help links subscribe changes refresh edit

	SandBox
SandBoxFileUploadTest ←	↑	→ SandBoxFormalProduct

SandBoxFisher

Edit detail for SandBoxFisher revision 7 of 11

	1 2 3 4 5 6 7 8 9 10 11
Editor: Mark Clements Time: 2009/04/07 20:06:09 GMT-7
Note: Added a Sage implementation

changed:
-How useful are the different CAS languages for implementing numerical routines? Prompted by a comparison of R and C for implementing Fisher's exact test for 2x2 tables (http://fluff.info/blog/arch/00000172.htm), I thought that it would be interesting to implement this particular test in Spad, Boot, Reduce, Maxima and Common Lisp (see below). Each set of code was required to implement a univariate root finder and the hypergeometric distribution to calculate the p-value under different alternatives, together with the 95% confidence interval and the maximum likelihood estimator for the odds ratio. The reference implementation is R, where the code and output would be:
How useful are the different CAS languages for implementing numerical routines? Prompted by a comparison of R and C for implementing Fisher's exact test for 2x2 tables (http://fluff.info/blog/arch/00000172.htm), I thought that it would be interesting to implement this particular test in Spad, Boot, Reduce, Maxima, Common Lisp and Sage (see below). Each set of code was required to implement a univariate root finder and the hypergeometric distribution to calculate the p-value under different alternatives, together with the 95% confidence interval and the maximum likelihood estimator for the odds ratio. The reference implementation is R, where the code and output would be:

changed:
-To summarise, all five languages (Spad, Boot, Reduce, Maxima and Common Lisp) provide arbitrary length integers and fractions, ensuring that the hypergeometric distribution was straightforward to implement. The Lisp and R implementations were very similar, which is not surprising, given that the two languages are closely related. In contrast, the nested functions in Spad seemed clumsy, with the requirement to use the #1 and #2 argument references (although this has changed recently in Fricas); I did, however, appreciate Spad's lexical scoping and facility to fall back to a symbolic analysis. An Aldor implementation may be cleaner than the Spad implementation. Boot's implementation was initially difficult, as I was unclear how to pass values to the nested functions with using function argument (which was not possible for the univariate root finder :-(). The use of the "$" prefix on derived variables seemed particularly clumsy. Finally, my version of Fricas:Boot defaulted to single precision floats, which caused problems with precision. Type specification in OpenAxiom:Boot and more recent versions of Fricas would negate the need for explicit coercion to double-floats. For Reduce, the lack of nested procedures in the algebraic mode made progress slow; importantly, the switch to symbolic mode made the implementation quite straightforward, with reasonably good debugging. The Maxima version was particularly short, given that Maxima already provides a root solver and the hypergeometric distribution.
-
-This begs the question: when would one use any of these languages for mixed numerical/symbolic analysis? In my opinion, Boot is the least likely to be used, although it does play closely and well with Common Lisp (a la Reduce and Maxima). One could code for numerical analysis in Boot and Common Lisp - however Boot's lack of lexical scoping may be a detraction. Moreover, by my understanding, Lisp or Boot are unable to evaluate Spad or Axiom functions. Second, for an R user, Spad's type system seems fussy (and extremely elegant); I also found that debugging could be slow. The lack of ability to call Spad functions from Lisp or Boot is an important restriction, requiring that a large body of code in Lisp (or Fortran via f2cl), such as for optimisation, would need to be hand translated to Spad (see [SandBoxMLE] for an example). Reduce and Maxima both provided fairly polished environments worthy of further consideration. 
-
To summarise, all six languages (Spad, Boot, Reduce, Maxima, Common Lisp and Sage) provide arbitrary length integers and fractions, ensuring that the hypergeometric distribution was straightforward to implement. The Lisp and R implementations were very similar, which is not surprising, given that the two languages are closely related. In contrast, the nested functions in Spad seemed clumsy, with the requirement to use the #1 and #2 argument references (although this has changed recently in Fricas); I did, however, appreciate Spad's lexical scoping and facility to fall back to a symbolic analysis. An Aldor implementation may be cleaner than the Spad implementation. Boot's implementation was initially difficult, as I was unclear how to pass values to the nested functions with using function argument (which was not possible for the univariate root finder :-(). The use of the "$" prefix on derived variables seemed particularly clumsy. Finally, my version of Fricas:Boot defaulted to single precision floats, which caused problems with precision. Type specification in OpenAxiom:Boot and more recent versions of Fricas would negate the need for explicit coercion to double-floats. For Reduce, the lack of nested procedures in the algebraic mode made progress slow; importantly, the switch to symbolic mode made the implementation quite straightforward, with reasonably good debugging. The Maxima and Sage versions were particularly short, given that they already provide a root solver.

This begs the question: when would one use any of these languages for mixed numerical/symbolic analysis? In my opinion, Boot is the least likely to be used, although it does play closely and well with Common Lisp (a la Reduce and Maxima). One could code for numerical analysis in Boot and Common Lisp - however Boot's lack of lexical scoping may be a detraction. Moreover, by my understanding, Lisp or Boot are unable to evaluate Spad or Axiom functions. Second, for an R user, Spad's type system seems fussy (and extremely elegant); I also found that debugging could be slow. The lack of ability to call Spad functions from Lisp or Boot is an important restriction, requiring that a large body of code in Lisp (or Fortran via f2cl), such as for optimisation, would need to be hand translated to Spad (see [SandBoxMLE] for an example). Reduce, Maxima and Sage provide polished environments worthy of further consideration. 

First, the Spad implementation:

changed:
-The Boot translation was more fiddly - but, then again, I had never used Boot before.
Second, the Boot translation was more fiddly - but, then again, I had never used Boot before.

changed:
-Using this code in Axiom:
Using the Boot code from Axiom:

changed:
-For Reduce (which was also my first Reduce program):
Third, for Reduce (which was also my first Reduce program):

changed:
-   %%
   %% 

changed:
-The Maxima implementation was fairly brief:
Fourth, the Maxima implementation was fairly brief:

changed:
-The implementation in Common Lisp was a more direct translation of the R code:
Fifth, the implementation in Common Lisp was a more direct translation of the R code:

changed:
-
-
Finally, I have also included an implementation in Sage and commented out early code that would
allow this to be used in Python. 

\begin{sageblock}
import scipy
from scipy.optimize import brentq

## the following is required for use in Python/Scipy outside of Sage
# from math import log, exp, cos, pi 
# infinity="infinity" 
# def binomial(n,x): 
#    total=1
#    for i in range(min(x,n-x)):
#       total=total*(n-i)/(i+1)
#    return total

def dhyper(x, m, n, k):
   return 1.0 * binomial(m, x) * binomial(n, k - x) / binomial(m + n, k)
def phyper(x, m, n, k, lowerTail):
   if lowerTail: 
      return sum([dhyper(i, m, n, k) for i in range(1,x+1)])
   else: 
      return sum([dhyper(i, m, n, k) for i in range(x+1,k+1)])
def fishertest(a,b,c,d,alternative,oddsratio,confLevel):
    m = a+c
    n = b+d
    k = a+b 
    x00 = a
    lo = max(0, k-n)
    hi = min(k, m)
    support = range(lo,hi+1)
    logdc = [log(dhyper(i, m, n, k)) for i in support]
    doubleEps = 1.0e-10
    def dnhyper(ncp):
        d = [logdc[i]+log(ncp)*support[i] for i in range(len(logdc))]
        d2 = [exp(di-max(d)) for di in d]
        return [d2i/sum(d2) for d2i in d2]
    def mnhyper(ncp):
        if ncp==0:
            return lo
        if ncp==infinity:
            return hi
        else: 
            d = dnhyper(ncp)
            return sum([support[i]*d[i] for i in range(len(d))])
    def pnhyper(q,ncp,upperTail):
        if ncp==1:
            if upperTail:
                return phyper(q-1, m, n, k, False)
            else: 
                return phyper(q, m, n, k, True)
        elif ncp==0:
            if upperTail:
                return 1 if q<=lo else 0
            else: 
                return 1 if q>=lo else 0
        elif ncp==infinity:
            if upperTail:
                return 1 if q<=hi else 0
            else:
                return 1 if q>= hi else 0
        else:
          d = dnhyper(ncp)
          if upperTail:
              return sum([d[i] for i in range(len(d)) if support[i]>=q])
          else:
              return sum([d[i] for i in range(1,len(d)) if support[i]<=q])
    def ncpL(alpha):
        if x00==lo: 
          return 0
        else:
          p = pnhyper(x00, 1, True)
          if p>alpha:
              return brentq(lambda y: pnhyper(x00,y,True) - alpha, 0, 1) 
          elif p<alpha:
              return 1/brentq(lambda y: pnhyper(x00,1/y,True) - alpha, doubleEps, 1)
          else:
              return 1
    def ncpU(alpha):
        if x00==hi:
          return infinity
        else:
          p = pnhyper(x00, 1, False)
          if p<alpha:
              return brentq(lambda y: pnhyper(x00,y,False) - alpha, 0, 1) 
          elif p>alpha:
              return 1/brentq(lambda y: pnhyper(x00,1/y,False) - alpha, doubleEps, 1)
          else:
              return 1
    def pvalue(): 
          if alternative == "less":
              return pnhyper(x00,oddsratio,False)
          elif alternative == "greater":
              return pnhyper(x00,oddsratio,True)
          elif alternative == "two-sided":
              relErr=1+1.0e-7
              dn=dnhyper(oddsratio)
              dstar=dn[x00-lo]*relErr
              return sum([di for di in dn if di<dstar])
          else: 
              return -1
    def cInterval():
        if alternative == "less":
            return [0, ncpU(1-confLevel)]
        elif alternative == "greater":
            return [ncpL(1-confLevel), infinity]
        elif alternative == "two-sided":
            return [ncpL((1-confLevel)/2.0), ncpU((1-confLevel)/2.0)]
        else:
            return [-1,-1]
    def mle(): 
        if x00==lo:
            return 0
        elif x00==hi:
            return infinity
        else:
          mu = mnhyper(1)
          if mu>x00:
              return brentq(lambda y: mnhyper(y) - x00,0,1)
          elif mu<x00:
              return 1/brentq(lambda y: mnhyper(1/y) - x00,doubleEps,1)
          else:
              return 1
    return dict(pvalue=pvalue(),cInterval=cInterval(),mle=mle())

