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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


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


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.

mu <- 0
sigma <- 1
N <- 1000
p_cens <- 0.8

x <- rnorm(N, mean = mu, sd = sigma)
is_censored <- rbinom(N, size = 1L, prob = p_cens) == 1L
x_lower <- x
x_lower[is_censored] <- x[is_censored] - runif(sum(is_censored), min = 0, max = 0.5)
x_upper <- x
x_upper[is_censored] <- x[is_censored] + runif(sum(is_censored), min = 0, max = 0.5)

t_lower <- runif(N, min = -2, max = 0)
t_upper <- runif(N, min = 0, max = 2)

is_observed <- t_lower <= x & x <= t_upper

obs <- trunc_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
  "simulated samples: %d\nobserved samples: %d\ncensored samples: %d\n", 
  N, nrow(obs), sum($x))

# Define outcome distribution and perform fit to truncated and (partially) censored sample
dist <- dist_normal()
the_fit <- fit(dist, obs)

# 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.
  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

Code of Conduct

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.