Repository files navigation

bignum provides numeric vectors with greater precision than R atomicnumeric vectors.

• biginteger() stores any integer (i.e. arbitrary precision).
• bigfloat() stores 50 decimal digits of precision.

They prioritize precision over performance, so computations are slowerthan those usinginteger() ordouble().

Under the hood, bignum uses thecpp_intandcpp_bin_float_50data types from the Boost.Multiprecision C++ library.

You can install the released version of bignum fromCRAN with:

install.packages("bignum")

Or you can install the development version from GitHub:

# install.packages("remotes")remotes::install_github("davidchall/bignum")

Before starting, we’ll increase the displayed precision so we can seethe benefits of bignum.

options(digits=20)options(bignum.sigfig=50)options(bignum.max_dec_width=52)

The limited precision of atomic vectors introduces errors when workingwith very large integers. As an example, let’s calculate the factorialof 23. In base R, we’d calculate:

factorial(23)#> [1] 25852016738884978212864

The factorial of 23 includes a factor of 10, and so the final digitmust be zero. Usingbiginteger() yields the correct result:

prod(biginteger(1:23))#> <biginteger[1]>#> [1] 25852016738884976640000

bigfloat() vectors support much higher precision thandouble()vectors:

1/3#> [1] 0.33333333333333331483bigfloat(1)/3#> <bigfloat[1]>#> [1] 0.33333333333333333333333333333333333333333333333333

However, you need to be careful not to limit the precision accidentally:

bigfloat(1/3)#> <bigfloat[1]>#> [1] 0.333333333333333

Please note that the bignum project is released with aContributor CodeofConduct.By contributing to this project, you agree to abide by its terms.