## do some checks
def testTolerance(x,y,tol):
   return abs(x-y)<=tol
fit=fishertest(10,10,10,20,"two-sided",1,0.95)
print [testTolerance(brentq(cos,0,2)*2,pi,0),
       testTolerance(binomial(100, 5), 75287520, 0),
       testTolerance(binomial(100, 95), 75287520, 0),
       testTolerance(dhyper(5, 10, 7, 8), 0.3628137, 1.0e-7),
       testTolerance(log(dhyper(5, 10, 7, 8)),-1.013866,  1.0e-7),
       testTolerance(phyper(5, 10, 7, 8, True),0.7821884, 1.0e-7),
       testTolerance(phyper(5, 10, 7, 8, False),0.2178116, 1.0e-7),
       testTolerance(fit['pvalue'], 0.2575, 1.0e-3),
       testTolerance(fit['cInterval'][0], 0.5383996, 1.0e-6),
       testTolerance(fit['cInterval'][1], 7.4363242, 1.0e-4),
       testTolerance(fit['mle'], 1.971640, 1.0e-4)]
## print some tests
print fishertest(10,10,10,20,"two-sided",1,0.95)
print fishertest(10,10,10,20,"less",1,0.95)
print fishertest(10,10,10,20,"greater",1,0.95)
\end{sageblock}

This would give the following output:

[0 <= 0, True, True, True, True, True, True, 4.92428109829e-05 <= 0.00100000000000000, 
   True, True, True]
{'mle': 1.9716269432603342, 'pvalue': 0.257549242811, 
   'cInterval': [0.53839938167817269, 7.4363408387619616]}
{'mle': 1.9716269432603342, 'pvalue': 0.929480494158038, 
   'cInterval': [0, 6.1438085831669706]}
{'mle': 1.9716269432603342, 'pvalue': 0.188301375769915, 
   'cInterval': [0.64599378194777934, +Infinity]}

How useful are the different CAS languages for implementing numerical routines? Prompted by a comparison of R and C for implementing Fisher's exact test for 2x2 tables (http://fluff.info/blog/arch/00000172.htm), I thought that it would be interesting to implement this particular test in Spad, Boot, Reduce, Maxima, Common Lisp and Sage (see below). Each set of code was required to implement a univariate root finder and the hypergeometric distribution to calculate the p-value under different alternatives, together with the 95% confidence interval and the maximum likelihood estimator for the odds ratio. The reference implementation is R, where the code and output would be:
\begin{verbatim}
> fisher.test(matrix(c(10,10,10,20),nrow=2))
        Fisher's Exact Test for Count Data

