Package index

Package overview

logmu logmu-package

Actuarial mortality experience analysis and modelling

Experience and valuation data

exp_data() val_data()

Create an exp_data or a val_data

is_exp_data() is_val_data()

Is x an exp_data or a val_data?

exp_start() exp_end()

Get experience period of exp_data

as_at()

Get the 'as at date' of a val_data

val_from_exp_data()

Create a val_data from an exp_data

`$<-`(<exp_data>) `$<-`(<val_data>)

Add or assign a data column

Mortality

is_mortality() end_age() log_mu() mortality()

Mortality-related functionality

mortality_const()

Create a constant mortality

mortality_table()

Create an mortality from a 2D age-period matrix of annual mortality rates

gompertz_mortality()

Create a Gompertz mortality with an age and a period slope

default_mortality()

The default mortality

Pronouns .i and .t

pronoun_expressions()

Parse a pronoun expression into an AST

variable() static_variable() is_variable() is_static_variable() value()

Variables: scalar functions of an individual and time

indicator() is_indicator() logical_value()

Indicators: a 0/1 function of an individual

is_logmu_function()

Test for a logmu function object

Choosing which records to count

band() bands() age() ages() duration() durations() period() periods() include() is_include() period_included()

Includes: a single time interval per individual

includes() is_includes() group_names()

Includes: a collection of includes

category() categories()

Categories: breaking a population down by a categorical field

Naming objects

name() `name<-`() group_name() `group_name<-`()

The name of an object

A/E analysis

aev()

Actual, expected and variance over experience data

create_aev() `[[`(<aev>) `$`(<aev>) `[`(<aev>)

Properties of an aev

is_aev()

Is an object an aev?

as.data.frame(<aev>)

Coerce an aev to a data frame

Charting an A/E

autoplot() plot(<aev>)

Chart an aev

theme_aev()

The chart theme, and how to resize a chart

Fitting and comparing models

model() is_model() print(<model>)

A proportional hazards mortality model

covariates() disjoint() is_covariates() is_disjoint()

Covariates: the fitted terms of a mortality model

fit() is_fit() coef(<fit>) vcov(<fit>) residuals(<fit>) summary(<fit>) print(<summary.fit>) print(<fit>)

Fit a mortality model by maximum weighted log-likelihood

compare_models() is_model_comparison() best_fit() summary(<model_comparison>) print(<summary.model_comparison>) print(<model_comparison>)

Compare mortality models by penalised log-likelihood

diagnose() is_diagnosis() print(<diagnosis>) plot(<diagnosis>)

Standard diagnostics for a fit, or for every candidate of a comparison

simulate(<mortality>)

Simulate experience data from a mortality

Running several analyses at once

batch()

Run several analyses over one dataset in a single pass

settings() is_settings() print(<logmu_settings>)

Settings for an analysis

Vector operations

vec_active_lanes() vec_active_tier()

SIMD active lanes and tier

vec_neg() vec_exp() vec_expm1() vec_log() vec_log1p() vec_m_from_q() vec_add() vec_sub() vec_mul() vec_div() vec_pow() vec_min() vec_max() vec_clamp()

Vector ops