This package is very early in its development cycle.
Interested in developing loss reserving techniques in Julia? Consider contributing to this package. Open an issue, create a pull request, or discuss on the Julia Zulip's #actuary channel.
using ChainLadder
using CSV
using Test
using DataFrames
csv_data = ChainLadder.sampledata("raa")
raa = CSV.read(csv_data,DataFrame)
t = CumulativeTriangle(raa.origin,raa.development,raa.values)
lin = LossDevelopmentFactor(t)
s = square(t,lin)
total_loss(t,lin)
outstanding_loss(t,lin)
Load sample data
csv_data =ChainLadder.sampledata("raa")
raa = CSV.read(csv_data,DataFrame)
t = CumulativeTriangle(raa.origin,raa.development,raa.values)
Available datasets (courtesy of Python's chainladder):
abc
auto
berqsherm
cc_sample
clrd
genins
ia_sample
liab
m3ir5
mcl
mortgage
mw2008
mw2014
prism
quarterly
raa
tail_sample
ukmotor
usaa
usauto
xyz