data:  matrix(c(10, 10, 10, 20), nrow = 2) 
p-value = 0.2575
alternative hypothesis: true odds ratio is not equal to 1 
95 percent confidence interval:
 0.5383996 7.4363242 
sample estimates:
odds ratio 
  1.971640 
\end{verbatim}
As a caveat: I have little experience with these programs. Any changes or improvements to the programs would be welcomed.
To summarise, all six languages (Spad, Boot, Reduce, Maxima, Common Lisp and Sage) provide arbitrary length integers and fractions, ensuring that the hypergeometric distribution was straightforward to implement. The Lisp and R implementations were very similar, which is not surprising, given that the two languages are closely related. In contrast, the nested functions in Spad seemed clumsy, with the requirement to use the #1 and #2 argument references (although this has changed recently in Fricas); I did, however, appreciate Spad's lexical scoping and facility to fall back to a symbolic analysis. An Aldor implementation may be cleaner than the Spad implementation. Boot's implementation was initially difficult, as I was unclear how to pass values to the nested functions with using function argument (which was not possible for the univariate root finder :-(). The use of the "$" prefix on derived variables seemed particularly clumsy. Finally, my version of Fricas:Boot defaulted to single precision floats, which caused problems with precision. Type specification in OpenAxiom:Boot and more recent versions of Fricas would negate the need for explicit coercion to double-floats. For Reduce, the lack of nested procedures in the algebraic mode made progress slow; importantly, the switch to symbolic mode made the implementation quite straightforward, with reasonably good debugging. The Maxima and Sage versions were particularly short, given that they already provide a root solver.
This begs the question: when would one use any of these languages for mixed numerical/symbolic analysis? In my opinion, Boot is the least likely to be used, although it does play closely and well with Common Lisp (a la Reduce and Maxima). One could code for numerical analysis in Boot and Common Lisp - however Boot's lack of lexical scoping may be a detraction. Moreover, by my understanding, Lisp or Boot are unable to evaluate Spad or Axiom functions. Second, for an R user, Spad's type system seems fussy (and extremely elegant); I also found that debugging could be slow. The lack of ability to call Spad functions from Lisp or Boot is an important restriction, requiring that a large body of code in Lisp (or Fortran via f2cl), such as for optimisation, would need to be hand translated to Spad (see [SandBoxMLE] for an example). Reduce, Maxima and Sage provide polished environments worthy of further consideration. 
First, the Spad implementation:
spad
)abbrev package TESTP TestPackage
R ==> Float
I ==> Integer
fisherRec ==> Record(PValue:R, CI:List R, Estimate:R)
TestPackage: with
   ridder: (R->R,R,R) -> R
   msign: (R,R) -> R
   choose:(I, I)->Fraction I 
   --chooseNew:(Integer, Integer)->Fraction Integer 
   dhyper:(I, I, I, I)->Fraction Integer
   phyper:(I, I, I, I, Boolean)->Fraction Integer
   fisherTest:(I,I,I,I, String, R, Boolean, R)-> fisherRec
   testTolerance:(R, R, R)->Boolean   
   test1: () -> Boolean
   test2: () -> Boolean
   test3: () -> Boolean
   test4: () -> Boolean
   test5: () -> Boolean
   test6: () -> Boolean
   test7: () -> Boolean
   test8: () -> Boolean
   test9: () -> Boolean
   test10: () -> Boolean
   alltests: () -> List Boolean
  == add
   import TrigonometricFunctionCategory -- for test1()
   --import OrderedCompletion(Float) for plusInfinity()$OrderedCompletion(Float)
   ridder(func, x1, x2) ==
    eps:= 1.0e-16::R
    maxit:= 30::Integer
    --verbose:= false
    fl:R := func x1
    fh:R := func x2
    xl:R := x1
    xh:R := x2
    ans:R := -1.11e30::R
    xnew:R := 0.0e0::R
    iterNum:= 0::Integer
    if fl=0.0::R then return x1
    else if fh=0.0::R then return x2
    else if (fl*fh) > 0.0::R then error "Initial points are not either side of zero."
    --if (fl*fh) < 0.0 then
    else repeat
                xm:= 0.5::R *(xl+xh)
                fm:= func xm
                ss:= sqrt((fm*fm) - (fl*fh))
                if ss =0.0::R then return ans
                xnew:= xm + (((xm - xl) * (if (fl>fh) then 1.0::R else -1.0::R) * fm) / ss)
                if abs(xnew-ans) <= eps then return ans
                ans:= xnew 
                fnew:= func ans
                if fnew=0.0::R then return ans
                if msign(fm,fnew) ~= fm then
                    xl:= xm 
                    fl:= fm 
                    xh:= ans 
                    fh:= fnew
                else if msign(fl, fnew) ~= fl then
                    xh:= ans 
                    fh:= fnew
                else if msign(fh, fnew) ~= fh then
                    xl:= ans 
                    fl:= fnew
                iterNum:=iterNum+1::Integer
                if iterNum >=maxit then 
                        error "Maximum iterations exceeded"
                --if verbose then FORMAT(true,"~,8f ~,8f ~,8f ~,8f~%", xl, xh, fl, fh)$Lisp
                if abs(xh-xl) <= eps then return ans
   msign(x, y) ==
    (abs x) * (if y>0.0::R then 1.0::R else if y<0.0::R then -1.0::R else 0.0::R)
   choose(n, x) ==
    total:Fraction Integer := 1/1
    for denom in 1..x repeat
        total:=total*((n-denom+1)/(denom))::Fraction Integer
    return total
   --chooseNew(n, x) == product((n-i+1)::Fraction Integer/i::Fraction Integer,i=1..x)
   dhyper(x, m, n, k) ==
    choose(m, x) * choose(n, k - x) / choose(m + n, k)
   phyper(x, m, n, k, lowerTail) ==
    i:PositiveInteger
    --total:Fraction Integer:=0/1
    if lowerTail then 
        reduce("+",[dhyper(i, m, n, k) for i in 1..x])
    else 
        reduce("+",[dhyper(i, m, n, k) for i in (x+1)..k])
   fisherTest(a,b,c,d, alternative, OR, confInt, confLevel) ==
        m:I := a+c -- first column
        n:I := b+d -- second column
        k:I := a+b -- first row
        x00:I := a
        lo:I := max(0, k-n)
        hi:I := min(k, m)
        support:List I := [i for i in lo..hi]
        logdc:List R:= [log(dhyper(i, m, n, k)::R) for i in support]
        doubleEps:R := 1.0e-50::R 
        plusInfinity:R := 1.0e6::R -- arbitrary
        dnhyper:(R->List R) :=
             ncp:R := #1
             d:List R := [logdc(i)+log(ncp)*support(i)::R for i in 1..#logdc]
             maxd:R := reduce(max,d)
             d2:List R :=[exp(di-maxd) for di in d]
             sumd2:R := reduce("+",d2)
             [d2i/sumd2 for d2i in d2]
        mnhyper:(R->R) :=
             ncp:R := #1
             if ncp=0.0::R then lo::R
             --else if ncp=%plusInfinity then hi::R
             else 
                d:List R := dnhyper(ncp)
                reduce("+",[support(i)::R*d(i) for i in 1..#d])
        pnhyper:((Integer,R,Boolean)->R) :=
             q:I := #1
             ncp:R := #2
             upperTail:Boolean := #3
             if ncp=1.0 then 
                if upperTail then phyper(q-1, m, n, k, false)::R
                else phyper(q, m, n, k, true)::R
             else if ncp=0.0 then
                if upperTail then 
                    if q<=lo then 1.0::R else 0.0::R
                else if q>=lo then 1.0::R else 0.0::R
--           else if ncp=%plusInfinity then
--              if upperTail then 
--                  if q<=hi then 1.0::R else 0.0::R
--              else if q>= hi then 1.0::R else 0.0::R
             else 
                d:List R := dnhyper(ncp)
                if upperTail then
                      reduce("+",[d(i) for i in 1..#d | support(i)>=q])
                else reduce("+",[d(i) for i in 1..#d | support(i)<=q])
        mle:(I->R) :=
             x:I := #1
             if x=lo then 0.0::R
             else if x=hi then plusInfinity
             else
                mu:R := mnhyper(1.0::R)
                if mu>x::R then 
                        f:(R->R) := mnhyper(#1) - x::R
                        ridder(f,0,1)
                else if mu<x::R then
                        f:(R->R) := mnhyper(1/#1) - x::R
                        1/ridder(f,doubleEps,1.0::R)
                else 1.0::R
        ncpU:(I,R)->R :=
             x:I := #1
             alpha:R := #2
             if x=hi then plusInfinity
             else
                     p:R := pnhyper(x, 1.0::R, false)
                     if p<alpha then 
                        f:(R->R) := pnhyper(x,#1,false) - alpha
                        ridder(f, 0.0::R, 1.0::R)
                     else if p>alpha then 
                                f:(R->R) := pnhyper(x,1/#1,false) - alpha
                                1/ridder(f, doubleEps, 1.0::R)
                     else 1.0::R
        ncpL:(Integer, R)->R :=
             x:I := #1
             alpha:R := #2
             if x=lo then 0.0::R
             else 
                p:R := pnhyper(x, 1, true)
                if p>alpha then
                        f:(R->R) := pnhyper(x,#1,true) - alpha
                        ridder(f, 0,1)
                else if p<alpha then
                        f:(R->R) := pnhyper(x,1/#1,true) - alpha
                        1/ridder(f, doubleEps,1.0::R)
                else 1.0::R
        pValue:R :=
             if alternative="less" then pnhyper(x00, OR,false) 
             else if alternative="greater" then pnhyper(x00, OR,true)
             else if alternative="two-sided" then
                relErr:= 1+1.0e-7::R
                dn:= dnhyper(OR)
                dstar:= dn(x00-lo+1)*relErr
                reduce("+",[di for di in dn | di<dstar])
             else -1.0::R
        cInterval:List R :=
               if confInt then 
                   if alternative="less" then  [0.0::R, ncpU(x00, 1.0::R-confLevel)]
                   else if alternative="greater" then [ncpL(x00, 1.0::R-confLevel), plusInfinity]
                   else if alternative="two-sided" then 
                      alpha:=(1-confLevel)/2
                      [ncpL(x00, alpha), ncpU(x00, alpha)]
                else [-1.0::R,-1.0::R]
                --else []
        estimate:= mle(x00)
        [pValue, cInterval, estimate]
   testTolerance(x, y, atol) ==
    if abs(x-y) <= atol then true else false
   test1() ==
    testTolerance(2*ridder(cos,0.0::R,2.0::R),pi()$Pi::R, 1.0e-18)
   test2() == testTolerance(choose(100, 5)::R, 75287520::R, 0)
   test3() == testTolerance(dhyper(5, 10, 7, 8)::R, 0.3628137::R, 1.0e-7)
   test4() == testTolerance(log(dhyper(5, 10, 7, 8)::R),-1.013866::R,  1.0e-7)
   test5() == testTolerance(phyper(5, 10, 7, 8, true)::R,0.7821884::R, 1.0e-7)
   test6() == testTolerance(phyper(5, 10, 7, 8, false)::R,0.2178116::R, 1.0e-7) 
   test7() == testTolerance(fisherTest(10,10,10,20,"two-sided",1.0,true,0.95).PValue,
       0.2575, 1.0e-3)
   test8() == testTolerance(fisherTest(10,10,10,20,"two-sided",1.0,true,0.95).CI.1,
       0.5383996, 1.0e-6)
   test9() == testTolerance(fisherTest(10,10,10,20,"two-sided",1.0,true,0.95).CI.2,
       7.4363242, 1.0e-4)
   test10() == testTolerance(fisherTest(10,10,10,20,"two-sided",1.0,true,0.95).Estimate,
       1.971640, 1.0e-4)
   alltests() == [test1(), test2(), test3(), test4(), test5(), test6(),
       test7(), test8(), test9(), test10()]
spad
   Compiling FriCAS source code from file 
      /var/zope2/var/LatexWiki/303933172962875058-25px001.spad using 
      old system compiler.
   TESTP abbreviates package TestPackage 
   processing macro definition R ==> Float 

   processing macro definition I ==> Integer 

   processing macro definition fisherRec ==> Record(PValue: R,CI: List R,Estimate: R) 

------------------------------------------------------------------------
   initializing NRLIB TESTP for TestPackage 
   compiling into NRLIB TESTP 
   importing TrigonometricFunctionCategory
   compiling exported ridder : (Float -> Float,Float,Float) -> Float
Time: 0.06 SEC.

   compiling exported msign : (Float,Float) -> Float
Time: 0.02 SEC.

   compiling exported choose : (Integer,Integer) -> Fraction Integer
Time: 0.04 SEC.

   compiling exported dhyper : (Integer,Integer,Integer,Integer) -> Fraction Integer
Time: 0 SEC.

   compiling exported phyper : (Integer,Integer,Integer,Integer,Boolean) -> Fraction Integer
Time: 0.02 SEC.

   compiling exported fisherTest : (Integer,Integer,Integer,Integer,String,Float,Boolean,Float) -> Record(PValue: Float,CI: List Float,Estimate: Float)
Time: 0.20 SEC.

   compiling exported testTolerance : (Float,Float,Float) -> Boolean
Time: 0 SEC.

   compiling exported test1 : () -> Boolean
Time: 0.01 SEC.

   compiling exported test2 : () -> Boolean
Time: 0.01 SEC.

   compiling exported test3 : () -> Boolean
Time: 0.01 SEC.

   compiling exported test4 : () -> Boolean
Time: 0.05 SEC.

   compiling exported test5 : () -> Boolean
Time: 0.01 SEC.

   compiling exported test6 : () -> Boolean
Time: 0.01 SEC.

   compiling exported test7 : () -> Boolean
Time: 0 SEC.

   compiling exported test8 : () -> Boolean
Time: 0.02 SEC.

   compiling exported test9 : () -> Boolean
Time: 0.02 SEC.

   compiling exported test10 : () -> Boolean
Time: 0 SEC.

   compiling exported alltests : () -> List Boolean
Time: 0 SEC.

