- Changes to the Matrix package (in 1.5-5) have impacted the MultiChainLadder2 function, which need to be resolved. In some cases the function fails when the data results in near-singular matrix inversions

- New seed argument for BootChainLadder to set an optional seed for the random generator

- New weights argument for MuinchChainLadder
- Fixed typo checking for weight argument in glmReserve. Thanks to Kennedy Mwavu for reporting this issue
- Fixed weights in CDR.MackChainLadder. Thanks to Giorgia Chieruzzi for contributing the patch

- Started using pkgdown to create package website
- Added ChainLadder hexagon graphic
- Moved vignettes/NEWS.Rmd to NEW.md to autogenerate changelog with pkgdown
- Clarified how to extract MackChainLadder objects in package vignette

- Updated package dependencies to address a warning from CRAN

- Updated URLs in the bibliography of the package vignette and help files

- Moved continuous integration testing from TravisCI to GitHub Actions
- Changed vignette format from Sweave to RMarkdown to facilitate easier testing using GitHub Actions

- Fixed as.triangle for a ‘long’ data set, when input data had missing values. Thanks to Dimitri Minassian for reporting this issue.
- Fixed standard error estimation in MackChainLadder when tail factor > 1 and alpha = 2. Thanks to Valentin Cornaciu for reporting this issue.

- Added Berquist-Sherman Paid Claim Development Adjustment methods to adjust paid claims based on the underlying relation between paid and closed claims.
- Added tests to check calendar year effect, correlation between development factors and inflation.

Thanks to Marco De Virgilis.

- Fix to as.triangle.data.frame. The labels of origin and development period were mixed up with the move away from reshape2 to aggregate in version 0.2.8. Thanks to Edward Tasker for reporting this issue.

- Fix to print statements to align them with the generic print methods. Thanks to Markus Senn
- Clarified how the ‘weights’ argument in chainladder and MackChainLadder can be used
- Removed dependency on reshape2 as it has been deprecated

- New quantile method for ‘MackChainLadder’ andv function QuantileIFRS17 to estimate the IFRS 17 Risk Adjustment. Thanks to Eric Dal Moro and Yuriy Krvavych

- New function ‘triangle’ to create a triangle from the given set of vectors of observed data. Thanks to Vincent Goulet

- Fixed bug in Mack.S.E function when “sigma[i - 2]^2” is zero. Thanks to Patrick Green for reporting and fixing the issue.

- Fixed meta information of NEWS vignette, so it will be shown as NEWS on CRAN

Moved NEWS file to Markdown format.

Previously it was required that the row and column names of a triangle be convertible to numeric, although that “requirement” did not always cause a problem. For example, the following sets the rownames of GenIns to the beginning Date of the accident year.

```
x <- GenIns
rownames(x) <- paste0(2001:2010, "-01-01")
```

A plot with the `lattice=TRUE`

option, which previously
would blow up, now displays with nice headings.

`plot(x, lattice=TRUE)`

It can often be useful to have “origin” values that are not necessarily convertible to numeric. For example, suppose you have a table of claim detail at various evaluation dates. Invariably, such a table will have a Date field holding the date of loss. It would be nice to be able to summarize that data by accident year “cuts”. It turns out there’s a builtin function in R that will get you most of the way there. It’s called ‘cut’.

Here we take the GenIns data in long format and generate 50 claims
per accident period. We assign each claim a random date within the year.
The incurred (or paid) “value” given is a random perturbation of
one-fiftieth of `GenInsLong$value.`

We accumulate the detail into an accident year triangle using
ChainLadder’s `as.triangle`

method. The summarized triangle
displayed at the end is very similar to `GenIns`

, and has
informative row labels.

```
x <- GenInsLong
# start off y with x's headings
y <- x[0,]
names(y)[1] <- "lossdate"
set.seed(1234)
n = 50 # number of simulated claims per accident perior
for (i in 1:nrow(x)) {
y <- rbind(y,
data.frame(
lossdate = as.Date(
as.numeric(as.Date(paste0(x[i, "accyear"]+2000, "-01-01"))) +
round(runif(n, 0, 364),0), origin = "1970-01-01"),
devyear = x[i, "devyear"],
incurred.claims = rnorm(n, mean = x[i, "incurred claims"] / n,
sd = x[i, "incurred claims"]/(10*n))
))
}
# here's the magic cut
y$ay <- cut(y$lossdate, breaks = "years")
# this summarized triangle is very similar to GenIns
as.triangle(y, origin = "ay", dev = "devyear", value = "incurred.claims")
```

The user is encouraged to experiment with other cut’s – e.g.,
`breaks = "quarters"`

will generate accident quarter
triangles.

