This package is considered a duplicate. The official version of this package is found at:https://ashesitr.r-universe.dev/reservr

Package: reservr 0.0.3

Alexander Rosenstock

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:Alexander Rosenstock [aut, cre, cph]

reservr_0.0.3.tar.gz
reservr_0.0.3.tar.gz(r-4.7-arm64)reservr_0.0.3.tar.gz(r-4.7-x86_64)reservr_0.0.3.tar.gz(r-4.6-arm64)reservr_0.0.3.tar.gz(r-4.6-x86_64)
reservr_0.0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
reservr/json (API)
NEWS

# Install 'reservr' in R:

install.packages('reservr', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ashesitr/reservr/issues

Pkgdown/docs site:https://ashesitr.github.io

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

On CRAN:

openblas cpp openmp

3.29 score 13 scripts 632 downloads 68 exports 43 dependencies

Last updated from:fa7ed11875. Checks:5 OK, 1 ERROR. Indexed: no.

Target	Result	Time
linux-devel-arm64	OK	321
linux-devel-x86_64	OK	393
source / vignettes	ERROR	380
linux-release-arm64	OK	266
linux-release-x86_64	OK	407
wasm-release	OK	184

Exports:as_params as_trunc_obs blended_transition blended_transition_inv callback_adaptive_lr callback_debug_dist_gradients dgpd dist_bdegp dist_beta dist_binomial dist_blended dist_dirac dist_discrete dist_empirical dist_erlangmix dist_exponential dist_gamma dist_genpareto dist_genpareto1 dist_lognormal dist_mixture dist_negbinomial dist_normal dist_pareto dist_poisson dist_translate dist_trunc dist_uniform dist_weibull dpareto dsoftmax fit fit_blended fit_dist fit_dist_direct fit_dist_start fit_erlang_mixture fit_mixture flatten_bounds flatten_params flatten_params_matrix inflate_params integrate_gk interval interval_intersection interval_union is.Distribution is.Interval k_matrix pgpd plot_distributions ppareto prob_report qgpd qpareto repdel_obs rgpd rpareto softmax tf_compile_model tf_initialise_model trunc_obs truncate_claims truncate_obs weighted_median weighted_moments weighted_quantile weighted_tabulate

Dependencies:assertthat backports base64enc BH cli config dotty fastmap generics glue here jsonlite keras3 lattice lifecycle magrittr Matrix matrixStats nloptr numDeriv png processx ps purrr R6 rappdirs Rcpp RcppArmadillo RcppParallel RcppTOML reticulate rlang rprojroot rstudioapi tensorflow tfautograph tfruns tidyselect vctrs whisker withr yaml zeallot

Vignettes and/or manual for this package failed to build, some documents may be unavailable. Please inspect thebuild logs for more information.

Fitting Distributions and Neural Networks to Censored and Truncated Data: The R Package reservr

Alexander Rosenstock

Rendered fromjss_paper.Rmdusingknitr::rmarkdownon Jun 15 2026.

Last update: 2024-06-25
Started: 2024-06-25

TensorFlow Integration

Rendered fromtensorflow.Rmdusingknitr::rmarkdownon Jun 15 2026.

Last update: 2024-06-25
Started: 2022-12-09

Working with Distributions

Rendered fromdistributions.Rmdusingknitr::rmarkdownon Jun 15 2026.

Last update: 2022-12-09
Started: 2022-12-09

Help page	Topics
Convert TensorFlow tensors to distribution parameters recursively	as_params
Transition functions for blended distributions	blended_transition blended_transition_inv
Keras Callback for adaptive learning rate with weight restoration	callback_adaptive_lr
Callback to monitor likelihood gradient components	callback_debug_dist_gradients
Construct a BDEGP-Family	dist_bdegp
Beta Distribution	dist_beta
Binomial Distribution	dist_binomial
Blended distribution	dist_blended
Dirac (degenerate point) Distribution	dist_dirac
Discrete Distribution	dist_discrete
Empirical distribution	dist_empirical
Erlang Mixture distribution	dist_erlangmix
Exponential distribution	dist_exponential
Gamma distribution	dist_gamma
Generalized Pareto Distribution	dist_genpareto dist_genpareto1
Log Normal distribution	dist_lognormal
Mixture distribution	dist_mixture
Negative binomial Distribution	dist_negbinomial
Normal distribution	dist_normal
Pareto Distribution	dist_pareto
Poisson Distribution	dist_poisson
Tranlsated distribution	dist_translate
Truncated distribution	dist_trunc
Uniform distribution	dist_uniform
Weibull Distribution	dist_weibull
Base class for Distributions	Distribution
Fit a Blended mixture using an ECME-Algorithm	fit_blended
Fit a general distribution to observations	fit.Distribution fit_dist fit_dist_direct
Find starting values for distribution parameters	fit_dist_start fit_dist_start.MixtureDistribution
Fit an Erlang mixture using an ECME-Algorithm	fit_erlang_mixture
Fit a generic mixture using an ECME-Algorithm	fit_mixture
Fit a neural network based distribution model to data	fit.reservr_keras_model
Flatten / Inflate parameter lists / vectors	flatten_bounds flatten_params flatten_params_matrix inflate_params
The Generalized Pareto Distribution (GPD)	dgpd GenPareto pgpd qgpd rgpd
Adaptive Gauss-Kronrod Quadrature for multiple limits	integrate_gk
Intervals	interval is.Interval
Convex union and intersection of intervals	interval-operations interval_intersection interval_union
Test if object is a Distribution	is.Distribution
Cast to a TensorFlow matrix	k_matrix
The Pareto Distribution	dpareto Pareto ppareto qpareto rpareto
Plot several distributions	plot_distributions
Predict individual distribution parameters	predict.reservr_keras_model
Determine probability of reporting under a Poisson arrival Process	prob_report
Quantiles of Distributions	quantile.Distribution
Soft-Max function	dsoftmax softmax
Compile a Keras model for truncated data under dist	tf_compile_model
Initialise model weights to a global parameter fit	tf_initialise_model
Define a set of truncated observations	as_trunc_obs repdel_obs truncate_obs trunc_obs
Truncate claims data subject to reporting delay	truncate_claims
Compute weighted moments	weighted_moments
Compute weighted quantiles	weighted_median weighted_quantile
Compute weighted tabulations	weighted_tabulate

Package: reservr 0.0.3

reservr: Fit Distributions and Neural Networks to Censored and Truncated Data

Fitting Distributions and Neural Networks to Censored and Truncated Data: The R Package reservr

TensorFlow Integration

Working with Distributions

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)