(time taken in buildFunctor:  0)

;;;     ***       |TestPackage| REDEFINED

;;;     ***       |TestPackage| REDEFINED
Time: 0 SEC.

   Warnings: 
      [1] ridder:  xh has no value
      [2] ridder:  xl has no value
      [3] fisherTest: The conditional modes (List (Float)) and (Integer) conflict

   Cumulative Statistics for Constructor TestPackage
      Time: 0.48 seconds

   finalizing NRLIB TESTP 
   Processing TestPackage for Browser database:
--->-->TestPackage((ridder (R (Mapping R R) R R))): Not documented!!!!
--->-->TestPackage((msign (R R R))): Not documented!!!!
--->-->TestPackage((choose ((Fraction I) I I))): Not documented!!!!
--->-->TestPackage((dhyper ((Fraction (Integer)) I I I I))): Not documented!!!!
--->-->TestPackage((phyper ((Fraction (Integer)) I I I I (Boolean)))): Not documented!!!!
--->-->TestPackage((fisherTest (fisherRec I I I I (String) R (Boolean) R))): Not documented!!!!
--->-->TestPackage((testTolerance ((Boolean) R R R))): Not documented!!!!
--->-->TestPackage((test1 ((Boolean)))): Not documented!!!!
--->-->TestPackage((test2 ((Boolean)))): Not documented!!!!
--->-->TestPackage((test3 ((Boolean)))): Not documented!!!!
--->-->TestPackage((test4 ((Boolean)))): Not documented!!!!
--->-->TestPackage((test5 ((Boolean)))): Not documented!!!!
--->-->TestPackage((test6 ((Boolean)))): Not documented!!!!
--->-->TestPackage((test7 ((Boolean)))): Not documented!!!!
--->-->TestPackage((test8 ((Boolean)))): Not documented!!!!
--->-->TestPackage((test9 ((Boolean)))): Not documented!!!!
--->-->TestPackage((test10 ((Boolean)))): Not documented!!!!
--->-->TestPackage((alltests ((List (Boolean))))): Not documented!!!!
--->-->TestPackage(constructor): Not documented!!!!
--->-->TestPackage(): Missing Description
------------------------------------------------------------------------
   TestPackage is now explicitly exposed in frame initial 
   TestPackage will be automatically loaded when needed from 
      /var/zope2/var/LatexWiki/TESTP.NRLIB/code

Using this code in Axiom:
axiom
-- test code is correct
alltests()
\begin{equation*}
\label{eq1}\left[  \mbox{\rm true} , \:  \mbox{\rm true} , \:  \mbox{\rm true} , \:  \mbox{\rm true} , \:  \mbox{\rm true} , \:  \mbox{\rm true} , \:  \mbox{\rm true} , \:  \mbox{\rm true} , \:  \mbox{\rm true} , \:  \mbox{\rm true} \right]?\end{equation*}
Type: List Boolean
axiom
-- show the example
fisherTest(10,10,10,20,"two-sided",1.0,true,0.95)
\begin{equation*}
\label{eq2}\begin{array}{@{}l}
\displaystyle
\left[{\hbox{\axiomType{PValue?}\ } ={0.25754924281098285122}}, \: \right.
\
\
\displaystyle
\left.{CI ={\left[{0.53839938167817271306}, \:{7.436340838743
9872468}\right]}}, \: \right.
\
\
\displaystyle
\left.{\hbox{\axiomType{Estimate}\ } ={1.9716269432603396417}}\right] \end{array}
\end{equation*}
Type: Record(PValue?: Float,CI: List Float,Estimate: Float)

Second, the Boot translation was more fiddly - but, then again, I had never used Boot before.
boot
doubleFloat(x) == COERCE(x,'DOUBLE_-FLOAT)
DF(x) == COERCE(x,'DOUBLE_-FLOAT)
ridder(func, x1, x2) ==
    --x2:=DF(x2)
    eps:= DF(1.0e-16)
    maxit:= 30
    fl := DF(FUNCALL(func,x1))
    fh := DF(FUNCALL(func,x2))
    xl := x1
    xh := x2
    ans := DF(-1.11e20)
    xnew := 0.0e0
    iterNum:= 0
    if fl=0.0 then return x1
    else if fh=0.0 then return x2
    else if (fl*fh) > 0.0 then error "Initial points are not either side of zero."
    --if (fl*fh) < 0.0 then
    else repeat
                xm:= 0.5 *(xl+xh)
                fm:= FUNCALL(func,xm)
                ss:= SQRT((fm*fm) - (fl*fh))
                if ss =0.0 then return ans
                xnew:= xm + (((xm - xl) * (if (fl>fh) then 1.0 else -1.0) * fm) / ss)
                if ABS(xnew-ans) <= eps then return ans
                ans:= xnew 
                fnew:= DF(FUNCALL(func,ans))
                if fnew=0.0 then return ans
                if msign(fm,fnew) ^= fm then
                    xl:= xm 
                    fl:= fm 
                    xh:= ans 
                    fh:= fnew
                else if msign(fl, fnew) ^= fl then
                    xh:= ans 
                    fh:= fnew
                else if msign(fh, fnew) ^= fh then
                    xl:= ans 
                    fl:= fnew
                iterNum:=iterNum+1
                if iterNum >=maxit then 
                        error "Maximum iterations exceeded"
                --if verbose then FORMAT(true,"~,8f ~,8f ~,8f ~,8f~%", xl, xh, fl, fh)$Lisp
                if ABS(xh-xl) <= eps then return ans
msign(x, y) ==
    (ABS x) * (if y>0.0 then 1.0 else if y<0.0 then -1.0 else 0.0)
choose(n, x) ==
    total := 1
    for denom in 1..x repeat
        total:=total*(n-denom+1)/denom
    return total
   --chooseNew(n, x) == product((n-i+1)::Fraction Integer/i::Fraction Integer,i=1..x)
dhyper(x, m, n, k) ==
    DF(choose(m, x) * choose(n, k - x)) / choose(m + n, k)
-- reduce(func,list) == 
--      value := list.0
--      for i in 1..(#list-1) repeat
--              value:=FUNCALL(func,value,list.i))
--      value
phyper(x, m, n, k, lowerTail) ==
    --total:Fraction Integer:=0/1
    if lowerTail then 
        +/[dhyper(i, m, n, k) for i in 1..x]
    else 
        +/[dhyper(i, m, n, k) for i in (x+1)..k]
dnhyper(ncp,logdc,support) ==
     d := [DF(logdc.i+LOG(ncp)*support.i) for i in 0..(#logdc-1)]
     maxd := APPLY(FUNCTION(MAX),d)
     d2 :=[EXP(di-maxd) for di in d]
     sumd2 := +/d2
     [d2i/sumd2 for d2i in d2]
testTolerance(x, y, atol) ==
    if ABS(x-y) <= atol then true else false
test1() == testTolerance(2*ridder('COS,0.0,2.0),3.1415926535897932385, 1.0e-7)
test2() == testTolerance(choose(100, 5), 75287520, 0)
test3() == testTolerance(dhyper(5, 10, 7, 8), 0.3628137, 1.0e-7)
test4() == testTolerance(LOG(dhyper(5, 10, 7, 8)),-1.013866,  1.0e-7)
test5() == testTolerance(phyper(5, 10, 7, 8, true),0.7821884, 1.0e-7)
test6() == testTolerance(phyper(5, 10, 7, 8, false),0.2178116, 1.0e-7) 
fisherTest(a,b,c,d, alternative, OR, confInt, confLevel) == main where
  main() ==
        $m := a+c -- first column
        $n := b+d -- second column
        $k := a+b -- first row
        $x00 := a
        $lo := MAX(0, $k-$n)
        $hi := MIN($k, $m)
        $support := [i for i in $lo..$hi]
        $logdc := [LOG(dhyper(i, $m, $n, $k)) for i in $support]
        $doubleEps := 1.0e-10
        $plusInfinity := 1.0e10
        pvalue :=
                if alternative='"less" then pnhyper($x00, OR,false) 
                else if alternative='"greater" then pnhyper($x00, OR,true)
                else if alternative='"two-sided" then
                        relErr:= 1+1.0e-7
                        d:= dnhyper(OR,$logdc,$support)
                        dstar:= ELT(d,$x00-$lo)*relErr
                        +/[di for di in d | di<dstar]
                else -1.0 -- no match
        estimate :=
                if $x00=$lo then 0
                -- else if $x00=hi then return($plusInfinity)
                else
                        mu:= mnhyper(1)
                        if mu>$x00 then ridder(FUNCTION(f1),0,1) 
                        else if mu<$x00 then 1/ridder(FUNCTION(f2),$doubleEps,1) 
                        else 1
        interval :=
               if confInt then 
                        $alpha := 1 - confLevel
                        if alternative='"less" then [0, ncpU($x00)]
                        else if alternative='"greater" then [ncpL($x00), $plusInfinity]
                        else if alternative='"two-sided" then 
                                $alpha :=(1-confLevel)/2.0
                                [ncpL($x00), ncpU($x00)]
                        else [-1,-1] 
                else [-2,-2]
        [pvalue,interval,estimate]
  pnhyper (q,ncp,upperTail) ==
             if ncp=1 then 
                if upperTail then phyper(q-1, $m, $n, $k, false)
                else phyper(q, $m, $n, $k, true)
             else if ncp=0 then
                if upperTail then 
                    if q<=$lo then 1 else 0
                else if q>=$lo then 1 else 0
--           else if ncp=$plusInfinity then
--              if upperTail then 
--                  if q<=hi then return(1) else return(0)
--              else if q>= hi then return(1) else return(0)
             else 
                d:= dnhyper(ncp, $logdc, $support)
                if upperTail then
                      +/[d.i for i in 0..(#d-1) | $support.i>=q]
                else +/[d.i for i in 0..(#d-1) | $support.i<=q]
  mnhyper(ncp) ==
             if ncp=0.0 then $lo
             --if ncp=$plusInfinity then return(hi::R)
             else
                d := dnhyper(ncp,$logdc,$support)
                +/[si*di for di in d for si in $support]
  f1(u) == mnhyper(u) - $x00
  f2(u) == mnhyper(1/u) - $x00
  ncpU x ==
             --if x=$hi then $plusInfinity
             p:= pnhyper(x, 1.0, false)
             if p<$alpha then 
                ridder(FUNCTION(fu1),0.0,1.0)
             else if p>$alpha then 
                1/ridder(FUNCTION(fu2), $doubleEps,1)
             else 1
  fu1 u == pnhyper($x00,u,false) - $alpha
  fu2 u == pnhyper($x00,1/u,false) - $alpha
  ncpL x == 
             if x=$lo then 0
             else 
                p:= pnhyper(x, 1, true)
                if p>$alpha then ridder(FUNCTION(fl1), 0,1)
                else if p<$alpha then 1/ridder(FUNCTION(fl2), $doubleEps,1)
                else 1
  fl1 u == pnhyper($x00,u,true) - $alpha
  fl2 u == pnhyper($x00,1/u,true) - $alpha
test7() == fisherTest(10,10,10,20,'"two-sided",1,true,0.95)
alltests() == [test1(), test2(), test3(), test4(), test5(), test6()]
boot
Value = T
; (DEFUN |fisherTest,fl2| ...) is being compiled.
;; The variable |$x00| is undefined.
;; The compiler will assume this variable is a global.
;; The variable |$alpha| is undefined.
;; The compiler will assume this variable is a global.
; (DEFUN |fisherTest,ncpL| ...) is being compiled.
;; The variable |$lo| is undefined.
;; The compiler will assume this variable is a global.
;; The variable |$doubleEps| is undefined.
;; The compiler will assume this variable is a global.
; (DEFUN |fisherTest,mnhyper| ...) is being compiled.
;; The variable |$logdc| is undefined.
;; The compiler will assume this variable is a global.
;; The variable |$support| is undefined.
;; The compiler will assume this variable is a global.
; (DEFUN |fisherTest,pnhyper| ...) is being compiled.
;; The variable |$m| is undefined.
;; The compiler will assume this variable is a global.
;; The variable |$n| is undefined.
;; The compiler will assume this variable is a global.
;; The variable |$k| is undefined.
;; The compiler will assume this variable is a global.
; (DEFUN |fisherTest| ...) is being compiled.
;; The variable |$hi| is undefined.
;; The compiler will assume this variable is a global.
;; The variable |$plusInfinity| is undefined.
;; The compiler will assume this variable is a global.
Value = 17616

