The lossrx from jimbrig

Feature: add doc_data function (internal)

remove from data-raw and add to R

Create an Overview of Loss Reserving Vignette

Bugfix: Fix R CMD CHECK Issues

Tasks:

Output:

-- R CMD check results ----------------------------------------------- lossrx 0.0.2 ----
Duration: 1m 38.2s

> checking package dependencies ... NOTE
  Imports includes 24 non-default packages.
  Importing from so many packages makes the package vulnerable to any of
  them becoming unavailable.  Move as many as possible to Suggests and
  use conditionally.

> checking for hidden files and directories ... NOTE
  Found the following hidden files and directories:
    inst/spawnctl/.env
  These were most likely included in error. See section 'Package
  structure' in the 'Writing R Extensions' manual.

> checking installed package size ... NOTE
    installed size is  5.4Mb
    sub-directories of 1Mb or more:
      data   2.0Mb
      doc    2.5Mb

> checking R code for possible problems ... NOTE
  view_claim_history: no visible binding for global variable
    'claims_transactional'
  view_claim_history: no visible global function definition for
    'case_when'
  view_claim_history: no visible binding for global variable 'Transaction
    ID'
  view_claim_history: no visible binding for global variable 'Paid
    Change'
  view_claim_history: no visible binding for global variable 'Case
    Reserve Change'
  view_claim_history: no visible binding for global variable 'Reported
    Change'
  view_claim_history: no visible binding for global variable 'Status
    Details'
  view_claim_history: no visible binding for global variable 'Status'
  view_claim_history: no visible binding for global variable 'Transaction
    Details'
  view_claim_history: no visible binding for global variable 'Transaction
    Date'
  Undefined global functions or variables:
    Case Reserve Change Paid Change Reported Change Status Status Details
    Transaction Date Transaction Details Transaction ID case_when
    claims_transactional

Create pkgdown

Fix code scanning alert - unexpected end of input

Tracking issue for:

https://github.com/jimbrig/lossrx/security/code-scanning/221

Configuration: Re-structure R Folder with Proper File Prefix Groupings

actuary
meta
db
util
stat
etc.

Database: Create Preliminary Database Schema for Claims Data

Tables:

claims
occurrences
policies
claimants
locations
vehicles
tpas
companies
segments
departments
coverages
evaluations

With various foreign key constraints throughout.

Feature: Add full suite of Interpolation functions

Should support:

Linear
Exponential
Dbl Exponential

Note do not utilize curve fitting or approx / curve functions, instead want to use nearest neighbors to interpolate from given selections.

Data: Re-structure data-raw datapreparation scripts and workflows

Add new data utils

pull_unique

Documentation: Create a Data Overview Vignette

Should describe the loss and exposure data as well as the database schema

Enhancement: Potential Enhancement for Interpolation Functions

From mages/ChainLadder#26 (comment):

interpolate_ldfs <- function(observed_ldf_df, interp_age){
  # observed_ldf_df <- sel_data
  # interp_age <- 9

  ## At some age ('ldf_2_one') all selected 'ldfs' = 1 for all 'ages' >= ldf_2_one 
  ## Hence our 'pct_ibnr' -> inf for all 'ages' >= 'ldf_2_one', 
  ## and recieve error when fit linear model
  ## Test if 'interp_age' >= 'ldf_2_one' then return 1. Else proceed to interpolation 

  ldf_2_one <- min(observed_ldf_df$age[observed_ldf_df$ldf == 1]) 
  #the first age which the ldf is 1

  if (interp_age >= ldf_2_one) {
    return(1)
  } else {

    ## Exclude rows from 'observed_ldf_df' where ldf == 1  
    observed_ldf_df <- observed_ldf_df[observed_ldf_df$ldf != 1,]

    observed_ldf_df <- observed_ldf_df %>% 
      dplyr::mutate(pct_ibnr = 1 - (1 / ldf)) 

    ## Fit weibull model
    weibul_model <- lm(log(-log(observed_ldf_df$pct_ibnr)) ~ 
        log(observed_ldf_df$age)) # Boor Eq (8)

    ## Define the age of the ldfs above and below the interpulated age  
    age_below <- interp_age - (interp_age %% 12) 
    age_above <- interp_age + (12 - (interp_age %% 12))


    fit_below <- exp(-exp(weibul_model$coefficients[1] + 
        weibul_model$coefficients[2] * log(age_below))) 
    fit_above <- exp(-exp(weibul_model$coefficients[1] + 
        weibul_model$coefficients[2] * log(age_above)))
    fit_at <- exp(-exp(weibul_model$coefficients[1] + 
        weibul_model$coefficients[2] * log(interp_age))) 

    ## Selected ldfs at age_below and age_above
    observed_below <- observed_ldf_df$pct_ibnr[observed_ldf_df$age == age_below]
    observed_above <- observed_ldf_df$pct_ibnr[observed_ldf_df$age == age_above]

    ## observed_below is na when age_below < 12. Set equal to 1
    if(interp_age < 12) observed_below = 1

    ## variables to make extrapolation easier
    max_obs_age <- max(observed_ldf_df$age)

    if(interp_age < max_obs_age){   # interpolate
      interp_along_curve <- observed_below + (((fit_at - fit_below) / 
          (fit_above - fit_below)) * 
          (observed_above - observed_below))
    }  else{                           # extrapolate

      fit_at_max_age <- exp(-exp(weibul_model$coefficients[1] + 
          weibul_model$coefficients[2] * 
          log(max_obs_age)))

      obs_at_max_age <- observed_ldf_df$pct_ibnr[observed_ldf_df$age == 
          max_obs_age]

      interp_along_curve <- fit_at * obs_at_max_age / fit_at_max_age
    }
    ## Calculate ldf
    implied_ldf <- 1 / (1 - interp_along_curve)
    ## Adjust for age < 12 months 
    implied_full_ay_ldf <- ifelse(interp_age >= 12, implied_ldf, 
      implied_ldf * 12 / interp_age) 

    return(implied_full_ay_ldf)

  }}

Create package hex sticker/logo

Fix code scanning alert - 1:nrow(...) is likely to be wrong in the empty edge case. Use seq_len(nrow(...)) instead.

Tracking issue for:

https://github.com/jimbrig/lossrx/security/code-scanning/128

run initial tests, checks, and build locally

Tests: Add unit tests for simulate function

Database: Add SQL, CSV, and YAML Schema Files to inst / data-raw

SQL: Schema Only (tables, extensions, custom enum's, databases, schemas, etc.)
CSV: for seeding the generated tables
YML: Structural and can be used by javascript

Documentation: Create Vignette for Actuarial Loss Distributions

API: Create an initial skeleton Swagger/OpenAPI YAML Specification Configuration File

Utilize lossreserving API - SwaggerHub as a starting point.

Feature: Add comprehensive db create_tbl function

#' Create Table
#'
#' Creates a database table given a connection, table name, and:
#' - SQL file specifying the table's schema
#' - CSV file to seed the table's values