A new function, `as.LongTriangle`

, will convert a triangle
from “wide” (matrix) format to “long” (data.frame) format. This differs
from ChainLadder’s as.data.frame.triangle method in that the rownames
and colnames of Triangle are stored as **factors**. This
feature can be particularly important when plotting a triangle because
the order of the “origin” and “dev” values is important.

Additionally, the columns of the resulting data frame may be renamed from the default values (“origin”, “dev”, and “value”) using the “varnames” argument for “origin”/“dev” and the “value.name” argument for “value”.

In the following example, the `GenIns`

triangle in
ChainLadder is converted to a `data.frame`

with non-default
names:

```
GenLong <- as.LongTriangle(GenIns,
varnames = c("accident year", "development age"),
value.name = "Incurred Loss")
```

In the following plot, the last accident year and the last development age are shown last, rather than second as they would have been if displayed alphabetically (ggplot’s default for character data):

```
library(ggplot2)
ggplot(GenLong, aes(x=`development age`, y = `Incurred Loss`,
group = `accident year`, color = `accident year`)) +
geom_line()
```

Previously, when an “exposure” attribute was assigned to a triangle
for use with `glmReserve`

, it was assumed/expected that the
user would supply the values in the same order as the accident years.
Then, behind the scenes, glmReserve would use an arithmetic formula to
match the exposure with the appropriate accident year using the numeric
“origin” values after the triangle had been converted to long
format.

`glmReserve`

now allows for “exposure” to have “names”
that coincide with the rownames of the triangle, which are used to match
to origin in long format. Here is an example, newly found in
`?glmReserve`

.

```
GenIns2 <- GenIns
rownames(GenIns2) <- paste0(2001:2010, "-01-01")
expos <- (7 + 1:10 * 0.4) * 10
names(expos) <- rownames(GenIns2)
attr(GenIns2, "exposure") <- expos
glmReserve(GenIns2)
```

The `glmReserve`

function now supports the negative
binomial GLM, a more natural way to model over-dispersion in count data.
The model is fitted through the `glm.nb`

function from the
`MASS`

package.

To fit the negative binomial GLM to the loss triangle, simply set
`nb = TRUE`

in calling the glmReserve function:

`(fit6 <- glmReserve(GenIns, nb = TRUE))`

New files in the `/inst/unittests/`

folder can be used for
future enhancements

- runit.Triangles.R for Triangles.R
- runit.glmReserve.R for glmReserve.R

Contributors of new contributions to those R files are encouraged to utilize those runit scripts for testing, and, of course, add other runit scripts as warrantted.

By default, R’s `lm`

method generates a warning when it
detects an “essentially perfect fit”. This can happen when one column of
a triangle is identical to the previous column; i.e., when all link
ratios in a column are the same. In the example below, the second column
is a fixed constant, 1.05, times the first column. ChainLadder
previously issued the lm warning below.

```
x <- matrix(byrow = TRUE, nrow = 4, ncol = 4,
dimnames = list(origin = LETTERS[1:4], dev = 1:4),
data = c(
100, 105, 106, 106.5,
200, 210, 211, NA,
300, 315, NA, NA,
400, NA, NA, NA)
)
mcl <- MackChainLadder(x, est.sigma = "Mack")
Warning messages:
1: In summary.lm(x) : essentially perfect fit: summary may be unreliable
2: In summary.lm(x) : essentially perfect fit: summary may be unreliable
3: In summary.lm(x) : essentially perfect fit: summary may be unreliable
```

which may have raised a concern with the user when none was warranted.

Now ChainLadder issues an “informational warning”:

```
x <- matrix(byrow = TRUE, nrow = 4, ncol = 4,
dimnames = list(origin = LETTERS[1:4], dev = 1:4),
data = c(
100, 105, 106, 106.5,
200, 210, 211, NA,
300, 315, NA, NA,
400, NA, NA, NA)
)
```

`mcl <- MackChainLadder(x, est.sigma = "Mack")`

Fixed tail extrapolation in Vignette. (Thanks to Mark Lee.)