Using the Boot code from Axiom:
axiom
alltests()$Lisp
\begin{equation}
\label{eq3}\left(T \  T \  T \  T \  T \  T \right)\end{equation}
Type: SExpression?
axiom
fisherTest(10,10,10,20,"two-sided",1,true,0.95::SF)$Lisp
\begin{equation}
\label{eq4}\left({
\begin{array}{@{}l}
\displaystyle
{0.2575492428109829}\  \cdot 
\
\
\displaystyle
{\left({0.53839938167817281}\ {7.4363408387439813}\right)}\  \cdot 
\
\
\displaystyle
{1.9716269432603395}
\end{array}
}\right)\end{equation}
Type: SExpression?

Third, for Reduce (which was also my first Reduce program):

                 symbolic;

nil

on rounded;

nil reduce
                 


                 symbolic procedure msign(x, y);
    (abs x) * (if y>0.0 then 1.0 else if y<0.0 then -1.0 else 0.0);

msign reduce
                 


                 %% Numerical root finding using Ridders method
   %% (Exit criteria hacked: how can one return from a repeat .. until statement?) 
symbolic procedure ridders(func, x1, x2);
   begin scalar eps, maxit, fl, fh, xl, xh, ans, xnew, iterNum, fnew;
      eps:= 1.0e-12;
      maxit:= 100;
      fl := funcall(func, x1);
      fh := funcall(func, x2);
      xl := x1;
      xh := x2;
      ans := -1.0e30;
      xnew := 0.0e0;
      iterNum := 0;
      if (fl*fh) > 0.0 then rederr "Initial points are not either side of zero.";
      if fl=0.0 then x1
      else if fh=0.0 then x2
         %if (fl*fh) < 0.0 then
      else repeat begin scalar xm, fm, ss;
         xm:= 0.5*(xl+xh);
         fm:= funcall(func, xm);
         ss:= sqrt((fm*fm) - (fl*fh));
         %if ss =0.0 then return ans;
         xnew:= xm + (((xm - xl) * (if (fl>fh) then 1.0 else -1.0) * fm) / ss);
         %if abs(xnew-ans) <= eps then return ans;
         ans:= xnew;
         fnew:= funcall(func, ans);
         %write(fnew);
         %if fnew=0.0 then return ans;
         if msign(fm,fnew) neq fm then begin;
            xl:= xm; 
            fl:= fm; 
            xh:= ans; 
            fh:= fnew; 
         end
         else if msign(fl, fnew) neq fl then begin;
            xh:= ans; 
            fh:= fnew;
         end    
         else if msign(fh, fnew) neq fh then begin;
            xl:= ans; 
            fl:= fnew; 
         end;
         iterNum:=iterNum+1;
         if iterNum >=maxit then rederr "Maximum iterations exceeded";
         %if verbose then write xl, xh, fl, fh;
      end until abs(fnew)<eps or abs(xh-xl) <= eps;
      return ans
   end;

ridders reduce
                 


                 symbolic procedure choose(n, x);
   begin scalar total, denom;
    total := 1.0;
    for denom:=1:x do <<
       total:=total/denom*(n-denom+1) >>;
    return total
   end;

choose reduce
                 


                 symbolic procedure dhyper(x, m, n, k);
   choose(m, x) * choose(n, k - x) / choose(m + n, k);

dhyper

procedure phyper(x, m, n, k, lowerTail);
   if lowerTail then 
      for i:=1:x sum dhyper(i, m, n, k)
   else 
      for i:=(x+1):k sum dhyper(i, m, n, k);

phyper reduce
                 


                 symbolic procedure testTolerance(x, y, atol);
   if abs(x-y) <= atol then t else nil;

testtolerance reduce
                 


                 symbolic procedure test1(); testTolerance(2*ridders(function(cos),0,2),
      cdr reval(algebraic pi),1e-8);

test1

symbolic procedure test2(); testTolerance(choose(100, 5), 75287520, 0);

test2

symbolic procedure test3(); testTolerance(dhyper(5, 10, 7, 8), 0.3628137, 1.0e-7);

test3

symbolic procedure test4(); testTolerance(log(dhyper(5, 10, 7, 8)),-1.013866,  1.0e-7);

test4

symbolic procedure test5(); testTolerance(phyper(5, 10, 7, 8, t),0.7821884, 1.0e-7);

test5

symbolic procedure test6(); testTolerance(phyper(5, 10, 7, 8, nil),0.2178116, 1.0e-7);

test6

symbolic procedure testSet1(); {test1(), test2(), test3(), test4(), test5(), test6()};

testset1 reduce
                 


                 testSet1();

