---
title: "Version 2.0 Notes"
author: "Balasubramanian Narasimhan"
date: '`r Sys.Date()`'
output:
  html_document:
  fig_caption: yes
  theme: cerulean
  toc: yes
  toc_depth: 2
vignette: >
  %\VignetteIndexEntry{Version 2.0 Notes}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

```{r echo=FALSE}
knitr::opts_chunk$set(
    message = FALSE,
    warning = FALSE,
    error = FALSE,
    tidy = FALSE,
    cache = FALSE
)
```

## What's new

Version 2.0 integrates two well-known cubature libraries in one place:

- The cubature C library  of [Steven G. Johnson](https://math.mit.edu/~stevenj/)
- The Cuba C library of [Thomas Hahn](https://wwwth.mpp.mpg.de/members/hahn/)

It also provides a single function `cubintegrate` that allows one to
call all methods in a uniform fashion, as I explain below.

__N.B.__ One has to be aware that there are cases where one library
will integrate a function while the other won't. Where possible, I
have brought such
[problems](https://github.com/stevengj/cubature/issues/7) to the
notice of the authors underlying C library and I encourage you to do
the same, by providing reproducible examples.

## Unified Interface

Following a suggestion by Simen Guare, we now have a function
`cubintegrate` that can be used to try out various integration methods
easily. Some examples. 

```{r}
library(cubature)
m <- 3
sigma <- diag(3)
sigma[2,1] <- sigma[1, 2] <- 3/5 ; sigma[3,1] <- sigma[1, 3] <- 1/3
sigma[3,2] <- sigma[2, 3] <- 11/15
logdet <- sum(log(eigen(sigma, symmetric = TRUE, only.values = TRUE)$values))
my_dmvnorm <- function (x, mean, sigma, logdet) {
    x <- matrix(x, ncol = length(x))
    distval <- stats::mahalanobis(x, center = mean, cov = sigma)
    exp(-(3 * log(2 * pi) + logdet + distval)/2)
}
```

First we try the scalar invocation with `hcubature`.

```{r}
cubintegrate(f = my_dmvnorm, lower = rep(-0.5, 3), upper = c(1, 4, 2), method = "pcubature",
             mean = rep(0, m), sigma = sigma, logdet = logdet)
```

We can compare that with Cuba's `cuhre`. 

```{r}
cubintegrate(f = my_dmvnorm, lower = rep(-0.5, 3), upper = c(1, 4, 2), method = "cuhre",
             mean = rep(0, m), sigma = sigma, logdet = logdet)
```

The Cuba routine can take various further arguments; see for example,
the help on `cuhre`. Such arguments can be directly passed to
`cubintegrate`. 

```{r}
cubintegrate(f = my_dmvnorm, lower = rep(-0.5, 3), upper = c(1, 4, 2), method = "cuhre",
             mean = rep(0, m), sigma = sigma, logdet = logdet,
             flags = list(verbose = 2))
```

As there are many such method-specific arguments, you may find the
function `default_args()` useful.

```{r}
default_args()
```

## Vectorization 

`cubintegrate` provides vector intefaces too: the parameter `nVec` is
by default 1, indicating a scalar interface. Any value > 1 results in
a vectorized call. So `f` has to be constructed appropriately, thus:
```{r}
my_dmvnorm_v <- function (x, mean, sigma, logdet) {
    distval <- stats::mahalanobis(t(x), center = mean, cov = sigma)
    exp(matrix(-(3 * log(2 * pi) + logdet + distval)/2, ncol = ncol(x)))
}
```

Here, the two underlying C libraries differ. The cubature library
manages the number of points used in vectorization dynamically and
this number can even vary from call to call. So any value of `nVec`
greater than 1 is merely a flag to use vectorization. The Cuba C
library on the other hand, will use the actual value of `nVec`. 

```{r}
cubintegrate(f = my_dmvnorm_v, lower = rep(-0.5, 3), upper = c(1, 4, 2), method = "pcubature",
             mean = rep(0, m), sigma = sigma, logdet = logdet,
             nVec = 128)
```

```{r}
cubintegrate(f = my_dmvnorm_v, lower = rep(-0.5, 3), upper = c(1, 4, 2), method = "cuhre",
             mean = rep(0, m), sigma = sigma, logdet = logdet,
             nVec = 128)
```

## Session Info


```{r}
sessionInfo()
```
