Package index • prospicio

Distributions

distribution(): Abstract parent of every distribution class
lognormal(): Lognormal distribution
lognormal_from_mean_cv(): Lognormal distribution from its mean and coefficient of variation
grid_distribution(): Distribution on an evenly spaced grid
discretize(): Discretize a severity onto a grid
map_grid(): Transform a grid distribution
sampled(): Distribution of equally weighted draws
gamma_distribution() gamma_from_mean_cv() gamma_from_mean_dispersion(): Gamma distribution
tweedie() tweedie_from_poisson_gamma(): Tweedie distribution
weibull_distribution(): Weibull distribution
loglogistic_distribution(): Loglogistic distribution
mixture_distribution(): Mixture of severities
custom_distribution(): Custom severity from your own distribution function
dist_to_json() dist_from_json(): Save and load distributions as JSON
log_density(): Log density

Large-loss severities

pareto(): Single-parameter Pareto distribution
piecewise_pareto(): Piecewise Pareto distribution
log_affine_pareto(): Log-affine local Pareto distribution
generalized_pareto() generalized_pareto_riegel(): Generalized Pareto severity
pareto_fit() piecewise_pareto_fit() generalized_pareto_fit(): Maximum likelihood fits to large losses
local_pareto_to_piecewise(): Convert a local Pareto distribution to piecewise Pareto
survival() layer_variance(): Survival function and layer variance
local_alpha(): Local Pareto alpha

Claim counts

poisson_count(): Poisson claim counts
negative_binomial_count(): Negative binomial claim counts
negative_binomial_count_from_mean_variance(): Negative binomial from its mean and variance
binomial_count(): Binomial claim counts
claim_count(): Claim count by mean and dispersion
pmf(): Claim-count probability mass

Distribution functions

cdf(): Distribution function
variance(): Variance of a distribution
draws(): Reproducible random draws
lev() stop_loss() layer(): Limited expected value, stop-loss and layer
VaR() TVaR(): Value at risk and tail value at risk

Predictive distributions

predictive_distribution(): Joint predictive distribution
blend_predictive(): Blend predictive distributions
join_predictive() reorder_groups(): Join predictive distributions into a portfolio
marginal(): One component of a predictive distribution
total(): Total over all components
draw_matrix(): Draw matrix of a predictive distribution
provenance(): Where a result came from

Aggregate loss

compound_distribution(): Compound (aggregate) loss distribution
simulate_events(): Simulated years of losses
events_from_years(): Years of losses from elsewhere
event_sums_insured(): Sums insured of one year's losses
event_times(): Times of one year's losses
with_uniform_times(): Date events uniformly over the year
with_seasonal_times(): Date events by a season
event_set(): Simulated years of losses (class)
events(): One simulated year's losses
event_counts(): Number of losses in each simulated year

Reinsurance

xol_layer(): Excess-of-loss reinsurance layer
with_loss_corridor(): Loss corridor
quota_share(): Quota share
aggregate_stop_loss(): Aggregate stop-loss
surplus_treaty(): Surplus treaty
ceded(): Ceded loss for one year
ceded_by_event(): Ceded loss per event
reinstatement_premium(): Reinstatement premium for one year
reinsurance_tower(): Reinsurance tower
inuring_tower(): Reinsurance tower with inuring order
tower_to_json() tower_from_json(): Save and load reinsurance towers as JSON
tower_ceded(): Ceded loss of each layer in a tower for one year
apply_tower(): Apply a reinsurance tower to simulated years
tower_on_grid(): Reinsurance tower on the aggregate grid

Pricing

collective_model(): Collective risk model
collective_simulate(): Simulate a collective model
excess_frequency(): Expected excess frequency
ilf() loss_elimination_ratio() pareto_extrapolation() alpha_between_layers() alpha_between_frequency_and_layer() alpha_between_frequencies(): Layer rating
risk_loaded_price() price_portfolio(): Risk-loaded prices from simulated losses
mbbefd() swiss_re_curve() exposure_curve() exposure_layer_share() severity_exposure_curve() rate_quantile(): MBBEFD exposure curves
tabulated_curve(): Tabulated exposure curve
risk_profile() profile_simulate() profile_layer_loss() profile_surplus_loss(): Risk profile for property per-risk business
match_tower(): Match a reinsurance tower
fit_pml_curve(): Fit a PML curve
fit_references(): Fit references
tower_model(): Tower model

