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 |