Bernoulli: Compute Bernoulli Numbers

View source: R/special-func.R

BernoulliR Documentation

Compute Bernoulli Numbers


Compute the nth Bernoulli number, or generate all Bernoulli numbers up to the nth, using diverse methods, that is, algorithms.

NOTE the current default methods will be changed – to get better accuracy!


Bernoulli    (n, method = c("sumBin", "sumRamanujan", "asymptotic"),
              verbose = FALSE)
Bernoulli.all(n, method = c("A-T", "sumBin", "sumRamanujan", "asymptotic"),
              precBits = NULL, verbose = getOption("verbose"))



positive integer, indicating the index of the largest (and last) of the Bernoulli numbers needed.


character string, specifying which method should be applied. The default for Bernoulli.all(), "A-T" stands for the Akiyama-Tanigawa algorithm which is nice and simple but has bad numerical properties. It can however work with high precision "mpfr"-numbers, see precBits. "sumRamanujan" is somewhat more efficient but not yet implemented.


currently only for method = "A-T"NULL or a positive integer indicating the precision of the initial numbers in bits, using package Rmpfr's multiprecision arithmetic.


(for "A-T":) logical indicating if the intermediate results of the algorithm should be printed.



a number


a numeric vector of length n, containing B(n)


Kaneko, Masanobu (2000) The Akiyama-Tanigawa algorithm for Bernoulli numbers; Journal of Integer Sequences 3, article 00.2.9

See Also

Eulerian, Stirling1, etc.


## The example for the paper
MASS::fractions(Bernoulli.all(8, verbose=TRUE))

B10 <- Bernoulli.all(10)

system.time(B50  <- Bernoulli.all(50))#  {does not cache} -- still "no time"
system.time(B100 <- Bernoulli.all(100))# still less than a milli second

## Using Bernoulli() is not much slower, but hopefully *more* accurate!
## Check first - TODO
system.time(B.1c <- Bernoulli(100))# caches ..
system.time(B1c. <- Bernoulli(100))# ==> now much faster
stopifnot(identical(B.1c, B1c.))

if(FALSE)## reset the cache:
assign("", list(), envir = copula:::.nacopEnv)

## More experiments in the source of the copula package ../tests/Stirling-etc.R

copula documentation built on June 15, 2022, 5:07 p.m.