# Fit Distributions and Neural Networks to Censored and Truncated Data

## Documentation for package ‘reservr’ version 0.0.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

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