README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}

ggplot2::theme_set(bayesplot::theme_default(base_family = "sans"))

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# priorsense

<!-- badges: start -->
[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-green.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)
[![CRAN status](https://www.r-pkg.org/badges/version/priorsense)](https://CRAN.R-project.org/package=priorsense)
[![R-CMD-check](https://github.com/n-kall/priorsense/workflows/R-CMD-check/badge.svg)](https://github.com/n-kall/priorsense/actions)
<!-- badges: end -->

## Overview

priorsense provides tools for prior diagnostics and sensitivity
analysis.

It currently includes functions for performing power-scaling
sensitivity analysis on Stan models. This is a way to check how
sensitive a posterior is to perturbations of the prior and likelihood
and diagnose the cause of sensitivity. For efficient computation,
power-scaling sensitivity analysis relies on Pareto smoothed
importance sampling (Vehtari et al., 2024) and importance weighted
moment matching (Paananen et al., 2021).

Power-scaling sensitivity analysis and priorsense are described in
Kallioinen et al. (2023).

## Installation

Download the stable version from CRAN with:

```{r, eval = F}
install.packages("priorsense")
```


Download the development version from [GitHub](https://github.com/) with:

```{r, eval = F}
# install.packages("remotes")
remotes::install_github("n-kall/priorsense", ref = "development")
```

## Usage

priorsense works with models created with rstan, cmdstanr or brms, or
with draws objects from the posterior package.

### Example 

Consider a simple univariate model with unknown mu and sigma fit to
some data y (available
via`example_powerscale_model("univariate_normal")`):

```stan
data {
  int<lower=1> N;
  array[N] real y;
}
parameters {
  real mu;
  real<lower=0> sigma;
}
model {
  // priors
  target += normal_lpdf(mu | 0, 1);
  target += normal_lpdf(sigma | 0, 2.5);
  // likelihood
  target += normal_lpdf(y | mu, sigma);
}
generated quantities {
  vector[N] log_lik;
  real lprior;
  // log likelihood
  for (n in 1:N) log_lik[n] =  normal_lpdf(y[n] | mu, sigma);
  // joint log prior
  lprior = normal_lpdf(mu | 0, 1) +
	normal_lpdf(sigma | 0, 2.5);
```

We first fit the model using Stan:

```{r, eval = T, results = F, message = F, warning = F}
library(priorsense)

normal_model <- example_powerscale_model("univariate_normal")

fit <- rstan::stan(
  model_code = normal_model$model_code,
  data = normal_model$data,
  refresh = FALSE,
  seed = 123
)
```

Once fit, sensitivity can be checked as follows:
```{r, eval = T}
powerscale_sensitivity(fit)
```

To visually inspect changes to the posterior, use one of the
diagnostic plot functions. Estimates with high Pareto-k values may be
inaccurate and are indicated.

```{r dens_plot, eval = T, out.width = '70%'}
powerscale_plot_dens(fit)
```

```{r ecdf_plot, eval = T, out.width = '70%'}
powerscale_plot_ecdf(fit)
```

```{r quants_plot, eval = T, out.width = '70%'}
powerscale_plot_quantities(fit)
```

In some cases, setting `moment_match = TRUE` will improve the
unreliable estimates at the cost of some further computation. This
requires the [`iwmm` package](https://github.com/topipa/iwmm).

## References

Noa Kallioinen, Topi Paananen, Paul-Christian Bürkner, Aki Vehtari
(2023).  Detecting and diagnosing prior and likelihood sensitivity
with power-scaling. Statistics and Computing. 34, 57.
https://doi.org/10.1007/s11222-023-10366-5

Topi Paananen, Juho Piironen, Paul-Christian Bürkner, Aki Vehtari (2021).
Implicitly adaptive importance sampling. Statistics and Computing
31, 16. https://doi.org/10.1007/s11222-020-09982-2

Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, Jonah Gabry (2024). 
Pareto smoothed importance sampling. Journal of
Machine Learning Research. 25, 72. https://jmlr.org/papers/v25/19-556.html