Fit Distributions and Neural Networks to Censored and Truncated
Data
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Documentation for package ‘reservr’ version 0.0.2
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| 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 |