# Stochastic reserving with R: ChainLadder 0.1.5-1 released

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Today we published version 0.1.5-1 of the **mages' blog**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

`ChainLadder`

package for R. It provides methods which are typically used in insurance claims reserving to forecast future claims payments.Claims development and chain-ladder forecast of the RAA data set using the Mack method |

Initially the package came with implementations of the Mack-, Munich- and Bootstrap Chain-Ladder methods. Since version 0.1.3-3 it also provides general multivariate chain ladder models by Wayne Zhang. Version 0.1.4-0 introduced new functions on loss development factor fitting and Cape Cod by Daniel Murphy following a paper by David Clark. Version 0.1.5-0 has added loss reserving models within the generalized linear model framework following a paper by England P. and Verrall R. (1999) implemented by Wayne Zhang.

For more details see the project web site: http://code.google.com/p/chainladder/.

Changes in version 0.1.5-1:

- Internal changes to
`plot.MackChainLadder`

to pass new checks introduced by R 2.14.0. - Commented out unnecessary creation of ‘io’ matrix in
`ClarkCapeCod`

function. Allows for analysis of very large matrices for`CapeCod`

without running out of RAM. ‘io’ matrix is an integral part of`ClarkLDF`

, and so remains in that function. -
`plot.clark`

method- Removed “conclusion” stated in
`QQplot`

of clark methods. - Restore ‘par’ settings upon exit
- Slight change to the title

- Removed “conclusion” stated in
- Reduced the minimum ‘theta’ boundary for weibull growth function
- Added warnings to
`as.triangle`

if origin or dev. period are not numeric

Here is a little example using the googleVis package to display the RAA claims development triangle:

library(ChainLadder) library(googleVis) class(RAA) <- "matrix" # change the class from triangle to matrix df <- as.data.frame(t(RAA)) # coerce triangle into a data.frame names(df) <- 1981 : 1990 df$dev <- 1:10 plot(gvisLineChart(df, "dev", options=list(gvis.editor="Edit me!", hAxis.title="dev. period")))

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