(t t t t t t) reduce
                 


                 %% 
symbolic procedure fisherTest(a,b,c,d,alternative,oddsratio,confLevel);
   begin scalar m, n, k, x00, lo, hi, doubleEps,
         dnhyper,mnhyper,pnhyper,mle,ncpu,ncpl,
         support,d2,logdc,alpha,
         pvalue,cinterval;
      m := a+c; % first column
      n := b+d; % second column
      k := a+b; % first row
      x00 := a;
      lo := max(0, k-n);
      hi := min(k, m);
      support := for i:=lo:hi collect i;
      logdc := for each i in support collect log(dhyper(i, m, n, k));
      doubleEps := 1.0e-10; 
      dnhyper := function(lambda ncp;
         begin scalar maxd, sumd2, d, d2;
            d := for i:=1:length(logdc) collect nth(logdc,i)+log(ncp)*nth(support,i);
            maxd := apply(function(max),d);
            d2 :=for each di in d collect exp(di-maxd);
            sumd2 := for each i in d2 sum i;
            return for each d2i in d2 collect d2i/sumd2
         end);
      mnhyper := function(lambda ncp;
         begin scalar d, value;
            value := if ncp=0.0 then lo
            %else if ncp equal 'plusInfinity then hi
            else begin;
               d := funcall(dnhyper,ncp);
               return for i:=1:length(d) sum nth(support,i)*nth(d,i)
            end;
            return value
         end);
      pnhyper := function(lambda(q,ncp,upperTail);
         if ncp=1.0 then 
            (if upperTail then phyper(q-1, m, n, k, nil)
            else phyper(q, m, n, k, t))
         else if ncp=0.0 then
            (if upperTail then 
               (if q<=lo then 1.0 else 0.0)
            else (if q>=lo then 1.0 else 0.0))
         else if ncp equal 'plusInfinity then
            (if upperTail then 
               (if q<=hi then 1.0 else 0.0)
            else (if q>= hi then 1.0 else 0.0))
         else
         begin scalar d, value;
            d := funcall(dnhyper,ncp);
            value := if upperTail then
               for i:=1:length(d) sum (if nth(support,i)>=q then nth(d,i) else 0)
            else for i:=1:length(d) sum (if nth(support,i)<=q then nth(d,i) else 0);
            return value
         end);
      ncpL := function(lambda alpha;
         if x00=lo then 0
         else begin scalar p,value,f1,f2;
            f1 := function(lambda y; funcall(pnhyper,x00,y,t) - alpha);
            f2 := function(lambda y; funcall(pnhyper,x00,1/y,t) - alpha);
            p := funcall(pnhyper,x00, 1.0, t);
            value := if p>alpha then 
                  ridders(f1, doubleEps, 1) % zero bound caused problems
               else if p<alpha then 1/ridders(f2, doubleEps, 1)
               else 1;
            return value;
         end);
      ncpU := function(lambda alpha;
         if x00=hi then 'plusInfinity
         else begin scalar p,value,f1,f2;
            f1 := function(lambda y; funcall(pnhyper,x00,y,nil) - alpha);
            f2 := function(lambda y; funcall(pnhyper,x00,1/y,nil) - alpha);
            p := funcall(pnhyper,x00, 1.0, nil);
            value := if p<alpha then ridders(f1, 0, 1)
               else if p>alpha then 1/ridders(f2, doubleEps, 1)
               else 1;
            return value;
         end);
      pvalue := 
         if alternative equal 'less then funcall(pnhyper,x00,oddsratio,nil)
            else if alternative equal 'greater then funcall(pnhyper,x00,oddsratio,t)
            else if alternative equal 'twosided then
            begin scalar relerr,dstar,dn;
               relErr:= 1+1.0e-7;
               dn:= funcall(dnhyper,oddsratio);
               dstar:= nth(dn,x00-lo+1)*relErr;
               return for each di in dn sum (if di<dstar then di else 0)
            end
            else -1; 
      cInterval :=
         if alternative equal 'less then 
            {0.0, funcall(ncpU,1-confLevel)}
         else if alternative equal 'greater then 
            {funcall(ncpL,1-confLevel), 'plusInfinity}
         else if alternative equal 'twosided then 
            {funcall(ncpL,(1-confLevel)/2), funcall(ncpU,(1-confLevel)/2)}
         else {-1,-1}; % no match
      mle := 
         if x00=lo then 0.0
         else if x00=hi then 'plusInfinity
         else
         begin scalar mu, value, f1, f2;
            f1 := function(lambda y; funcall(mnhyper,y) - x00);
            f2 := function(lambda y; funcall(mnhyper,1/y) - x00);
            mu := funcall(mnhyper,1.0);
            value := if mu>x00 then ridders(f1,0,1)
            else if mu<x00 then
               1/ridders(f2,doubleEps,1.0)
            else 1.0;
            return value;
         end;
      return {pvalue,cinterval,mle};
   end;

fishertest reduce
                 


                 fishertest(10,10,10,20,'twosided,1,0.95);

(0.25754924281098 (0.53839938167829 7.4363408387441) 1.9716269432603)

fishertest(10,10,10,20,'less,1,0.95);

(0.92948113166106 (0.0 6.1438085831682) 1.9716269432603)

fishertest(10,10,10,20,'greater,1,0.95);

(0.18830137576992 (0.6459937819479 plusinfinity) 1.9716269432603) reduce
                 


                 symbolic procedure testSet2;
   begin scalar fit1;
      fit1 := fishertest(10,10,10,20,'twosided,1,0.95);
      return {testTolerance(first(fit1), 0.2575, 1.0e-3),
         testTolerance(caadr(fit1),0.5383996, 1.0e-6),
         testTolerance(cadadr(fit1),7.4363242, 1.0e-4),
         testTolerance(third(fit1),1.971640, 1.0e-4)}
   end;

testset2 reduce
                 


                 testSet2();

(t t t t) reduce
                 

Fourth, the Maxima implementation was fairly brief:
maxima
(%i2) \begin{flalign}
\mathrm{load}\left(
 \mathrm{distrib}\right)
\end{flalign}

(%o2) \begin{equation}
\label{eq5}
\mbox{{}/usr/share/maxima/5.10.0/share/contrib/distrib/distrib.mac{}}
\end{equation}

maxima
(%i4) \begin{flalign}
\mathrm{numer}:
 \mathbf{true}
\end{flalign}

(%o4) \begin{equation}
\label{eq6}
\mathbf{true}
\end{equation}

maxima
(%i6) \begin{flalign}
\mathrm{display2d}:
 \mathbf{false}
\end{flalign}

maxima(%i7) fishertest(a,b,c,d,alternative,oddsratio,confLevel):=block(
                 [m,n,k,x00,lo,hi,doubleEps,dnhyper,mnhyper,pnhyper,mle,ncpu,
                  ncpl,support,d2,logdc,alpha,pvalue,cinterval],m:c+a,n:d+b,
                 k:b+a,x00:a,lo:max(0,k-n),hi:min(k,m),
                 support:makelist(i,i,lo,hi),
                 logdc:create_list(log(pdf_hypergeometric(i,m,n,k)),i,
                                   support),doubleEps:1.0E-10,
                 dnhyper:lambda([ncp],
                                block([maxd,sumd2,d,d2],
                                      d:makelist(log(ncp)*support[i]+logdc[i],
                                                 i,1,length(logdc)),
                                      maxd:apply(max,d),
                                      d2:create_list(exp(di-maxd),di,d),
                                      sumd2:lsum(i,i,d2),
                                      create_list(d2i/sumd2,d2i,d2))),
                 mnhyper:lambda([ncp],
                                block([d,value],
                                      if equal(ncp,0) then lo
                                          else (if equal(ncp,inf) then hi
                                                    else block([],
                                                               d:dnhyper(ncp),
                                                               sum(
                                                                support[i]
                                                                 *d[i],i,1,
                                                                length(
                                                                 d)))))),
                 pnhyper:lambda([q,ncp,upperTail],
                                if equal(ncp,1)
                                    then (if upperTail
                                              then 1
                                              -cdf_hypergeometric(q-1,m,n,k)
                                              else cdf_hypergeometric(
                                              q,m,n,k))
                                    else (if equal(ncp,0)
                                              then (if upperTail
                                                        then (if q <= lo
                                                                  then 1
                                                                  else 0)
                                                        else (if q >= lo
                                                                  then 1
                                                                  else 0))
                                              else (if equal(ncp,inf)
                                                        then (if upperTail
                                                                  then (if 
                                                                  q <= hi
                                                                   then 1
                                                                   else 0)
                                                                  else (if 
                                                                  q >= hi
                                                                   then 1
                                                                   else 0))
                                                        else block(
                                                        [d],d:dnhyper(ncp),
                                                        if upperTail
                                                            then sum(
                                                            if support[i] >= q
                                                                then d[i]
                                                                else 0,i,1,
                                                            length(d))
                                                            else sum(
                                                            if support[i] <= q
                                                                then d[i]
                                                                else 0,i,1,
                                                            length(d)))))),
                 ncpL:lambda([alpha],
                             if equal(x00,lo) then 0
                                 else block([p],p:pnhyper(x00,1,true),
                                            if p > alpha
                                                then find_root(
                                                lambda([y],
                                                       pnhyper(x00,y,true)
                                                        -alpha),0,1)
                                                else (if p < alpha
                                                          then 1
                                                          /find_root(
                                                           lambda([y],
                                                                  pnhyper(
                                                                   x00,1/y,
                                                                   true)
                                                                   -alpha),
                                                           doubleEps,1)
                                                          else 1))),
                 ncpU:lambda([alpha],
                             if x00 = hi then inf
                                 else block([p],p:pnhyper(x00,1,false),
                                            if p < alpha
                                                then find_root(
                                                lambda([y],
                                                       pnhyper(x00,y,false)
                                                        -alpha),0,1)
                                                else (if p > alpha
                                                          then 1
                                                          /find_root(
                                                           lambda([y],
                                                                  pnhyper(
                                                                   x00,1/y,
                                                                   false)
                                                                   -alpha),
                                                           doubleEps,1)
                                                          else 1))),
                 pvalue:if alternative = 'less
                            then pnhyper(x00,oddsratio,false)
                            else (if alternative = 'greater
                                      then pnhyper(x00,oddsratio,true)
                                      else (if alternative = 'twosided
                                                then block([relerr,dstar,dn],
                                                           relErr
                                                            :9.9999999999999995E-8
                                                             +1,
                                                           dn
                                                            :dnhyper(
                                                             oddsratio),
                                                           dstar
                                                            :dn[1-lo+x00]
                                                             *relErr,
                                                           lsum(
                                                            if di < dstar
                                                                then di
                                                                else 0,di,dn))
                                                else -1)),
                 cInterval:if alternative = 'less then [0,ncpU(1-confLevel)]
                               else (if alternative = 'greater
                                         then [ncpL(1-confLevel),inf]
                                         else (if alternative = 'twosided
                                                   then [
                                                   ncpL((1-confLevel)/2),
                                                   ncpU((1-confLevel)/2)]
                                                   else [-1,-1])),
                 mle:if equal(x00,lo) then 0
                         else (if equal(x00,hi) then inf
                                   else block([mu],mu:mnhyper(1),
                                              if mu > x00
                                                  then find_root(
                                                  lambda([y],mnhyper(y)-x00),
                                                  0,1)
                                                  else (if mu < x00
                                                            then 1
                                                            /find_root(
                                                             lambda([y],
                                                                    mnhyper(
                                                                     1/y)
                                                                     -x00),
                                                             doubleEps,1)
                                                            else 1))),
                 [pvalue,cInterval,mle])
(%i8) display2d:true

