In order to facilitate R instruction for actuaries, we have organized several
sets of publicly available data of interest to non-life actuaries. In addition, we suggest
a set of packages, which most practicing actuaries will use routinely. Finally, there is
an R markdown skeleton for basic reserve analysis.
person("Brian A.", "Fannin"
, role = c("aut", "cre")
, email = "FanninQED@yahoo.com")
dplyr, tidyr, ggplot2, lubridate, devtools, actuar,
ChainLadder, knitr, readr, readxl, rmarkdown, mondate, FinCal,
rstan, scales, nlme, maps

dplyr, tidyr, ggplot2, lubridate, devtools, actuar,
ChainLadder, knitr, readr, readxl, rmarkdown, mondate, FinCal,
rstan, scales, nlme, maps, fitdistrplus, tweedie, nycflights13,
XML, RColorBrewer, Lahman

person("Brian A.", "Fannin"
, role = c("aut", "cre")
, email = "captain@PirateGrunt.com")
dplyr, tidyr, ggplot2, lubridate, devtools, actuar,
ChainLadder, knitr, readr, readxl, rmarkdown, mondate, FinCal,
scales, nlme, maps, fitdistrplus, tweedie, nycflights13, XML,
RColorBrewer, Lahman

actuar, ChainLadder, devtools, FinCal, fitdistrplus,
insuranceData, knitr, Lahman, lubridate, maps, mondate,
nycflights13, nlme, RColorBrewer, rmarkdown, scales, tidyverse,
tweedie, XML

actuar, ChainLadder, dplyr, devtools, FinCal, fitdistrplus,
forcats, ggplot2, insuranceData, knitr, Lahman, lubridate,
maps, mondate, nlme, nycflights13, purrr, randomForest,
randomNames, readr, readxl, RColorBrewer, rmarkdown, scales,
stringr, tibble, tidyr, tweedie, XML

Memory efficient handling of FASTA sequence files, a pure R
implementation.
This package provides functions to import FASTA sequence
files into R memory with various bit counts per letter.

Easily Get True-Positive Rate and False-Positive Rate and KS
Statistic
person(given = "Jiaxiang",
family = "Li",
role = c("aut", "cre"),
email = "alex.lijiaxiang@foxmail.com",
comment = c(ORCID = "https://orcid.org/0000-0003-3196-6492"))
The Kolmogorov-Smirnov (K-S) statistic is a standard method to measure the model strength for
credit risk scoring models. This package calculates the K–S statistic and
plots the true-positive rate and false-positive rate to measure the model strength.
This package was written with the credit marketer, who uses risk models in
conjunction with his campaigns. The users could read more details from
Thrasher (1992) <doi:10.1002/dir.4000060408> and 'pyks' <https://pypi.org/project/pyks/>.