- Fixed summary calls.
- Updated documentation for weights parameter of chainladder method.
- Fixes for tail extrapolation in Vignette and Chainladder
- The calculation for the tail log-linear extrapolation given in the vignette had a minor error. This has been corrected, and the result now agrees with the results of MackChainLadder(RAA, tail=TRUE).
- The calculation of the tail using the log-linear extrapolation in ChainLadder.R had a potential error - when clratios has values of less than unity they are dropped, but the extrapolation was started from a quantity indexed by the length of f, not the value of fn. This changes the results if clratios has a pattern like e.,g.: … 1.1, 0.98,1.01,0.005 (i.e. a link ratio less than unity which is not the last value)
- Minor fix to the comments in ChainLadder.R and MackChainLadder.R, fixing notation for alpha which is now consistent with the documentation and Mack’s original paper.

Added back functionality to estimate the index parameter for the compound Poisson model in ‘glmReserve’ (now depends on package cplm). This works for both ‘formula’ and ‘bootstrap’.

Added methods ‘resid’ and plot for class ‘glmReserve’ (now depends on ggplot2)

New function PaidIncurredChain by Fabio Concina, based on the 2010 Merz & Wuthrich paper Paid-incurred chain claims reserving method

plot.MackChainLadder and plot.BootChainLadder gained new argument

‘which’, allowing users to specify which sub-plot to display. Thanks to Christophe Dutang for this suggestion.

Updated NAMESPACE file to comply with new R CMD checks in R-3.3.0

Removed package dependencies on grDevices and Hmisc

Expanded package vignette with new paragraph on importing spreadsheet data, a new section “Paid-Incurred Chain Model” and an added example for a full claims development picture in the “One Year Claims Development Result” section.

New generic function CDR to estimate the one year claims development result. S3 methods for the Mack and bootstrap model have been added already:

- CDR.MackChainLadder to estimate the one year claims development result of the Mack model without tail factor, based on papers by Merz & Wuthrich (2008, 2014)
- CDR.BootChainLadder to estimate the one year claims development result of the bootstrap model, using ideas and code by Giuseppe Crupi.

New function tweedieReserve to estimate reserves in a GLM framework, including the one year claims development result.

Package vignette has new chapter ‘One Year Claims Development Result’.

New example data MW2008 and MW2014 form the Merz & Wuthrich (2008, 2014) papers

Source code development moved from Google Code to GitHub: https://github.com/mages/ChainLadder

as.data.frame.triangle now gives warning message when dev. period is a character

Alessandro Carrato, Giuseppe Crupi and Mario Wuthrich have been added as authors, thanks to their major contribution to code and documentation

Christophe Dutang, Arnaud Lacoume and Arthur Charpentier have been added as contributors, thanks to their feedback, guidance and code contribution

- Updated README and DESCRIPTION file to comply with changes of CRAN policy.

- BootChainLadder produced warnings for triangles that had static developments when the argument process.distr was set to “od.pois”.
- as.triangle.data.frame didn’t work for a data.frame less than three rows
- Arguments xlab and ylab were not passed through in plot.triangle, when lattice=TRUE

- The glmReserve function currently doesn’t allow the parameter var.power to be set to NULL, which would have called the cpglm function of the cplm package. The cplm package is due to dependency issues with lme4 no longer available via CRAN.

A new function, CLFMdelta, finds the value of delta such that the model coefficients resulting from the ‘chainladder’ function with that value for argument delta are consistent with an input vector of ‘selected’ age-to-age factors, subject to restrictions on the ‘selected’ factors relative to the input ‘Triangle’. See the paper “A Family of Chain-Ladder Factor Models for Selected Link Ratios” by Bardis, Majidi, Murphy, Variance Journal

A new ‘coef’ method returns the age-to-age factor coefficients of the regression models estimated by the ‘chainladder’ function.

Exports a function “LRfunction” that calculates a Triangle’s link ratio function and can be used to plot the space of “reasonable link ratio selections” per the CLFM paper.

- Removed some package dependencies in DESCRIPTION and moved them to Imports.

- The list output of the MackChainLadder function now includes the parameter risk and process risk breakdowns of the total risk estimate for the sum of projected losses across all origin years by development age.
- The Mack Method’s recursive parameter risk calculation now enables Dr. Mack’s original two-term formula (the default) and optionally the three-term formula found in Murphy’s 1994 paper and in the 2006 paper by Buchwalder, Buhlmann, Merz, and Wuthrich.
- A few more Mack Method examples.

- The phi-scaling factor in BootChainLadder was incorrect. Instead of
calculating the number of data items in the upper left triangle as
n
*(n+1)/2, n*(n-1)/2 was used. Thanks to Thomas Girodot for reporting this bug.