maxima
(%i10) \begin{flalign}
\mathrm{fishertest}\left(10
  , 10 , 10 , 20 , \mbox{{}'{}}\mathrm{twosided} , 1 , 0.95\right)
\end{flalign}

(%o10) \begin{equation*}
\label{eq7}
\left[ 0.25754924281098
  , \left[ 0.53839938167817 , 7.436340838743986 \right]?  , 1.971626943260339
  \right]
\end{equation*}

maxima
(%i12) \begin{flalign}
\mathrm{fishertest}\left(10
  , 10 , 10 , 20 , \mbox{{}'{}}\mathrm{less} , 1 , 0.95\right)
\end{flalign}

(%o12) \begin{equation*}
\label{eq8}
\left[ 0.92948113166106
  , \left[ 0 , 6.143808583168206 \right]?  , 1.971626943260339
  \right]
\end{equation*}

maxima
(%i14) \begin{flalign}
\mathrm{fishertest}\left(10
  , 10 , 10 , 20 , \mbox{{}'{}}\mathrm{greater} , 1 , 0.95\right)
\end{flalign}

(%o14) \begin{equation*}
\label{eq9}
\left[ 0.18830137576992
  , \left[ 0.64599378194778 , \infty  \right]?  , 1.971626943260339
  \right]
\end{equation*}

maxima
(%i16) \begin{flalign}
\mathrm{display2d}:
 \mathbf{false}
\end{flalign}

maxima(%i17) testTolerance(x,y,atol):=if abs(x-y) <= atol then true

maxima(%i19) testSet():=block([fit1],fit1:fishertest(10,10,10,20,'twosided,1,0.95),
               [testTolerance(fit1[1],0.2575,0.001),
                testTolerance(fit1[2][1],0.5383996,9.9999999999999995E-7),
                testTolerance(fit1[2][2],7.4363242,1.0E-4),
                testTolerance(fit1[3],1.97164,1.0E-4)])
(%i20) display2d:true

maxima
(%i22) \begin{flalign}
\mathrm{testSet}\left(
 \right)
\end{flalign}

(%o22) \begin{equation*}
\label{eq10}
\left[ \mathbf{true} , 
 \mathbf{true} , \mathbf{true} , \mathbf{true} \right]
\end{equation*}

Fifth, the implementation in Common Lisp was a more direct translation of the R code:
lisp
;; from cl-statistics.lisp
(defun safe-exp (x)
  "Eliminates floating point underflow for the exponential function.
Instead, it just returns 0.0d0"
  (setf x (coerce x 'double-float))
  (if (< x (log least-positive-double-float))
      0.0d0
      (exp x)))
(defun ridder (func x1 x2 &key (eps 1.0d-16) (maxit 30) (verbose nil))
  (let (
        (fl (funcall func x1))
        (fh (funcall func x2))
        (xl x1)
        (xh x2)
        (ans -1.11d30)
        (xnew 0.0d0)
        (iter-num 0)
        )
    (cond
     ((= fl 0) x1)
     ((= fh 0) x2)
     ((> (* fl fh) 0.0d0) 
      (error "Functions of the start points are not either side of zero."))
     ((< (* fl fh) 0.0d0) 
      (loop
       (let* (
              (xm (* 0.5d0 (+ xl xh)))
              (fm (funcall func xm))
              (ss (sqrt (- (* fm fm) (* fl fh))))
              )
         (if (= ss 0.0d0) (return ans))
         (setf xnew (+ xm (/ (* (- xm xl) (if (> fl fh) 1.0d0 -1.0d0) fm) ss)))
         (if (<= (abs (- xnew ans)) eps) (return ans))
         (setf ans xnew fnew (funcall func ans))
         (if (= fnew 0.0d0) (return ans))
         (cond ((not (= (msign fm fnew) fm))
                (setf xl xm fl fm xh ans fh fnew))
               ((not (= (msign fl fnew) fl))
                (setf xh ans fh fnew))
               ((not (= (msign fh fnew) fh))
                (setf xl ans fl fnew)))
         (incf iter-num)
         (if (>= iter-num maxit) 
             (return (values nil "Maximum iterations exceeded"))) ;; (error)?
         (if verbose (format t "~,8f ~,8f ~,8f ~,8f~%" xl xh fl fh))
         (if (<= (abs (- xh xl)) eps) (return ans))))))))
(defun msign (x y)
  (* (abs x) (cond ((> y 0.0d0) 1.0d0) ((< y 0.0d0) -1.0d0) (t 0.0d0))))
;;(- (* (ridder #'cos 0.0d0 2.0d0) 2.0d0) pi)

(defun choose (n x)
  (loop 
     for denom from 1 to x 
     and numerator from n downto (- n (1- x)) 
     and total = 1 then (* total (/ numerator denom)) 
     finally (return total)))
(defun dhyper (x m n k &key (log nil))
  (let ((val
         (/ (* (choose m x) (choose n (- k x))) (choose (+ m n) k))))
    (if log 
        (log (coerce val 'double-float)) 
        val)))
(defun phyper (x m n k &key (lower-tail t))
  (if lower-tail 
      (loop for i from 1 to x summing (dhyper i m n k))
      (loop for i from (1+ x) to k summing (dhyper i m n k))))

