The goal of reservr is to provide a flexible interface for specifying
distributions and fitting them to (randomly) truncated and possibly
interval-censored data. It provides custom fitting algorithms to fit
distributions to i.i.d. samples as well as dynnamic TensorFlow
integration to allow training neural networks with arbitrary output
distributions. The latter can be used to include explanatory variables
in the distributional fits. Reservr also provides some tools relevant
for working with its core functionality in an actuarial setting, namely
the functions `prob_report()`

and
`truncate_claims()`

, both of which make assumptions on the
type of random truncation applied to the data.

Please refer to the vignettes `distributions.Rmd`

and
`tensorflow.Rmd`

for detailed introductions.

reservr is not yet on CRAN. You can install the latest development version of reservr via

`::install_github("AshesITR/reservr") devtools`

You can install the released version of reservr from CRAN with:

`install.packages("reservr")`

If you want to use all of reservrs features, make sure to also install tensorflow.

This is a basic example which shows how to fit a normal distribution to randomly truncated and censored data.

```
library(reservr)
set.seed(123)
<- 0
mu <- 1
sigma <- 1000
N <- 0.8
p_cens
<- rnorm(N, mean = mu, sd = sigma)
x <- rbinom(N, size = 1L, prob = p_cens) == 1L
is_censored <- x
x_lower <- x[is_censored] - runif(sum(is_censored), min = 0, max = 0.5)
x_lower[is_censored] <- x
x_upper <- x[is_censored] + runif(sum(is_censored), min = 0, max = 0.5)
x_upper[is_censored]
<- runif(N, min = -2, max = 0)
t_lower <- runif(N, min = 0, max = 2)
t_upper
<- t_lower <= x & x <= t_upper
is_observed
<- trunc_obs(
obs xmin = pmax(x_lower, t_lower)[is_observed],
xmax = pmin(x_upper, t_upper)[is_observed],
tmin = t_lower[is_observed],
tmax = t_upper[is_observed]
)
# Summary of the simulation
cat(sprintf(
"simulated samples: %d\nobserved samples: %d\ncensored samples: %d\n",
nrow(obs), sum(is.na(obs$x))
N,
))
# Define outcome distribution and perform fit to truncated and (partially) censored sample
<- dist_normal()
dist <- fit(dist, obs)
the_fit
# Visualize resulting parameters and show a kernel density estimate of the samples.
# We replace interval-censored samples with their midpoint for the kernel density estimate.
plot_distributions(
true = dist,
fitted = dist,
empirical = dist_empirical(0.5 * (obs$xmin + obs$xmax)),
.x = seq(-5, 5, length.out = 201),
plots = "density",
with_params = list(
true = list(mean = mu, sd = sigma),
fitted = the_fit$params
) )
```

Please note that the reservr project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.