vector in memory, performs cell ID mapping if necessary, and writes final GeoTIFF rasters.

Usage

write_layers(
  input_dir,
  output_dir,
  file_pattern = "bio",
  gdal_opt = c("COMPRESS=DEFLATE", "PREDICTOR=3", "NUM_THREADS=ALL_CPUS"),
  overwrite = FALSE,
  verbose = TRUE
)

Arguments

Value

None. Writes GeoTIFF files to 'output_dir'.

Author(s)

Luis Osorio-Olvera, Gonzalo E. Pinilla-Buitrago