- The function “getLatestCumulative” adds attributes to the result
- names = origin (rownames) from the Triangle
- rowsname = name of row dimension of Triangle
- colnames = dev (colnames) from Triangle
- colsname = name of the column dimension of Triangle The function has an additional argument, na.values, a vector of values (e.g., zero) that are synonymous with NA when searching for the rightmost non-NA value

- as.triangle.data.frame now aggregates multiple data.frame records when more than one (origin, dev) observation is found (the previous version took the first observation).

- The vignette has been updated with sections on Multivariate chain-ladder, Clark’s method and Generalised linear model methods
- MunichChainLadder no longer accepts triangles with more rows than columns as the function is not laid out for such data sets yet. Thanks to Ben Escoto for highlighting this issue.

- The function “glmReserve” now simulates predictive distributions of the loss reserves when bootstrapping is used.
- “glmReserve” allows the variance function of the compound Poisson distribution to be estimated from the data, using the estimation method provided by the “cplm” package.
- We offer a new function “MultiChainLadder2” to fit several commonly used multivariate chain ladder models, which is much easier to use.

- The output from “glmReserve” is made to be of class “glmReserve”, instead of class “glm” used in previous versions.
- Fix bugs when exposure is included in “glmReserve”. Thanks to Alessandro Carrato for reporting this bug.
- The “mse.method” argument in “glmReserve” supports partial match.
- Dramatic improvement on the documentation of “MultiChainLadder”.
- Complete the sections of “MultiChainLadder” and “glmReserve” in the vignettes.

- We started writing a vignette. The current version is still draft and far from complete. Feedback will be much appreciated.

- Removed .Internal call to make ChainLadder compliant with R 2.15.0
- Changed argument “t” in plot.triangle to “type” in order to be consistent with plot.default

- as.triangle() gave triangles back, with development periods not ordered, when the input data frame had unordered development periods in different units, e.g. dev=c(1,100,10) Thanks to Ben Escoto for reporting this issue.

- 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

- Reduced the minimum ‘theta’ boundary for weibull growth function
- Added warnings to as.triangle if origin dev. period are not numeric

- New function glmReserve, which implements loss reserving models within the generalized linear model framework following a paper by England P. and Verrall R. (1999)

- Minor changes to reflect a more rigours package build process for R >= 2.14.0
- Start up message uses now packageStartupMessage rather than cat to allow the message to be suppressed.

- ClarkLDF and ClarkCapeCod functions were reorganized to clarify the
delivery and presentation of the methods’ results
- Individual components now contain distinct values within Clark’s methodologies
- ‘summary’ methods produce “reports” that display results in the form of typical loss development and Bornhuetter-Ferguson exhibits
- “Table” functions now produce the results as shown in the tables on pp. 64, 65 and 68 of Clark’s paper
- A ‘vcov’ method produces the covariance matrix of the estimated parameters

- An ‘ata’ function exists to calculate the “age-to-age” development factors of a loss “triangle”, as well as the simple and volume weighted averages

- The TruncatedGrowth function value under the Clark Cape Cod method was incorrectly printed in the Table68 data.frame when the calculations were to be based on the average date of loss (argument adol=TRUE). The underlying calculations used the correct adol adjustment, only the printed output was incorrect.

ClarkLDF and ClarkCapeCod functions: additional functionality

- Clark’s methods now work for “one-row triangles” – i.e., loss experience from only one origin period
- Clark’s methods work for “phase-shifted” triangles – i.e., triangles whose first age does not coincide with the end of the origin period. Example: accident year origin periods with September 30th evaluation dates.

A ‘vcov’ method now exists to produce the covariance matrix of the estimated parameters using the approach in Clark’s paper

Additional values (in lists) returned by Clark’s methods:

- FI = Fisher Information matrix as Clark defines it in his paper (i.e., without the sigma^2 value)
- dR = the gradient of the reserves function evaluated at the optimal parameter values
- value = value of the log-likelihood function at the solution
- counts = number of evaluations of the log likelihood and its derivative before convergence

Fine-tuning of maximum likelihood numerical algorithm’s control parameters

- Enable more consistent convergence properties between R’s 32-bit and 64-bit environments
- Initial starting values for the weibull function were adjusted for successful convergence across a wider set of triangles
- Upper bounds introduced for “L-BFGS-B” maximum likelihood method to bound weibull away from unity at too early an age

If the solution is found at the boundary of the parameter region, it is conceivable that a “more optimal” solution might exist if the boundary constraints were not as conservative, so a warning is given

The parameters returned by the methods were the scaled versions; they now at their original scales.

