Envirotyping for Quantitative Genetics and Plant Breeding

An enviromics toolkit for genotype-by-environment analysis and envirotype-informed genomic prediction. Collects worldwide daily and hourly weather from 'NASA POWER', soil profiles from 'SoilGrids', elevation and bioclimatic layers from 'WorldClim', agro-ecological zones from 'FAO GAEZ', and downscaled 'CMIP6' climate scenarios, with checkpointed retrieval that can resume after an interrupted run. Derives agro-meteorological parameters (solar radiation, evapotranspiration, thermal time, atmospheric demand), runs a FAO-56 single crop coefficient soil water balance, and maps daily weather onto crop developmental stages. Builds environmental covariable matrices, envirotype typologies and frequency matrices, mega-environment clusters, historical risk profiles and target population of environments weights. Reduces interval-resolved envirotypes by principal component analysis, delineates soil zones with Gaussian mixture models, and constructs linear, Gaussian and arc-cosine environmental relatedness kernels. Fits Bayesian reaction-norm kernel models and predicts trained genotypes at untested locations and planting dates, with coverage diagnostics, spatial mapping and planting-window optimisation. Simulation functions generate multi-environment trials and enviromes with known architecture for power analysis. Methods are described in Costa-Neto, Galli, Carvalho, Crossa and Fritsche-Neto (2021) , and build on the Bayesian genotype-by-environment kernel sampler of Granato, Cuevas, Luna-Vazquez, Crossa, Montesinos-Lopez, Burgueno and Fritsche-Neto (2018) .

{EnvRtype} - The Envirotyper Library in Genetics

EnvRtype is an R package for enviromics — the study of the envirome, the set of environmental conditions linked to the biological performance of living beings (Costa-Neto and Fritsche-Neto, 2021; Crossa et al., 2021). It collects worldwide daily weather and soil data, derives agro-meteorological parameters, builds environmental covariable matrices and relatedness kernels, mines environmental typologies, delineates soil zones with Gaussian mixture models, and runs a FAO-56 soil water balance for use in enviromics and genotype-by-environment (GxE) analyses.

This is a fully re-engineered successor to the original EnvRtype (allogamous/EnvRtype, 2021), extending it from a weather-typing toolbox into an end-to-end envirotyping, simulation and prediction framework.

📄 Original publication: Costa-Neto, G., Galli, G., Carvalho, H. F., Crossa, J., & Fritsche-Neto, R. (2021). EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture. G3 Genes|Genomes|Genetics, 11(4), jkab040. Read the paper →

📖 Documentation: https://gcostaneto.github.io/EnvRtype/

Installation

if (!require("devtools")) install.packages("devtools")
devtools::install_github("gcostaneto/EnvRtype")

library(EnvRtype)

What's new compared to the original EnvRtype (2021)

The original EnvRtype (Costa-Neto et al., 2021, G3) focused on collecting NASA POWER weather data, computing environmental typologies and building environmental kernels for reaction-norm models. This version keeps that workflow and adds several new layers:

Package modules & workflow

The package is organised in four data layers — acquisition, processing, characterisation and prediction — plus a simulation engine that generates ground-truth data to validate each layer. Each subsection lists the functions involved and the primary references used to build them.

Overview — the four layers + simulation

Design follows the enviromics reaction-norm framework of Costa-Neto et al. (2021, G3) and its envirome-wide extension (Costa-Neto et al., 2023, G3).

flowchart TB
  L1["<b>1 · ACQUIRE</b><br/>get_weather · get_soil · get_elevation<br/>get_bioclim · get_spatial · get_AEZ<br/>get_climate_scenario · + resumable/hourly variants"]
  L2["<b>2 · PROCESS</b><br/>processWTH · summaryWTH · param_*<br/>water_balance · env_phenology"]
  L3["<b>3 · CHARACTERISE</b><br/>W_matrix · T_matrix · env_indices · env_pca<br/>soil_classification · env_typing · env_risk_profile"]
  L4["<b>4 · MODEL &amp; SCAN</b><br/>env_kernel · get_kernel · kernel_model<br/>kernel_cv · scan_untested_envs · map_scan"]
  SIM["<b>SIMULATE</b><br/>sim_markers · sim_envirome<br/>sim_met() → C_env → sim_W()<br/><i>ground truth for every layer</i>"]

