Package index • EnvRtype

All functions

G2F_2014_17: Genome to Fields (G2F) GxE panel (2014-2017)
T_matrix(): Envirotype Frequency Matrix, Ready for the Kernel Pipeline
W_matrix(): Build the Matrix of Environmental Covariables for Reaction Norm
aez_default_url(): Default GAEZ Download URL
aez_legend(): FAO GAEZ Agro-Ecological Zone Legend
as.data.frame(<sim_met>): Coerce a Simulated MET to a data.frame
as.matrix(<sim_W>): Coerce a Simulated Envirome to a Plain Matrix
as.matrix(<sim_envirome>): Coerce a Simulated Envirome Diversity Matrix to a Plain Matrix
best_planting_date(): Best planting date per genotype
clay_5_15: Clay content (g/kg) from 5 to 15cm depth for Nairobi, Kenya
cluster_environments(): Group environments into mega-environments (clusters)
coverage_summary(): Summarise coverage of a scanned region
decompose_kernels(): Attach spectral decompositions to a list of dense kernels
env_cluster(): Envirotype clustering into mega-environments
env_copula(): Copula-based Dependence Indices for an Environmental Covariable Matrix
env_cor(): Extract the Correlation Among Environments
env_cor_heatmap(): Correlation Heatmap of Environmental Indices
env_expand(): Merge an environmental covariable matrix onto a phenotypic table
env_indices(): Build Interval-Resolved Environmental Indices
env_kernel(): Easily Building of Environmental Relatedness Kernels
env_loading_curve(): Seasonal Loading Curves of an Environmental PC
env_pc_associate(): Correlate Environmental PCs with a Per-Environment Variable
env_pca(): PCA of Interval-Resolved Environmental Indices
env_pca_biplot(): Environments in Principal Component Space
env_pca_scree(): Scree Plot of Environmental PCA
env_phenology(): Thermal-Time Phenology: Map Daily Weather onto Developmental Stages
env_risk_profile(): Historical Envirotype Risk Profile Across Years
env_target_importance(): Environmental drivers of phenotypic/target clustering
env_typing(): Environmental Typologies based on Cardinal, Quantilic Limits or Data-Driven Mining
get_AEZ(): Retrieve FAO GAEZ Agro-Ecological Zone Classification for Sites
get_bioclim(): WorldClim Bioclimatic Variables at Trial Sites
get_climate_scenario(): Future-Climate Envirotyping from CMIP6 Downscaled Scenarios
get_elevation(): Elevation at Trial Sites from WorldClim 2.1
get_kernel(): Envirotype-informed Kernels for Multi-Environment Genomic Prediction
get_soil(): Collect Soil Features from SoilGrids (250 m)
get_soil_resumable(): Crash-Resilient Wrapper for get_soil()
get_spatial(): Extract Point Estimates from Raster Files
get_weather(): Easily Collect Worldwide Daily Weather Data
get_weather_hourly(): Day/Night Partitioned Hourly Weather from NASA POWER
get_weather_resumable(): Crash-Resilient Wrapper for get_weather()
grid_scan(): Scan many sites in memory-bounded chunks
kernel_cv(): k-fold cross-validation predictive ability (out-of-sample)
kernel_model(): Kernel Models for Predicting Phenotypes across Multi-Environment Conditions
kernel_model_clustered(): Fit kernel_model() informed by an environment-cluster table
kernel_model_mc(): Multi-chain fit with convergence diagnostics (Gelman-Rubin R-hat, ESS)
maizeG: Maize Genomic Relationship Matrix (GRM)
maizeWTH: Maize Weather Data
maizeYield: Maize Phenotypic Data Set
map_scan(): Map scanned predictions, or training-network coverage
param_atmospheric(): Estimate Atmospheric Parameters Related to Evapotranspiration
param_radiation(): Estimate Basic Solar-Radiation Parameters
param_temperature(): Estimate Thermal Parameters
phenology_templates() show_phenology(): List Built-in Phenology Templates
planting_window_table(): Tabulate scan predictions against planting date
plot(<sim_W>): Diagnostic Plot for a Simulated Envirome
plot(<sim_met>): Diagnostic Plot for a Simulated MET
plot_planting_window(): Plot genotype performance across candidate planting dates
print(<T_matrix>): Print an Envirotype (T) Matrix
print(<kernel_set>): Print a kernel set
print(<scan_untested_envs>): Print a Scan of Untested Environments
print(<sim_W>): Print a Simulated Envirome
print(<sim_envirome>): Print a Simulated Envirome Diversity Matrix
print(<sim_markers>): Print a Simulated Marker Matrix
print(<sim_met>): Print a Simulated Multi-Environment Trial
print(<soil_gmm>): Print a soil_gmm object
processWTH(): Enrich a get_weather() Output with Derived Parameters
project_risk(): Compare Envirotype Risk Between Baseline and a Climate Scenario
read_progress_log(): Inspect the Progress Log of a Checkpointed Run
restart_from_log(): Resume an Interrupted get_weather() or get_soil() Run
scan_spatial_table(): Join scan predictions to coordinates
scan_untested_envs(): Predict Trained Genotypes at Untested Environments Without Refitting
sim_W(): Simulate an Envirome with a Known Relationship to an Environment Correlation
sim_W_grid(): Sweep Envirome Size and Quality (Power Analysis)
sim_envirome(): Simulate an Envirome with a Controllable Environmental Diversity
sim_markers(): Simulate a Marker Matrix with a Controllable Genetic Diversity
sim_met(): Simulate a Multi-Environment Trial with a Known Genetic Architecture
sim_met_C(): Simulate a q x q Genetic Correlation Among Environments
soil_classification(): Delineate Soil Zones with a Gaussian Mixture Model
summaryWTH(): Basic Summary Statistics for Environmental Data
summary_water_balance(): Summarise Water-Balance Output by Environment or Growth Stage
tpe_weights(): Target Population of Environments (TPE) Weights
truncate_gxe_kernel(): Truncate the GxE kernel to its structural rank.
undecompose_kernels(): Drop cached decompositions
varcomp_summary(): Validated variance components, heritabilities and genetic correlation.
water_balance(): Daily Soil Water Balance and Crop Water-Stress Indices