Package: hypermodR 0.0.0.9001

Raban Heller

hypermodR: Hyperspectral Modelling and Validation

Interpretable and black-box hyperspectral modelling with explicit spectral, spatial and temporal data contracts, grouped validation, reproducible evaluation, optional isolated model runtimes, and an interactive research workbench. Model and instrument qualification is recorded separately from interface availability.

Authors:Raban Heller [aut, cre]

hypermodR_0.0.0.9001.tar.gz
hypermodR_0.0.0.9001.zip(r-4.7-any)hypermodR_0.0.0.9001.zip(r-4.6-any)hypermodR_0.0.0.9001.zip(r-4.5-any)
hypermodR_0.0.0.9001.tgz(r-4.6-any)hypermodR_0.0.0.9001.tgz(r-4.5-any)
hypermodR_0.0.0.9001.tar.gz(r-4.7-any)hypermodR_0.0.0.9001.tar.gz(r-4.6-any)
hypermodR_0.0.0.9001.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
hypermodR/json (API)

# Install 'hypermodR' in R:
install.packages('hypermodR', repos = c('https://cttir.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/cttir/hypermodr/issues

On CRAN:

Conda:

4.22 score 80 exports 4 dependencies

Last updated from:e81c070b19. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK225
source / vignettesOK325
linux-releaseOK237
macos-releaseOK190
macos-oldrelOK199
windows-develOK282
windows-releaseOK205
windows-oldrelOK138
wasm-releaseOK165

Exports:hm_active_learninghm_admit_backendhm_annotation_contracthm_apphm_app_statehm_apply_calibrationhm_as_datasethm_as_hsi_cubehm_backend_capabilitieshm_backend_planhm_backend_statushm_bakehm_benchmark_protocolhm_bootstraphm_cachehm_cache_keyhm_calibratehm_cancel_jobhm_check_backendhm_comparehm_coordinateshm_datasethm_dual_stream_mhsihm_endpoint_contracthm_evaluatehm_examplehm_export_annotationshm_export_resulthm_extract_nativehm_fithm_from_tivishm_htchm_hyperfreehm_hypersigmahm_hypervisionhm_install_backendhm_instrument_profileshm_job_statushm_model_spechm_modelshm_physics_kernelhm_poolhm_portable_sequencehm_predicthm_predictionhm_prephm_prompthm_provenancehm_read_blockhm_read_runhm_recipehm_reliabilityhm_render_hsihm_resolve_backendhm_resolve_checkpointhm_resourceshm_save_runhm_sequencehm_spectrahm_spectral_libraryhm_spectralearthhm_spectralgpthm_splithm_stage_annotationshm_start_backendhm_stop_backendhm_streamhm_stream_cancelhm_stream_pushhm_submit_jobhm_taskhm_temporal_modelhm_tivita_profilehm_tunehm_verify_provenancehm_verify_registryhm_vllm_chathm_with_resourceshm_worker_requesthm_workflow

Dependencies:clidigestgenericsjsonlite

The Shiny workbench
Rendered fromshiny-workbench.Rmdusingknitr::rmarkdown

Last update: 2026-11-02
Started: 2026-10-25

Validation and reliability
Reconstruction coordinates
Rendered fromvalidation-and-reliability.Rmdusingknitr::rmarkdown

Last update: 2026-11-02
Started: 2026-10-25

Backends and installation
Rendered frombackends-and-installation.Rmdusingknitr::rmarkdown

Last update: 2026-11-02
Started: 2026-10-25

A grouped hyperspectral workflow
Rendered fromhypermodR.Rmdusingknitr::rmarkdown

Last update: 2026-10-28
Started: 2026-10-25

Conventional machine learning
Rendered frommachine-learning.Rmdusingknitr::rmarkdown

Last update: 2026-10-28
Started: 2026-10-25

Cubert video and temporal access
Rendered fromcubert-video.Rmdusingknitr::rmarkdown

Last update: 2026-10-28
Started: 2026-10-25

Data and interoperability
Rendered fromdata-and-interoperability.Rmdusingknitr::rmarkdown

Last update: 2026-10-28
Started: 2026-10-25

Deep and foundation models
Rendered fromdeep-and-foundation-models.Rmdusingknitr::rmarkdown

Last update: 2026-10-28
Started: 2026-10-25

Extending hypermodR
Rendered fromextending-hypermodR.Rmdusingknitr::rmarkdown

Last update: 2026-10-28
Started: 2026-10-25

Interpretable models
Supplied physical and residual models
Rendered frominterpretable-models.Rmdusingknitr::rmarkdown