  L1 --> L2 --> L3 --> L4
  SIM -.->|"validates"| L3
  SIM -.->|"validates"| L4

  classDef l1 fill:#1565c0,stroke:#0d47a1,color:#fff
  classDef l2 fill:#1976d2,stroke:#0d47a1,color:#fff
  classDef l3 fill:#2e7d32,stroke:#1b5e20,color:#fff
  classDef l4 fill:#c62828,stroke:#b71c1c,color:#fff
  classDef sim fill:#6a1b9a,stroke:#4a148c,color:#fff
  class L1 l1
  class L2 l2
  class L3 l3
  class L4 l4
  class SIM sim

1–2 · Data acquisition and processing

Weather and geodata come from NASA POWER via nasapower (Sparks, 2018), SoilGrids 2.0 (Poggio et al., 2021), WorldClim 2 (Fick & Hijmans, 2017), bioclimatic predictors (O'Donnell & Ignizio, 2012), CMIP6 / ScenarioMIP scenarios (Hawkins et al., 2013; O'Neill et al., 2016) and GAEZ v4 (FAO & IIASA, 2021). Agro-meteorological processing, the FAO-56 soil water balance and phenology follow Allen et al. (1998), Pereira et al. (2021), Doorenbos & Kassam (1979), Steduto et al. (2012), Borg & Grimes (1986), Forsythe et al. (1995), Zadoks et al. (1974) and Counce et al. (2000).

flowchart LR
  GW["get_weather()"]
  GWH["get_weather_hourly()"]
  GWR["get_weather_resumable()"]
  RPL["read_progress_log()"]
  RFL["restart_from_log()"]
  GS["get_soil()"]
  GSR["get_soil_resumable()"]
  GEL["get_elevation()"]
  GBC["get_bioclim()"]
  GSP["get_spatial()"]
  GAEZ["get_AEZ()"]
  GCS["get_climate_scenario()"]

  PRAD["param_radiation()"]
  PATM["param_atmospheric()"]
  PTMP["param_temperature()"]
  PWT["processWTH()"]
  SWT["summaryWTH()"]

  EPH["env_phenology()"]
  PHT["phenology_templates()"]
  PWD["planting_window_table()"]
  BPD["best_planting_date()"]
  SPH["show_phenology()"]
  PPW["plot_planting_window()"]

  WBAL["water_balance()"]
  SWB["summary_water_balance()"]

  GWR --> RPL --> RFL --> GW
  GW --> PWT
  GWH --> PWT
  PRAD --> PWT
  PTMP --> PWT
  GEL --> PATM
  PATM --> PWT
  PWT --> SWT
  PWT --> EPH
  PHT --> EPH
  EPH --> PWD
  EPH --> SPH
  PWD --> BPD
  PWD --> PPW
  PWT --> WBAL
  GSR --> GS
  GS --> WBAL
  GEL --> WBAL
  WBAL --> SWB

  classDef collect fill:#1565c0,stroke:#0d47a1,color:#fff
  classDef char fill:#2e7d32,stroke:#1b5e20,color:#fff
  classDef wb fill:#0097a7,stroke:#006064,color:#fff
  class GW,GWH,GWR,RPL,RFL,GS,GSR,GEL,GBC,GSP,GAEZ,GCS,PRAD,PATM,PTMP,PWT,SWT collect
  class EPH,PHT,PWD,BPD,SPH,PPW char
  class WBAL,SWB wb

3 · Characterisation and dissection

Environmental covariables, typologies and kernels follow Costa-Neto et al. (2021, G3); interval-resolved indices follow Della Coletta et al. (2023); breeding-zone typologies and target-population importance follow Costa-Neto et al. (2023, Agronomy Journal); copula reparameterisation follows Sklar (1959), Genest & Rivest (1993) and Salvadori et al. (2007); Gaussian-mixture soil zoning follows Fraley & Raftery (2002), Scrucca et al. (2016) and Egozcue et al. (2003).

