Package index • lossratio

Input layer

Validation and grain helpers for raw experience data. as_triangle() already runs the required checks and coercions inline — these helpers are exposed for users who want to validate or enrich without building a triangle.

derive_grain_columns(): Derive monthly / quarterly / semi-annual / annual grain columns
validate_triangle(): Validate triangle structure before building a development

Aggregation builders

Three frameworks for viewing the same long-format experience data — cohort × dev (Triangle), calendar period (Calendar), or portfolio total (Total).

as_triangle(): Coerce experience data to a Triangle object
as_calendar(): Coerce experience data to a Calendar object
as_total(): Coerce experience data to a Total object

Link table

Long-format intermediate underlying both the chain-ladder (ATA) and exposure-driven (ED) workflows. Built once, summarised differently via summary.Link()’s model argument.

as_link(): Coerce a Triangle to a Link object
summary(<Link>): Summarise a Link table

Estimation

Models that produce full projections on a Triangle. Base algorithms: fit_cl (chain ladder / multiplicative), fit_ed (exposure-driven / additive), fit_sa (stage-adaptive composition of ED + CL anchored on a maturity point). ELR-based reserve methods: fit_bf (Bornhuetter-Ferguson with external prior), fit_cc (Cape Cod with data-pooled ELR estimate). Role dispatchers: fit_loss (loss-side ed/cl/sa/bf/cc), fit_premium (premium-side ed/cl). Composition: fit_ratio (loss-ratio umbrella with delta-method SE). All return an object carrying a $full projection table.

fit_cl(): Fit chain ladder projection from a Triangle object
fit_ed(): Fit ED intensity factors
fit_sa(): Fit stage-adaptive (SA) loss projection on a Triangle
fit_bf(): Bornhuetter-Ferguson projection
fit_cc(): Cape Cod projection (Stanard 1985)
fit_loss(): Fit a loss projection on a Triangle
fit_premium(): Fit a chain ladder projection on the premium triangle
fit_ratio(): Fit loss ratio projection model

Factor diagnostics

Per-link factor estimation at the factor level. Both fit_ata (multiplicative ATA factors f_k) and fit_intensity (ED-style additive intensities g_k) return per-link factors with standard errors and diagnostic stats — without producing a full projection. fit_ata feeds into fit_cl, detect_maturity(), and the SA stage transition in fit_ratio; fit_intensity feeds into fit_ed and is the ED counterpart diagnostic.

fit_ata(): Fit age-to-age development factors
fit_intensity(): Fit per-link ED intensity factors

Cell-selection diagnostics

Decide which cells of the triangle to use for estimation. detect_maturity works along the dev axis (link beyond which ATA factors are stable); detect_regime works along the cohort axis (structural break across underwriting cohorts). The *_at() / *_spec() helpers build manual / lazy-detect input objects for the maturity / loss_regime / premium_regime arguments of the fit functions.

detect_maturity(): Find ata maturity by group
detect_regime() print(<Regime>) summary(<Regime>) print(<summary.Regime>): Detect structural regime shifts across underwriting cohorts
maturity_at(): Construct a Maturity object from manually specified maturity points
maturity_spec(): Build a lazy maturity detection spec
regime_at(): Construct a Regime object from manually specified regime changes
regime_spec(): Build a lazy regime detection spec

Bootstrap

Cohort × dev standard-error decomposition via simulation (Pythagorean split into parameter and process components). Returned object is consumed by fit_loss / fit_premium / fit_ratio through their bootstrap argument to replace analytical SE / CI with empirical counterparts.

bootstrap(): Bootstrap a Triangle

Projection diagnostic

Operates on a fitted RatioFit, not on the raw triangle. Locates the valuation depth $v$ at which the projected ultimate loss ratio stops revising under a dual criterion (predictive revision below noise threshold AND cross-cohort dispersion small, sustained over M consecutive valuations).

detect_convergence(): Find the development period at which the loss ratio estimate stabilises

Backtest

Hold out the latest calendar diagonals from a triangle, refit, and compare projections against the withheld actuals.

backtest() print(<Backtest>) summary(<Backtest>) print(<summary.Backtest>): Backtest a loss / premium / loss-ratio projection on existing data

Visualisation

plot() (base generic) and plot_triangle() (lossratio generic) dispatch on the object class. render() is a console table renderer for data frames and Triangle objects.

