Title: Statistical Methods and Models for Claims Reserving in General
Description: Various statistical methods and models which are
typically used for the estimation of outstanding claims reserves
in general insurance. The package has implementations of the Mack,
Munich, Bootstrap, multivariate, and chain-ladder factor models (CLFM),
as well as the loss development factor curve fitting methods of
Dave Clark and generalised linear model based reserving models.

person("Alessandro", "Carrato", role = "aut",
person("Arthur", "Charpentier", role = "ctb"),
person("Mario", "Wuthrich", role = "aut")
in general insurance, including those to estimate the claims
development result as required under Solvency II.
Imports: Matrix, actuar, Hmisc, methods, stats, reshape2, MASS,

person("Mario", "Wuthrich", role = "aut"),
Imports: Matrix, actuar, methods, stats, reshape2, lattice, grid,

person("Yanwei (Wayne)", "Zhang", role = "aut",
person("Mario", "Wuthrich", role = "aut",
Imports: Matrix, actuar, methods, stats, lattice, grid, tweedie, utils,
systemfit, statmod, cplm (>= 0.7-3), ggplot2, MASS

systemfit, statmod, cplm (>= 0.7-3), ggplot2, MASS, mvtnorm

tweedie, utils, systemfit, statmod, cplm (>= 0.7-3), ggplot2

tweedie, utils, systemfit, statmod, cplm (>= 0.7-3), ggplot2,

Description: It is a R package for the detection of the point of a
sharp turn of the behavior of the time series.

Authors@R: person("Michael", "Marks", email = "michaelmarks@analyticaconsulting.com", role = c("aut", "cre"))
Description: A basic implementation of the change in mean detection method outlined in: Taylor, Wayne A. (2000) <https://variation.com/wp-content/uploads/change-point-analyzer/change-point-analysis-a-powerful-new-tool-for-detecting-changes.pdf>. The package recursively uses the mean-squared error change point calculation to identify candidate change points. The candidate change points are then re-estimated and Taylor's backwards elimination process is then employed to come up with a final set of change points. Many of the underlying functions are written in C++ for improved performance.
Imports: Rcpp (>= 1.0.4), dplyr, purrr, tidyr, magrittr, bench, rlang,