flowchart LR
  SWT["summaryWTH()"]
  GS["get_soil()"]
  WM["W_matrix()"]
  ETYP["env_typing()"]
  TM["T_matrix()"]
  EEX["env_expand()"]
  ECOP["env_copula()"]
  ERP["env_risk_profile()"]
  TPW["tpe_weights()"]
  PRK["project_risk()"]
  ETI["env_target_importance()"]
  CVS["coverage_summary()"]
  GCS["get_climate_scenario()"]
  SCL["soil_classification()"]

  EIX["env_indices()"]
  EPC["env_pca()"]
  EPS["env_pca_scree()"]
  EPB["env_pca_biplot()"]
  ELC["env_loading_curve()"]
  ECOR["env_cor()"]
  ECH["env_cor_heatmap()"]
  EPA["env_pc_associate()"]

  SWT --> WM
  WM --> ETYP
  ETYP --> TM
  WM --> EEX
  WM --> ECOP
  WM --> ETI
  ERP --> TPW
  ERP --> PRK
  GCS --> PRK
  TPW --> CVS
  GS --> SCL
  EIX --> WM
  EIX --> EPC
  EIX --> ECOR
  ECOR --> ECH
  EPC --> EPS
  EPC --> EPB
  EPC --> ELC
  EPC --> EPA

  classDef char fill:#2e7d32,stroke:#1b5e20,color:#fff
  classDef dis fill:#6a1b9a,stroke:#4a148c,color:#fff
  classDef collect fill:#1565c0,stroke:#0d47a1,color:#fff
  class WM,ETYP,TM,EEX,ECOP,ERP,TPW,PRK,ETI,CVS,SCL char
  class EIX,EPC,EPS,EPB,ELC,ECOR,ECH,EPA dis
  class SWT,GS,GCS collect

4 · Prediction and scanning

Reaction-norm and kernel models follow Costa-Neto et al. (2021, G3), the nonlinear / Gaussian kernels of Costa-Neto, Fritsche-Neto & Crossa (2021, Heredity) and the deep kernels of Cuevas et al. (2019); the GxE modelling engine follows the BGGE approach of Granato et al. (2018); scanning of untested environments follows the envirome-wide prediction of Costa-Neto et al. (2023, G3).

flowchart TB
  EK["env_kernel()"]
  GK["get_kernel()<br/>K_G · K_E · K_S"]
  DK["decompose_kernels()"]
  UDK["undecompose_kernels()"]
  TGK["truncate_gxe_kernel()"]
  KM["kernel_model()"]
  KCV["kernel_cv()"]
  KMC["kernel_model_clustered()"]
  KMM["kernel_model_mc()"]
  ECL["env_cluster()"]
  CLE["cluster_environments()"]
  VCS["varcomp_summary()"]
  SUE["scan_untested_envs()"]
  GSC["grid_scan()"]
  MSC["map_scan()"]
  SST["scan_spatial_table()"]
  CVS["coverage_summary()"]

  EK --> GK
  GK --> DK
  DK --> UDK
  DK --> TGK
  TGK --> KM
  DK --> KM
  KM --> KCV
  KM --> VCS
  KM --> SUE
  ECL --> CLE
  CLE --> KMC
  KM --> KMM
  SUE --> GSC
  SUE --> MSC
  SUE --> SST
  SUE --> CVS

  classDef pred fill:#c62828,stroke:#b71c1c,color:#fff
  classDef rel fill:#ef6c00,stroke:#e65100,color:#fff
  classDef char fill:#2e7d32,stroke:#1b5e20,color:#fff
  class GK,DK,UDK,TGK,KM,KCV,KMC,KMM,VCS,SUE,GSC,MSC,SST,CVS pred
  class EK rel
  class ECL,CLE char