Risk measures

distortion(): Distortion risk measure
distortion_g() distortion_weights(): Distortion function and rank weights
risk_measure(): Distortion risk measure of a distribution
allocate(): Allocate a risk measure to components
capital_allocation(): Capital allocation and diversification
entropic_risk() esscher_premium(): Exponential-utility risk measures
marginal_expected_shortfall() esscher_allocation() covar(): Systemic risk contributions

Models

glm_fit(): Fit a generalized linear model
glm_model(): Fitted GLM (class)
robust_vcov(): Sandwich covariance of a GLM
bayes_glm_fit(): Fit a Bayesian GLM by NUTS
bayes_glm_model(): Sampled Bayesian GLM (class)
bayes_loo(): PSIS-LOO of a Bayesian GLM
save_model() load_model(): Save and load a model
elastic_net_fit(): Fit an elastic-net GLM
elastic_net_model(): Fitted elastic net (class)
elastic_net_cv(): Cross-validate an elastic net
gam_fit(): Fit a generalized additive model
gam_model(): Fitted GAM (class)
booster_fit(): Fit gradient-boosted trees
booster_model(): Fitted gradient-boosted trees (class)
simulate_from_means(): Predictive distribution from fitted means
predict_quantiles(): Quantile sets from several quantile boosters
predict_dispersion(): Dispersion per row of a boosted model
predict_distribution(): Joint predictive distribution of a fitted model
family_deviance() gini_index() lift_table() crps_draws() pinball_loss() log_score() pit_values() ks_uniform(): Model metrics
k_fold() group_k_fold() time_ordered(): Resampling splits
cross_validate() grid_search() random_search(): Cross-validation and hyperparameter search
compare_models(): Compare models on the same splits
actual_vs_expected(): Monitor a model: actual against expected by period
mcmc_diagnostics(): MCMC convergence diagnostics
elpd_loo() elpd_waic(): Expected log pointwise predictive density
stacking_weights() pseudo_bma_weights(): Model weights for blending: stacking and pseudo-BMA
bayes_stacking() hierarchical_stacking() pooling_scales(): Bayesian and hierarchical stacking
stacking_fit(): Posterior stacking weights (class)

Dependence

copula() gaussian_copula() t_copula() archimedean_copula(): Copulas
copula_sample(): Draw uniforms from a copula
copula_simulate(): Simulate marginals joined by a copula
iman_conover(): Reorder draws to a target correlation (Iman-Conover)

Extreme value tails

pot_tail(): Peaks-over-threshold tail
mean_excess() hill_estimator(): Tail diagnostics
gpd_fit(): Generalized Pareto fit

Reserving

triangle(): Loss triangle
subset.triangle: Select segments and columns of a triangle by name
aggregate.triangle: Sum a triangle over keys
triangle_views: View, summarise and print a triangle
to_incremental() to_cumulative(): Incremental and cumulative triangles
latest_diagonal(): Latest diagonal of a triangle
link_ratios(): Link ratios of a triangle
grain(): Change the grain of a triangle
chain_ladder_fit() chain_ladder(): Chain ladder
mack_fit() mack(): Mack chain ladder
claims_development_result(): Claims development result: the one-year view of a Mack fit
tail_constant(): Constant tail factor
tail_curve(): Curve-fitted tail
tail_bondy(): Bondy tail
tail_log_linear(): R ChainLadder's log-linear tail
expected_loss_fit() expected_loss() bornhuetter_ferguson() benktander(): Expected loss, Bornhuetter-Ferguson and Benktander
cape_cod_fit() cape_cod(): Cape Cod
odp_bootstrap_fit() odp_bootstrap(): ODP bootstrap
mack_bootstrap_fit() mack_bootstrap(): Bootstrap of Mack's model
one_year_fit() odp_one_year() mack_one_year(): Simulated one-year view
clark_fit() clark_ldf() clark_cape_cod() growth(): Clark's growth-curve methods
totals_frame() development_frame() segment(): Long results of a fit over every segment