(defun fisher-test (x &key (alternative 'two-sided) (or 1.0d0)
                    (conf-int t)
                    (conf-level 0.95d0) (uniroot #'ridder))
    "Fisher's exact test for a 2x2 integer array.
This is a hand translation of R's fisher.test() 
making use of CL's large integers for the hypergeometric distribution"
    (let* ((m (loop for i upto 1 summing (aref x i 0)))
           (n (loop for i upto 1 summing (aref x i 1)))
           (k (loop for i upto 1 summing (aref x 0 i)))
           (x00 (aref x 0 0)) ; cf replacing x by (aref x 0 0)
           (lo (max 0 (- k n)))
           (hi (min k m))
           (support (loop for i from lo to hi collect i))
           (log-dc (loop for i in support 
                      collect (dhyper i m n k :log t)))
           (double-eps 1.0d-50))
      (labels 
          ((dnhyper (ncp)
             (setf ncp (coerce ncp 'double-float))
             (let* ((d (loop for i in log-dc and j in support
                          collect (+ i (* (log  ncp) j))))
                    (max-d (apply #'max d))
                    (d2 (loop for i in d collect 
                             (safe-exp (- i max-d)))) ;; NB: safe-exp used here
                    (sum-d2 (reduce #'+ d2)))
               (loop for i in d2 collect (/ i sum-d2))))
           (mnhyper (ncp)
             (cond
               ((= ncp 0) lo)
               ((equal ncp 'infinity) hi)
               (t (loop for i in support and j in (dnhyper ncp)
                       summing (* i j)))))
           (pnhyper (q ncp &key (upper-tail nil))
             (cond 
               ((= ncp 1)
                (if upper-tail 
                    (coerce (phyper (1- x00) m n k :lower-tail nil) 'double-float)
                    (coerce (phyper x00 m n k) 'double-float)))
               ((= ncp 0)
                (if upper-tail 
                    (if (<= q lo) 1 0)
                    (if (>= q lo) 1 0)))
               ((equal ncp 'infinity)
                (if upper-tail 
                    (if (<= q hi) 1 0)
                    (if (>= q hi) 1 0)))
               (t 
                (let ((d (dnhyper ncp)))
                  (if upper-tail
                      (loop for d-i in d and support-i in support
                           when (>= support-i q)
                           summing d-i)
                      (loop for d-i in d and support-i in support
                           when (<= support-i q)
                           summing d-i))))))
           (mle (x)
             (cond
               ((= x lo) 0)
               ((= x hi) 'infinity)
               (t
                (let ((mu (mnhyper 1)))
                  (cond 
                    ((> mu x)
                     (funcall uniroot (lambda (u) (- (mnhyper u) x))
                              0 1))
                    ((< mu x)
                     (/ (funcall uniroot (lambda (u) (- (mnhyper (/ u)) x))
                              double-eps 1)))
                    (t 1))))))
           (ncp-u (x alpha)
             (and (= x hi) 'infinity)
             (let ((p (pnhyper x 1)))
               (cond 
                 ((< p alpha)
                  (funcall uniroot (lambda (u) (- (pnhyper x u) alpha))
                         0 1))
                 ((> p alpha)
                  (/ (funcall uniroot (lambda (u) (- (pnhyper x (/ u)) alpha))
                            double-eps 1)))
                 (t 1))))
           (ncp-l (x alpha)
             (and (= x lo) 0)
             (let ((p (pnhyper x 1 :upper-tail t)))
               (cond 
                 ((> p alpha)
                  (funcall uniroot (lambda (u) (- (pnhyper x u :upper-tail t) alpha))
                         0 1))
                 ((< p alpha)
                  (/ (funcall uniroot (lambda (u) (- (pnhyper x (/ u) :upper-tail t) alpha))
                            double-eps 1)))
                 (t 1)))))
        (let ((p-value 
               (ecase alternative
                 (less (pnhyper x00 or))
                 (greater (pnhyper x00 or :upper-tail t)) 
                 (two-sided 
                  (let* ((relErr  (1+ 1.0d-7))
                         (d (dnhyper or))
                         (dstar (* (elt d (- x00 lo)) relErr)))
                    (loop for di in d when (< di dstar) summing di)))))
              (c-interval
               (if conf-int
                   (ecase alternative
                     (less (list 0 (ncp-u x00 (- 1 conf-level))))
                     (greater (list (ncp-l x00 (- 1 conf-level)) 'infinity))
                     (two-sided 
                      (let ((alpha (/ (- 1 conf-level) 2)))
                        (list (ncp-l x00 alpha) (ncp-u x00 alpha)))))
                   nil))
              (estimate (mle x00)))
          (values p-value c-interval estimate)))))
;;(fisher-test #2a((10 10) (10 20)))
lisp
; (DEFUN RIDDER ...) is being compiled.
;; The variable FNEW is undefined.
;; The compiler will assume this variable is a global.
Value = 17624

With output:
\begin{verbatim}
CL-USER> (fisher-test #2a((10 10) (10 20)))
0.2575492428109829d0
(0.5383993816781727d0 7.4363408387439875d0)
1.9716269432603386d0
\end{verbatim}
Finally, I have also included an implementation in Sage and commented out early code that would
allow this to be used in Python. 
\begin{sageblock}
import scipy
from scipy.optimize import brentq
## the following is required for use in Python/Scipy outside of Sage
# from math import log, exp, cos, pi 
# infinity="infinity" 
# def binomial(n,x): 
#    total=1
#    for i in range(min(x,n-x)):
#       total=total*(n-i)/(i+1)
#    return total
def dhyper(x, m, n, k):
   return 1.0  binomial(m, x)  binomial(n, k - x) / binomial(m + n, k)
def phyper(x, m, n, k, lowerTail):
   if lowerTail: 
      return sum([dhyper(i, m, n, k) for i in range(1,x+1)]?)
   else: 
      return sum([dhyper(i, m, n, k) for i in range(x+1,k+1)]?)
def fishertest(a,b,c,d,alternative,oddsratio,confLevel):
    m = a+c
    n = b+d
    k = a+b 
    x00 = a
    lo = max(0, k-n)
    hi = min(k, m)
    support = range(lo,hi+1)
    logdc = [log(dhyper(i, m, n, k)) for i in support]?
    doubleEps = 1.0e-10
    def dnhyper(ncp):
        d = [logdc[i]?+log(ncp)*support[i]? for i in range(len(logdc))]
        d2 = [exp(di-max(d)) for di in d]?
        return [d2i/sum(d2) for d2i in d2]?
    def mnhyper(ncp):
        if ncp==0:
            return lo
        if ncp==infinity:
            return hi
        else: 
            d = dnhyper(ncp)
            return sum([support[i]?*d[i]? for i in range(len(d))])
    def pnhyper(q,ncp,upperTail):
        if ncp==1:
            if upperTail:
                return phyper(q-1, m, n, k, False)
            else: 
                return phyper(q, m, n, k, True)
        elif ncp==0:
            if upperTail:
                return 1 if q<=lo else 0
            else: 
                return 1 if q>=lo else 0
        elif ncp==infinity:
            if upperTail:
                return 1 if q<=hi else 0
            else:
                return 1 if q>= hi else 0
        else:
          d = dnhyper(ncp)
          if upperTail:
              return sum([d[i]? for i in range(len(d)) if support[i]?>=q])
          else:
              return sum([d[i]? for i in range(1,len(d)) if support[i]?<=q])
    def ncpL(alpha):
        if x00==lo: 
          return 0
        else:
          p = pnhyper(x00, 1, True)
          if p>alpha:
              return brentq(lambda y: pnhyper(x00,y,True) - alpha, 0, 1) 
          elif palpha:
              return 1/brentq(lambda y: pnhyper(x00,1/y,False) - alpha, doubleEps, 1)
          else:
              return 1
    def pvalue(): 
          if alternative == "less":
              return pnhyper(x00,oddsratio,False)
          elif alternative == "greater":
              return pnhyper(x00,oddsratio,True)
          elif alternative == "two-sided":
              relErr=1+1.0e-7
              dn=dnhyper(oddsratio)
              dstar=dn[x00-lo]?*relErr
              return sum([di for di in dn if di?)
          else: 
              return -1
    def cInterval():
        if alternative == "less":
            return [0, ncpU(1-confLevel)]?
        elif alternative == "greater":
            return [ncpL(1-confLevel), infinity]?
        elif alternative == "two-sided":
            return [ncpL((1-confLevel)/2.0), ncpU((1-confLevel)/2.0)]?
        else:
            return [-1,-1]?
    def mle(): 
        if x00==lo:
            return 0
        elif x00==hi:
            return infinity
        else:
          mu = mnhyper(1)
          if mu>x00:
              return brentq(lambda y: mnhyper(y) - x00,0,1)
          elif mu
## do some checks
def testTolerance(x,y,tol):
   return abs(x-y)<=tol
fit=fishertest(10,10,10,20,"two-sided",1,0.95)
print [testTolerance(brentq(cos,0,2)*2,pi,0),
       testTolerance(binomial(100, 5), 75287520, 0),
       testTolerance(binomial(100, 95), 75287520, 0),
       testTolerance(dhyper(5, 10, 7, 8), 0.3628137, 1.0e-7),
       testTolerance(log(dhyper(5, 10, 7, 8)),-1.013866,  1.0e-7),
       testTolerance(phyper(5, 10, 7, 8, True),0.7821884, 1.0e-7),
       testTolerance(phyper(5, 10, 7, 8, False),0.2178116, 1.0e-7),
       testTolerance(fit['pvalue'], 0.2575, 1.0e-3),
       testTolerance(fit['cInterval'][0], 0.5383996, 1.0e-6),
       testTolerance(fit['cInterval'][1], 7.4363242, 1.0e-4),
       testTolerance(fit['mle'], 1.971640, 1.0e-4)]
## print some tests
print fishertest(10,10,10,20,"two-sided",1,0.95)
print fishertest(10,10,10,20,"less",1,0.95)
print fishertest(10,10,10,20,"greater",1,0.95)
\end{sageblock}
This would give the following output:
[0 <= 0, True, True, True, True, True, True, 4.92428109829e-05 <= 0.00100000000000000, 
   True, True, True]
{'mle': 1.9716269432603342, 'pvalue': 0.257549242811, 
   'cInterval': [0.53839938167817269, 7.4363408387619616]}
{'mle': 1.9716269432603342, 'pvalue': 0.929480494158038, 
   'cInterval': [0, 6.1438085831669706]}
{'mle': 1.9716269432603342, 'pvalue': 0.188301375769915, 
   'cInterval': [0.64599378194777934, +Infinity]}

Some or all expressions may not have rendered properly, because Latex returned the following error:

sage: unset PYTHONPATH; PATH=/usr/local/bin:$PATH HOME=/var/lib/zope sage 8109687140352260270-18.0px.sage
[0 <= 0, True, True, True, True, True, True, 4.92428109829e-05 <= 0.00100000000000000, True, True, True]
{'mle': 1.9716269432603342, 'pvalue': 0.257549242811, 'cInterval': [0.53839938167817269, 7.4363408387619616]}
{'mle': 1.9716269432603342, 'pvalue': 0.929480494158038, 'cInterval': [0, 6.1438085831669706]}
{'mle': 1.9716269432603342, 'pvalue': 0.188301375769915, 'cInterval': [0.64599378194777934, +Infinity]}