Last update: 2026-10-28
Started: 2026-10-25

TIVITA workflows
Rendered fromtivita-workflows.Rmdusingknitr::rmarkdown

Last update: 2026-10-28
Started: 2026-10-25

Readme and manuals

Help Manual

Help pageTopics
Attach predictions to observation identitiesaugment.hm_fit
Inspect model and training scopeglance.hm_fit
Queue uncertain training-pool cases for annotation reviewhm_active_learning
Admit reviewed resolved metadata as an exact install planhm_admit_backend
Bind an annotation project to its immutable export contracthm_annotation_contract
Open the local spectral modelling workbenchhm_app
Create an isolated workbench statehm_app_state
Apply a frozen calibrator without learning from new outcomeshm_apply_calibration
Convert a qualified companion objecthm_as_dataset hm_as_dataset.annot_image hm_as_dataset.annot_project hm_as_dataset.cuvis_measurement hm_as_dataset.cuvis_session hm_as_dataset.default hm_as_dataset.hsi_cube
Materialise one frame through hyperspectRhm_as_hsi_cube
Inspect local runtime capabilities without starting serviceshm_backend_capabilities
Plan an isolated runtime installationhm_backend_plan
Inspect a managed processhm_backend_status
Apply immutable training transformationshm_bake
Admit a bounded Spectralverse benchmark protocol without accessing datahm_benchmark_protocol
Paired whole-unit bootstrap of fixed predictionshm_bootstrap
Create independent in-memory cached valueshm_cache
Compute a complete cache identityhm_cache_key
Freeze temperature scaling and abstention using an inner held-out partitionhm_calibrate
Cancel only an owned modelling jobhm_cancel_job
Inspect installation and separately supplied parity evidencehm_check_backend
Compare matched predictions using paired whole-unit resamplinghm_compare
Map image coordinates through explicit geometryhm_coordinates
Construct a native spectral datasethm_dataset
Declare the dual_stream_mhsi upstream candidatehm_dual_stream_mhsi
Declare independent reference endpointshm_endpoint_contract
Evaluate predictions against an explicitly aligned referencehm_evaluate print.hm_evaluation summary.hm_evaluation
Generate an explicitly synthetic grouped spectral examplehm_example
Export grouped annotations through hyperannotatRhm_export_annotations
Export a reproducible workbench resulthm_export_result
Extract admitted native upstream image featureshm_extract_native
Fit a workflow using training indices onlycoef.hm_fit hm_fit print.hm_fit summary.hm_fit
Import an explicitly profiled TIVITA measurement listhm_from_tivis
Declare the htc upstream candidatehm_htc
Declare the hyperfree upstream candidatehm_hyperfree
Declare the hypersigma upstream candidatehm_hypersigma
Declare the hypervision upstream candidatehm_hypervision
Install declared files into an immutable user runtime directoryhm_install_backend
Inspect admitted instrument capabilitieshm_instrument_profiles
Poll a modelling jobhm_job_status
Declare a model specificationhm_model_spec
List candidate model familieshm_models
Declare supplied physical forward and inverse kernelshm_physics_kernel
Explicitly pool all valid spatial pixelshm_pool
Index a portable spectral sequence without repairing chronologyhm_portable_sequence
Predict with a fitted workflowhm_predict hm_predict.hm_fit predict.hm_fit
Validate model predictionshm_prediction plot.hm_prediction print.hm_prediction summary.hm_prediction
Fit preprocessing on training spectrahm_prep
Declare auxiliary language-model prompt provenancehm_prompt
Capture explicit reproducibility identitieshm_provenance
Read a bounded spatial blockhm_read_block hm_read_block,HMDataset-method
Read and verify a completed runhm_read_run
Declare preprocessing stepshm_recipe
Describe confidence bins and coverage-risk without claiming OOD validationhm_reliability
Render a declared three-band auxiliary imagehm_render_hsi
Resolve official binary metadata into an exact reviewable runtime manifesthm_resolve_backend
Resolve an immutable public Hugging Face checkpoint without downloading weightshm_resolve_checkpoint
Declare bounded execution resourceshm_resources
Save an immutable run with an atomic completion receipthm_save_run
Construct a spectral sequencehm_sequence
Read native spectra without spatial reductionhm_spectra hm_spectra,HMDataset-method
Declare an extinction or response libraryhm_spectral_library
Declare the spectralearth upstream candidatehm_spectralearth
Declare the spectralgpt upstream candidatehm_spectralgpt
Plan grouped resamplinghm_split
Stage a bound integer prediction for human reviewhm_stage_annotations
Start a process belonging to an installed runtimehm_start_backend
Stop only an owned runtime processhm_stop_backend
Bounded causal frame streaminghm_stream
Cancel a frame streamhm_stream_cancel
Push ordered frames into a streamhm_stream_push
Submit an isolated, cancellable modelling jobhm_submit_job
Declare a modelling taskhm_task
Declare a causal temporal referencehm_temporal_model
Declare an explicit TIVITA array profilehm_tivita_profile
Select parameters using only grouped inner training foldshm_tune
Verify source identities against a provenance receipthm_verify_provenance
Verify a signed registry releasehm_verify_registry
Query an explicitly configured OpenAI-compatible vLLM endpointhm_vllm_chat
Run an R expression in a bounded child processhm_with_resources
Exchange bounded typed arrays with an owned workerhm_worker_request
Assemble a modelling workflowhm_workflow
Validated spectral data containerHMDataset-class
Validated time-indexed spectral sequenceHMSequence-class
Summarize fitted coefficients in a standard tabletidy.hm_fit