Package: reservr 0.0.3.9000
reservr: Fit Distributions and Neural Networks to Censored and Truncated Data
Define distribution families and fit them to interval-censored and interval-truncated data, where the truncation bounds may depend on the individual observation. The defined distributions feature density, probability, sampling and fitting methods as well as efficient implementations of the log-density log f(x) and log-probability log P(x0 <= X <= x1) for use in 'TensorFlow' neural networks via the 'tensorflow' package. Allows training parametric neural networks on interval-censored and interval-truncated data with flexible parameterization. Applications include Claims Development in Non-Life Insurance, e.g. modelling reporting delay distributions from incomplete data, see Bücher, Rosenstock (2022) <doi:10.1007/s13385-022-00314-4>.
Authors:
reservr_0.0.3.9000.tar.gz
reservr_0.0.3.9000.zip(r-4.5)reservr_0.0.3.9000.zip(r-4.4)reservr_0.0.3.9000.zip(r-4.3)
reservr_0.0.3.9000.tgz(r-4.4-arm64)reservr_0.0.3.9000.tgz(r-4.4-x86_64)reservr_0.0.3.9000.tgz(r-4.3-x86_64)reservr_0.0.3.9000.tgz(r-4.3-arm64)
reservr_0.0.3.9000.tar.gz(r-4.5-noble)reservr_0.0.3.9000.tar.gz(r-4.4-noble)
reservr_0.0.3.9000.tgz(r-4.4-emscripten)reservr_0.0.3.9000.tgz(r-4.3-emscripten)
reservr.pdf |reservr.html✨
reservr/json (API)
NEWS
| # Install 'reservr' in R: |
| install.packages('reservr', repos = c('https://ashesitr.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/ashesitr/reservr/issues
Last updated 3 months agofrom:4405efdff7. Checks:OK: 1 NOTE: 8. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 23 2024 |
| R-4.5-win-x86_64 | NOTE | Sep 23 2024 |
| R-4.5-linux-x86_64 | NOTE | Sep 23 2024 |
| R-4.4-win-x86_64 | NOTE | Sep 23 2024 |
| R-4.4-mac-aarch64 | NOTE | Sep 23 2024 |
| R-4.4-mac-x86_64 | NOTE | Aug 24 2024 |
| R-4.3-win-x86_64 | NOTE | Sep 23 2024 |
| R-4.3-mac-x86_64 | NOTE | Sep 23 2024 |
| R-4.3-mac-aarch64 | NOTE | Sep 23 2024 |
Exports:as_paramsas_trunc_obsblended_transitionblended_transition_invcallback_adaptive_lrcallback_debug_dist_gradientsdgpddist_bdegpdist_betadist_binomialdist_blendeddist_diracdist_discretedist_empiricaldist_erlangmixdist_exponentialdist_gammadist_genparetodist_genpareto1dist_lognormaldist_mixturedist_negbinomialdist_normaldist_paretodist_poissondist_translatedist_truncdist_uniformdist_weibulldparetodsoftmaxfitfit_blendedfit_distfit_dist_directfit_dist_startfit_erlang_mixturefit_mixtureflatten_boundsflatten_paramsflatten_params_matrixinflate_paramsintegrate_gkintervalinterval_intersectioninterval_unionis.Distributionis.Intervalk_matrixpgpdplot_distributionspparetoprob_reportqgpdqparetorepdel_obsrgpdrparetosoftmaxtf_compile_modeltf_initialise_modeltrunc_obstruncate_claimstruncate_obsweighted_medianweighted_momentsweighted_quantileweighted_tabulate
Dependencies:assertthatbackportsbase64encBHcliconfigfastmapgenericsglueherejsonlitekeras3latticelifecyclemagrittrMatrixmatrixStatsnloptrnumDerivpngprocessxpspurrrR6rappdirsRcppRcppArmadilloRcppParallelRcppTOMLreticulaterlangrprojrootrstudioapitensorflowtfautographtfrunstidyselectvctrswhiskerwithryamlzeallot
Fitting Distributions and Neural Networks to Censored and Truncated Data: The R Package reservr
Rendered fromjss_paper.Rmdusingknitr::rmarkdownon Sep 23 2024.Last update: 2024-06-17
Started: 2023-11-02
TensorFlow Integration
Rendered fromtensorflow.Rmdusingknitr::rmarkdownon Sep 23 2024.Last update: 2024-06-15
Started: 2021-09-23
Working with Distributions
Rendered fromdistributions.Rmdusingknitr::rmarkdownon Sep 23 2024.Last update: 2022-06-02
Started: 2021-09-23