Simulation — closing the validation loop

The simulator encodes a known environmental covariance and reaction-norm structure to benchmark every layer, following the envirome-wide prediction framework of Costa-Neto et al. (2023, G3) and the kernel models of Costa-Neto et al. (2021, G3). sim_markers() and sim_envirome() additionally generate marker and envirome data with controllable genetic and environmental diversity (MAF spectrum, population structure, linkage; mega-environments, gradients and covariable redundancy), feeding sim_met() and get_kernel() directly.

flowchart LR
  SMK["sim_markers()<br/><i>genetic diversity</i>"]
  K["genomic kinship K<br/><i>n × n lines</i>"]
  SEV["sim_envirome()<br/><i>environmental diversity</i>"]
  SMC["sim_met_C()<br/><i>q × q envs</i>"]
  SM["sim_met()"]
  CE["$C_env<br/><i>q × q correlation<br/>among environments</i>"]
  PH["$data<br/>env · gid · rep · value"]
  SW["sim_W()"]
  SWG["sim_W_grid()"]
  W["simulated W<br/><i>known % explained</i>"]
  GK["get_kernel()"]
  KM["kernel_model()"]
  VCS["varcomp_summary()"]
  CHK{{"compare to<br/>known truth"}}

  SMK --> K
  K --> SM
  K --> GK
  SEV --> SM
  SEV --> GK
  SMC --> SM
  SM --> CE
  SM --> PH
  CE --> SW
  SW --> W
  SWG --> W
  W --> GK
  PH --> KM
  GK --> KM
  KM --> VCS
  VCS --> CHK
  SM -.->|"$truth"| CHK

  classDef sim fill:#6a1b9a,stroke:#4a148c,color:#fff
  classDef pred fill:#c62828,stroke:#b71c1c,color:#fff
  classDef data fill:#455a64,stroke:#263238,color:#fff
  classDef chk fill:#f9a825,stroke:#f57f17,color:#000
  class SM,SW,SWG,SMC,SMK,SEV sim
  class GK,KM,VCS pred
  class K,CE,PH,W data
  class CHK chk

Prediction

EnvRtype fits enviromics-enabled reaction-norm models in which phenotypes are regressed on genomic, environmental and GxE relatedness kernels. The current prediction workflow provides:

• Kernel construction — get_kernel() assembles genomic (K_G), environmental (K_E) and soil (K_S) kernels; env_kernel() builds Gaussian / linear environmental kernels; decompose_kernels(), undecompose_kernels() and truncate_gxe_kernel() manage the GxE block structure.
• Model fitting — kernel_model() fits the multi-kernel Bayesian reaction-norm model; kernel_model_mc() runs multiple chains; kernel_model_clustered() fits environment-clustered models built with env_cluster() / cluster_environments().
• Validation & variance — kernel_cv() runs cross-validation (CV1 / CV2 / CV0 schemes) and varcomp_summary() reports variance components and the share of variance explained by each kernel.
• Untested environments — scan_untested_envs() predicts genotype performance in environments with no phenotypes, while grid_scan(), map_scan(), scan_spatial_table() and coverage_summary() summarise the target population of environments (TPE).

These tools implement the reaction-norm and kernel methods of Costa-Neto et al. (2021, G3), the nonlinear / Gaussian kernels of Costa-Neto, Fritsche-Neto & Crossa (2021, Heredity), the deep kernels of Cuevas et al. (2019, G3), the BGGE GxE engine of Granato et al. (2018, G3), and the envirome-wide prediction of untested environments of Costa-Neto et al. (2023, G3). Full citations are listed under References for the new functions below.

Acknowledgements

Original EnvRtype team (2020–2021)

EnvRtype began in 2020 and was first published as allogamous/EnvRtype in Costa-Neto, G., Galli, G., Carvalho, H. F., Crossa, J., & Fritsche-Neto, R. (2021). EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture. G3: Genes|Genomes|Genetics, 11(4), jkab040. We gratefully acknowledge the original authors and the community of users whose feedback shaped this new version.