plot_triangle(): Triangle plot generic
plot(<ATAFit>): Plot an ata fit
plot(<BFFit>): Plot a Bornhuetter-Ferguson fit
plot(<Backtest>): Plot a backtest object
plot(<CCFit>): Plot a Cape Cod fit
plot(<CLFit>): Plot a chain ladder fit
plot(<Calendar>): Plot calendar-based development statistics
plot(<Convergence>): Plot the Convergence diagnostic
plot(<EDFit>): Plot an ED fit
plot(<IntensityFit>): Plot an Intensity fit
plot(<Link>): Plot link-factor diagnostics
plot(<PremiumFit>): Plot an premium fit
plot(<RatioFit>): Plot a loss ratio fit
plot(<Regime>): Plot a cohort regime detection result
plot(<RegimeOptimalWindow>): Plot change-count vs window with the elbow marker
plot(<SAFit>): Plot a stage-adaptive fit
plot(<Total>): Plot a Total object as a per-group bar chart
plot(<Triangle>): Plot development trajectories with optional summary overlay
plot(<TriangleValidation>): Plot a TriangleValidation result
plot_triangle(<ATAFit>): Triangle heatmap for an ata fit
plot_triangle(<BFFit>): Plot a Bornhuetter-Ferguson fit as a triangle table
plot_triangle(<Backtest>): Triangle heatmap of backtest A/E Error
plot_triangle(<CCFit>): Plot a Cape Cod fit as a triangle table
plot_triangle(<CLFit>): Plot a chain ladder fit as a triangle table
plot_triangle(<EDFit>): Triangle heatmap for an ED fit
plot_triangle(<IntensityFit>): Triangle heatmap for an Intensity fit
plot_triangle(<Link>): Plot a Link object as a triangle heatmap
plot_triangle(<PremiumFit>): Plot an premium fit as a triangle table
plot_triangle(<RatioFit>): Plot loss ratio projection as a triangle heatmap
plot_triangle(<SAFit>): Plot a stage-adaptive fit as a triangle table
plot_triangle(<Triangle>): Plot development values as a triangle table
plot_triangle(<TriangleValidation>): Triangle-heatmap view of dev-sequence gaps
render() print(<Triangle>): Render a tabular object as a compact console table

Other S3 methods

print / summary / longer methods registered on package classes.

backtest() print(<Backtest>) summary(<Backtest>) print(<summary.Backtest>): Backtest a loss / premium / loss-ratio projection on existing data
detect_regime() print(<Regime>) summary(<Regime>) print(<summary.Regime>): Detect structural regime shifts across underwriting cohorts
print(<ATAFit>): Print an ATAFit object
print(<ATASummary>): Print method for ATASummary
print(<BFFit>): Print method for BFFit
print(<BootstrapTriangle>): Print method for BootstrapTriangle
print(<CCFit>): Print method for CCFit
print(<CLFit>): Print a CLFit object
print(<EDFit>): Print an EDFit object
print(<EDSummary>): Print method for EDSummary
print(<IntensityFit>): Print method for IntensityFit
print(<LossFit>): Print method for LossFit
print(<PremiumFit>): Print method for PremiumFit
print(<RatioFit>): Print an RatioFit object
print(<SAFit>): Print method for SAFit
render() print(<Triangle>): Render a tabular object as a compact console table
summary(<ATAFit>): Summary method for ATAFit
summary(<BFFit>): Summary method for BFFit
summary(<CCFit>): Summary method for CCFit
summary(<CLFit>): Summary method for CLFit
summary(<Calendar>): Summarise calendar-development statistics (Mean, Median, Weighted)
summary(<EDFit>): Summary method for EDFit
summary(<IntensityFit>): Summary method for IntensityFit
summary(<Link>): Summarise a Link table
summary(<LossFit>): Summary method for LossFit
summary(<PremiumFit>): Summary method for PremiumFit
summary(<RatioFit>): Summary method for RatioFit
summary(<SAFit>): Summary method for SAFit
summary(<Total>): Summarise a Total object
summary(<Triangle>): Summarise development statistics (Mean, Median, Weighted)

Helpers

longer(): Reshape an object to long form (S3 generic)
mask_triangle(): Mask the last N calendar diagonals from a Triangle

Datasets

experience: Sample loss experience data