The loss development factor (LDF) being returned by ClarkCapeCod was not documented

New implementation of the methods in David Clark’s “LDF Curve Fitting” paper in the 2003 Forum by Daniel Murphy.

- Includes LDF and CapeCod methods (functions ‘ClarkLDF’ and ‘ClarkCapeCod’, respectively)
- Programmed to handle log-logistic and weibull growth functions
- Printing an object returned by the function results in a table similar to that on p. 65 of the paper
- Plotting such an object results in four residual plots, including a Q-Q plot with the results of the Shapiro-Wilk test

- ‘residuals.MackChainLadder’: Zero weights applied to MackChainLadder caused an error. Thanks to Ernesto Schirmacher for reporting this bug.

- New multivariate chain ladder function ‘MultiChainLadder’ by Wayne (Yanwei) Zhang actuaryzhang@uchicago.edu
- New function ‘getLatestCumulative’ available. It returns for a given triangle the most recent values for each origin period.
- New demos! Type demo(package=‘ChainLadder’) for more information.
- Demos exist for the following topics: ChainLadder, MackChainLadder, DatabaseExamples, MSOffice, MultiChainLadder
- New SWord example file ChainLadder_SWord_Example.doc, which demonstrates how R code snippets can be integrated into a Word file. The following R command system.file(“SWord”, package=“ChainLadder”) will show the directory of the file.

- The examples in MackChainLadder and ChainLadder-package have been shortened and demo files have been created instead. The examples focus on the syntax of the function calls, while the demos give more detailed information on how you might want to use the functions in a business context.

- ‘plot.MunichChainLadder’: The labels of the axis of the residuals plots where the mixed up. Thanks to Ben Escoto for reporting this issue.
- ‘estimate.sigma’ didn’t check for sigma>0 before applying a log-linear regression. Thanks to Dan Murphy reporting this bug.

‘MackChainLadder’ has new argument ‘alpha’ as an additional weighting parameter. As a result, the argument ‘weights’ is now just that, weights should be between 0 and 1. The argument ‘alpha’ describes the different chain ladder age-to-age factors: The default for alpha for all development periods is 1. See Mack’s 1999 paper: alpha=1 gives the historical chain ladder age-to-age factors, alpha=0 gives the straight average of the observed individual development factors and alpha=2 is the result of an ordinary regression with intercept 0.

Basic ‘chainladder’ function now available using linear models. See ?chainladder for more information.

More examples for ‘MackChainLadder’ demonstrate how to apply the MackChainLadder over several triangles in ‘one-line’.

‘as.data.frame.triangle’ has new argument ‘lob’ (e.g. line of business) which allows to set an additional label column in the data frame output.

‘MackChainLadder’: Latest position of incomplete triangles were in some cases not returned correctly. Thanks to Ben Escoto for reporting and providing a patch.

‘MackChainLadder’:

- Mack.S.E was not correctly calculated for non-standard chain ladder age-to-age factors (e.g. straight averages or ordinary regression through the origin) due the missing argument for ‘alpha’.
- Chain ladder age-to-age factors were always applied to diagonal elements to calculate forecasts, although data in sub-diagonal triangle could exist. Many thanks to Przemyslaw Sloma for reporting those issues.

New triangle class with S3 methods for plot, print and conversion from triangles to data.frames and vis versa

New utility functions ‘incr2cum’ and ‘cum2incr’ to convert incremental triangles into cumulative triangles and vis versa. Thanks to Chritophe Dutang.

New logical argument lattice for plot.MackChainLadder (and plot.triangle), which allows to plot developments by origin period in separate panels.

- ‘MunichChainLadder’: tail factors were not accepted. Thanks to Stefan Pohl for reporting this issue.

- ‘MackChainLadder’: ‘F.se’[ultimate] was calculated of the ultimate column instead of the latest paid.

- ‘MackChainLadder’ has new arguments ‘tail.sigma’ and ‘tail.se’ to provide estimates of the variability for a given tail factor.

- ‘MackChainLadder’: calculation of ‘Mack.S.E’ did not use an ultimate sigma factor to estimate ‘Mack.S.E’ when a tail factor > 1 was provided (Thanks to Mark Hoffmann for reporting this issue).