Current maintainers and developers

• Germano Costa-Neto — maintainer & lead developer <[email protected]>
• Fernanda Pontes — developer <[email protected]>

References for the new functions

The methods and data sources underlying the new and extended functions are documented below, grouped by module.

Enviromics framework, kernels and genomic prediction

• Costa-Neto, G., Galli, G., Carvalho, H. F., Crossa, J., & Fritsche-Neto, R. (2021). EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture. G3: Genes|Genomes|Genetics, 11(4), jkab040. https://doi.org/10.1093/g3journal/jkab040
• Costa-Neto, G., Fritsche-Neto, R., & Crossa, J. (2021). Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials. Heredity, 126, 92–106. https://doi.org/10.1038/s41437-020-00353-1
• Costa-Neto, G., Galli, G., Carvalho, H. F., Crossa, J., & Fritsche-Neto, R. (2023). Envirome-wide associations enhance multi-environment prediction. G3: Genes|Genomes|Genetics, 13(2), jkac313. https://doi.org/10.1093/g3journal/jkac313
• Cuevas, J., Montesinos-López, O., Juliana, P., Guzmán, C., Pérez-Rodríguez, P., González-Bucio, J., Burgueño, J., Montesinos-López, A., & Crossa, J. (2019). Deep kernel for genomic and near infrared predictions in multi-environment breeding trials. G3: Genes|Genomes|Genetics, 9(9), 2913–2924. https://doi.org/10.1534/g3.119.400493
• Granato, I., Cuevas, J., Luna-Vázquez, F., Crossa, J., Montesinos-López, O., Burgueño, J., & Fritsche-Neto, R. (2018). BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models. G3: Genes|Genomes|Genetics, 8(9), 3039–3047. https://doi.org/10.1534/g3.118.200435

Environmental characterisation, typologies and target-population importance

• Costa-Neto, G., da Matta, D., Fernandes, I. K., & Heinemann, A. B. (2023). Environmental clusters defining breeding zones for tropical irrigated rice in Brazil. Agronomy Journal, 115(5). https://doi.org/10.1002/agj2.21481
• Della Coletta, R., Liese, S. E., Fernandes, S. B., Mikel, M. A., Bohn, M. O., Lipka, A. E., & Hirsch, C. N. (2023). Linking genetic and environmental factors through marker effect networks to understand trait plasticity. GENETICS, 224(4), iyad103. https://doi.org/10.1093/genetics/iyad103

Copula-based environmental indices (env_copula)

• Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l'Institut de Statistique de l'Université de Paris, 8, 229–231.
• Genest, C., & Rivest, L.-P. (1993). Statistical inference procedures for bivariate Archimedean copulas. Journal of the American Statistical Association, 88(423), 1034–1043. https://doi.org/10.1080/01621459.1993.10476372
• Salvadori, G., De Michele, C., Kottegoda, N. T., & Rosso, R. (2007). Extremes in Nature: An Approach Using Copulas. Springer.

Environmental data acquisition (weather, soil, elevation, bioclimate, scenarios, agro-ecological zones)

• Sparks, A. H. (2018). nasapower: NASA-POWER Data from R. Journal of Open Source Software, 3(30), 1035. https://doi.org/10.21105/joss.01035
• Forsythe, W. C., Rykiel, E. J., Stahl, R. S., Wu, H., & Schoolfield, R. M. (1995). A model comparison for daylength as a function of latitude and day of the year. Ecological Modelling, 80(1), 87–95. https://doi.org/10.1016/0304-3800(94)00034-F
• Peng, S., Huang, J., Sheehy, J. E., Laza, R. C., Visperas, R. M., Zhong, X., Centeno, G. S., Khush, G. S., & Cassman, K. G. (2004). Rice yields decline with higher night temperature from global warming. Proceedings of the National Academy of Sciences, 101(27), 9971–9975. https://doi.org/10.1073/pnas.0403720101
• Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty. SOIL, 7(1), 217–240. https://doi.org/10.5194/soil-7-217-2021
• Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086
• 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. https://doi.org/10.3133/ds691
• Hawkins, E., Osborne, T. M., Ho, C. K., & Challinor, A. J. (2013). Calibration and bias correction of climate projections for crop modelling: an idealised case study over Europe. Agricultural and Forest Meteorology, 170, 19–31. https://doi.org/10.1016/j.agrformet.2012.04.007
• O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., & Sanderson, B. M. (2016). The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geoscientific Model Development, 9(9), 3461–3482. https://doi.org/10.5194/gmd-9-3461-2016
• FAO & IIASA (2021). Global Agro-Ecological Zones (GAEZ v4). FAO, Rome. https://gaez.fao.org

