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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>.

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VersionRepositoryFileSize
0.0.3 rolling linux/jammy R-4.5 reservr_0.0.3.tar.gz 2.4 MiB
0.0.3 rolling linux/noble R-4.5 reservr_0.0.3.tar.gz 2.4 MiB
0.0.3 rolling source/ R- reservr_0.0.3.tar.gz 693.5 KiB
0.0.3 latest linux/jammy R-4.5 reservr_0.0.3.tar.gz 2.4 MiB
0.0.3 latest linux/noble R-4.5 reservr_0.0.3.tar.gz 2.4 MiB
0.0.3 latest source/ R- reservr_0.0.3.tar.gz 693.5 KiB
0.0.3 2026-04-26 source/ R- reservr_0.0.3.tar.gz 693.5 KiB
0.0.3 2026-04-23 source/ R- reservr_0.0.3.tar.gz 693.5 KiB
0.0.3 2026-04-09 windows/windows R-4.5 reservr_0.0.3.zip 2.8 MiB
0.0.3 2025-04-20 source/ R- reservr_0.0.3.tar.gz 693.5 KiB

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