- Updated documentation to work with new Rd-file parser (R version >= 2.9.0)
- Updated documentation for ‘ABC’ data (Thanks to Glen Barnett)

- Updated documentation for ‘MackChainLadder’ (Thanks to Daniel Murphy)

- ‘MackChainLadder’ gives two more elements back: ‘Mack.ProcessRisk’ and ‘Mack.ParameterRisk’ for the process and parameter risk error (Thanks to Daniel Murphy)
- In the summary output of ‘MackChainLadder’ the label ‘CV’ changed to ‘CV(IBNR)’ to clarify that we show the coefficient of variance of the IBNR.
- ‘MackChainLadder’ provides new example plots for CV(IBNR) vs. origin
period and CV(Ultimate) vs. origin period

- Updated documentation

- Updated documentation

- New function ‘BootChainLadder’, based on papers by England and Verrall, and Barnett and Zehnwirth
- ‘MackChainLadder’ and ‘MunichChainLadder’ allow for tail factors
- ‘MackChainLadder’ estimates the overall standard error for the total
IBNR

- New arguments ‘tail’ and ‘est.sigma’ for MackChainLadder, to control the tail factor and the estimation of sigma_{n-1}
- New arguments ‘tailP’, ‘tailI’ and ‘est.sigmaP’, ‘est.sigmaI’ for ‘MunichChainLadder’, which are passed on to ‘MackChainLadder’ to control the tail factor and the estimation of sigma_{n-1} for the Paid and Incurred triangle
- ‘Mack-, ’Munich-, and ’BootChainLadder’ accept (mxn) matrices with m>=n, e.g more accident years than development years
- New example data sets: ‘ABC’ (annual run-off triangle of a worker’s compensation portfolio of a large company), ‘qpaid’, ‘qincurred’ (‘made-up’ data of a quarterly development triangle of annual origin period)
- Triangles with higher development period frequency (e.g quarterly) than origin period frequency (e.g annual) can be used after being ‘blown-up’ to a common period frequency, see the help of ‘qpaid’
- ‘Mack-, ’Munich- and ’BootChainLadder’ accept ‘blown-up’ triangles of higher development period frequency than origin period frequency filled with ‘NA’, see the help of ‘qpaid’

- summary functions for ‘Mack-, ’Munich-, ’BootChainLadder’ give all a list back with two elements: ‘ByOrigin’ and ‘Totals’
- Change of labels: origin years -> origin period and development years -> development origin
- Coefficient of Variation is abbreviate with ‘CV’ instead of ‘CoV’
- The example spreadsheet ‘ChainLadder_in_Excel.xls’ has new examples, including ‘BootChainLadder’
- New greeting message after the R-call ‘library(ChainLadder)’
- Improved documentation

- ‘MunichChainLadder’: calculation of ‘lambdaP’ and ‘lambdI’ was incorrect. Thanks to Beat Huggler for reporting this issue.

- R/BootstrapReserve.R Included all the functions for the BootChainLadder function. The BootChainLadder procedure provides a predictive distribution of reserves for a cumulative claims development triangle.
- R/BootstrapReserve.R, MackChainLadder.R, MunichChainLadder The summary methods for MackChainLadder, MunichChainLadder, BootChainLadder give a list back with two elements “ByOrigin” and “Totals”
- R/zzz.R Included a .onLoad function to produce a little message after the ChainLadder package is loaded.
- Excel/ChainLadder_in_Excel.xls Added new examples for BootChainLadder and how to use Rapply to call functions from the ChainLadder package.

- R/MackChainLadder.R Included tail factor estimation. The function MackChainLadder has a new argument “tail” to either estimate the tail factor via a log-linear regression or to set it manually.
- data/qpaid.RData, qincurred.RData Added examples of quarterly development triangles

- R/MackChainLadder.R Prepared the functions Mack.S.E and Total.Mack.S.E to accept triangles with rows full of NA values. This might be useful for non quadratic triangles

- R/MackChainLadder.R Bug fix: Function Mack.S.E did not give F.se back, which is needed by TotalMack.S.E. Many thanks to Florian Leitenstorfer for reporting this issue.

- inst/Excel/ChainLadder_in_Excel.xls uses now dynamic functions and shows how to call ‘plot’ from Excel
- R/MackChainLadderFunctions.R: Changed labels Reserving to IBNR (=Incurred But Not Reported)

- R/MackChainLadderFunctions.R: Mack.S.E checks now which sigma>0 before log linear regression of sigma to estimate sigma[n-1]

- R/MackChainLadderFunctions.R: added function TotalMack.S.E function to estimate the overall standard error for the reserve. MackChainLadder gives now also the Total.Mack.S.E. back plus the estimate standard error for all individual age-to-age factors F.se.

- First release on CRAN