Water balance, evapotranspiration and phenology

• Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration: guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56. FAO, Rome.
• Pereira, L. S., Paredes, P., López-Urrea, R., Hunsaker, D. J., Mota, M., & Mohammadi Shad, Z. (2021). Standard single and basal crop coefficients for field crops. Updates and advances to the FAO56 crop water requirements method. Agricultural Water Management, 243, 106466. https://doi.org/10.1016/j.agwat.2020.106466
• Doorenbos, J., & Kassam, A. H. (1979). Yield response to water. FAO Irrigation and Drainage Paper 33. FAO, Rome.
• Steduto, P., Hsiao, T. C., Fereres, E., & Raes, D. (2012). Crop yield response to water. FAO Irrigation and Drainage Paper 66. FAO, Rome.
• Borg, H., & Grimes, D. W. (1986). Depth development of roots with time: an empirical description. Transactions of the ASAE, 29(1), 194–197. https://doi.org/10.13031/2013.30125
• Zadoks, J. C., Chang, T. T., & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed Research, 14(6), 415–421. https://doi.org/10.1111/j.1365-3180.1974.tb01084.x
• Counce, P. A., Keisling, T. C., & Mitchell, A. J. (2000). A uniform, objective, and adaptive system for expressing rice development. Crop Science, 40(2), 436–443. https://doi.org/10.2135/cropsci2000.402436x

Soil classification and Gaussian-mixture risk zoning

• Fraley, C., & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631. https://doi.org/10.1198/016214502760047131
• Scrucca, L., Fop, M., Murphy, T. B., & Raftery, A. E. (2016). mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. The R Journal, 8(1), 205–233. https://doi.org/10.32614/RJ-2016-021
• Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G., & Barceló-Vidal, C. (2003). Isometric logratio transformations for compositional data analysis. Mathematical Geology, 35(3), 279–300. https://doi.org/10.1023/A:1023818214614
• Zhang, F., Jia, Z., Wu, S., Chen, C., Chen, X., Zheng, C., & Xu, M. (2025). Machine learning and Gaussian mixture model for delineating soil cadmium risk zones. Ecosystem Health and Sustainability. https://doi.org/10.34133/ehs.0402

Citation

If you use EnvRtype in your research, please cite the original publication:

Costa-Neto, G., Galli, G., Carvalho, H. F., Crossa, J., & Fritsche-Neto, R. (2021). EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture. G3 Genes|Genomes|Genetics, 11(4), jkab040. https://doi.org/10.1093/g3journal/jkab040

🔗 academic.oup.com/g3journal/article/11/4/jkab040/6129777

@article{costaneto2021envrtype,
  title   = {EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture},
  author  = {Costa-Neto, Germano and Galli, Giovanni and Carvalho, Humberto Fanelli
             and Crossa, Jos{\'e} and Fritsche-Neto, Roberto},
  journal = {G3 Genes|Genomes|Genetics},
  volume  = {11},
  number  = {4},
  pages   = {jkab040},
  year    = {2021},
  doi     = {10.1093/g3journal/jkab040},
  url     = {https://academic.oup.com/g3journal/article/11/4/jkab040/6129777}
}

From within R:

citation("EnvRtype")

Please also kindly let the maintainer know when you are using EnvRtype (contact [email protected]).

License

Released under the GPL-3 license — free and open source. You may use, study, modify and redistribute the code, provided derivative works remain under the same terms.