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/dports/math/R-cran-VGAM/VGAM/man/
H A Dzanegbinomial.Rd7 Fits a zero-altered negative binomial distribution based on
8 a conditional model involving a binomial distribution and a
9 positive-negative binomial distribution.
38 \eqn{\mu_{nb}}{munb} of an ordinary negative binomial distribution.
137 or \eqn{Y} has a positive-negative binomial distribution with
140 negative binomial distribution differs from the zero-inflated negative
141 binomial distribution in that the former has zeros coming from one
142 source, whereas the latter has zeros coming from the negative binomial
143 distribution too. The zero-inflated negative binomial distribution
145 call the zero-altered negative binomial a \emph{hurdle} model.
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/dports/math/R-cran-statmod/statmod/man/
H A Dsage.test.Rd8 The counts in each group as a proportion of the whole are assumed to follow a binomial distribution.
30 An exact two-sided binomial test is computed for each tag.
32 …counts are in the same proportions as the library sizes, i.e., that the binomial probability for t…
38 When the counts are reasonably large, the binomial test, Fisher's test and Pearson's chisquare all …
39 When the counts are smaller, the binomial test is usually to be preferred in this context.
72 \code{\link{binom.test}} in the stats package performs univariate binomial tests.
/dports/math/maxima/maxima-5.43.2/share/sym/
H A Dkak.lisp97 (let* ((binnk (binomial n k))
121 (binomial (- n i)
164 (let* ((binnk (binomial n k))
180 ($piej l infkl 0 (binomial (- n l) (- k l)) n )
234 (let ((nxcoe ($mult_sym (car poule) (binomial pui ote)))
254 (let* ((binnk (binomial n k))
/dports/math/reduce/Reduce-svn5758-src/doc/manual2/
H A Dzeilberg.tex22 products of powers, factorials, $\Gamma$ function terms, binomial
56 gosper(binomial(k,n),k);
58 (k + 1)*binomial(k,n)
86 extended_gosper(binomial(k/2,n),k);
89 (k + 2)*binomial(---,n) + (k + 1)*binomial(-------,n)
125 sumrecursion(binomial(n,k),k,n);
231 expressions given in factorial-$\Gamma$-binomial-Pochhammer notation
245 factorial-$\Gamma$-binomial-Poch\-hammer notation are converted into
269 sumtohyper(binomial(n,k)^3,k);
292 $\Gamma$ function terms, binomial coefficients, and Pochhammer symbols
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/dports/math/R-cran-gss/gss/
H A DINDEX127 mkdata.binomial Making pseudo data for logistic regression
128 dev.resid.binomial Deviance residuals for logistic regression
129 dev.null.binomial Null model deviance for logistic regression
130 cv.binomial CV score for logistic regression
131 y0.binomial Preparing for KL projection of logistic fit
132 proj0.binomial Making pseudo data for projection of logistic fit
133 kl.binomial Computing KL distance between logistic fits
134 cfit.binomial Computing constant logistic fit
163 mkdata.nbinomial Making pseudo data for negative binomial regression
166 cv.nbinomial CV score for negative binomial regression
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/dports/databases/percona57-pam-for-mysql/boost_1_59_0/libs/math/doc/distributions/
H A Dbinomial.qbk3 ``#include <boost/math/distributions/binomial.hpp>``
11 typedef binomial_distribution<> binomial;
64 binomial distribution assumes that p is fixed for all trials.
68 whereas for the negative binomial,
71 The PDF for the binomial distribution is given by:
302 `pdf(binomial(n, p), k)`]]
307 `cdf(binomial(n, p), k)`]]
312 `cdf(complement(binomial(n, p), k))`]]
319 `quantile(binomial(n, p), P)`]]
326 `quantile(complement(binomial(n, p), P))`]]
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/dports/databases/percona57-server/boost_1_59_0/libs/math/doc/distributions/
H A Dbinomial.qbk3 ``#include <boost/math/distributions/binomial.hpp>``
11 typedef binomial_distribution<> binomial;
64 binomial distribution assumes that p is fixed for all trials.
68 whereas for the negative binomial,
71 The PDF for the binomial distribution is given by:
302 `pdf(binomial(n, p), k)`]]
307 `cdf(binomial(n, p), k)`]]
312 `cdf(complement(binomial(n, p), k))`]]
319 `quantile(binomial(n, p), P)`]]
326 `quantile(complement(binomial(n, p), P))`]]
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/dports/databases/xtrabackup/boost_1_59_0/libs/math/doc/distributions/
H A Dbinomial.qbk3 ``#include <boost/math/distributions/binomial.hpp>``
11 typedef binomial_distribution<> binomial;
64 binomial distribution assumes that p is fixed for all trials.
68 whereas for the negative binomial,
71 The PDF for the binomial distribution is given by:
302 `pdf(binomial(n, p), k)`]]
307 `cdf(binomial(n, p), k)`]]
312 `cdf(complement(binomial(n, p), k))`]]
319 `quantile(binomial(n, p), P)`]]
326 `quantile(complement(binomial(n, p), P))`]]
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/dports/databases/percona57-client/boost_1_59_0/libs/math/doc/distributions/
H A Dbinomial.qbk3 ``#include <boost/math/distributions/binomial.hpp>``
11 typedef binomial_distribution<> binomial;
64 binomial distribution assumes that p is fixed for all trials.
68 whereas for the negative binomial,
71 The PDF for the binomial distribution is given by:
302 `pdf(binomial(n, p), k)`]]
307 `cdf(binomial(n, p), k)`]]
312 `cdf(complement(binomial(n, p), k))`]]
319 `quantile(binomial(n, p), P)`]]
326 `quantile(complement(binomial(n, p), P))`]]
[all …]
/dports/databases/mysqlwsrep57-server/boost_1_59_0/libs/math/doc/distributions/
H A Dbinomial.qbk3 ``#include <boost/math/distributions/binomial.hpp>``
11 typedef binomial_distribution<> binomial;
64 binomial distribution assumes that p is fixed for all trials.
68 whereas for the negative binomial,
71 The PDF for the binomial distribution is given by:
302 `pdf(binomial(n, p), k)`]]
307 `cdf(binomial(n, p), k)`]]
312 `cdf(complement(binomial(n, p), k))`]]
319 `quantile(binomial(n, p), P)`]]
326 `quantile(complement(binomial(n, p), P))`]]
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/dports/math/py-Diofant/Diofant-0.13.0/diofant/stats/
H A Dfrv_types.py19 from ..functions import KroneckerDelta, binomial
239 binomial(n, k) * p**k * (1 - p)**(n - k) for k in range(n + 1)}
270 Rational(binomial(m, k) * binomial(N - m, n - k),
271 binomial(N, n))
/dports/devel/R-cran-ModelMetrics/ModelMetrics/man/
H A Dmauc.Rd18 setosa <- glm(I(Species == 'setosa') ~ Sepal.Length, data = iris, family = 'binomial')
19 versicolor <- glm(I(Species == 'versicolor') ~ Sepal.Length, data = iris, family = 'binomial')
20 virginica <- glm(I(Species == 'virginica') ~ Sepal.Length, data = iris, family = 'binomial')
/dports/math/py-sympy/sympy-1.9/sympy/simplify/tests/
H A Dtest_gammasimp.py2 Rational, gammasimp, factorial, gamma, binomial, pi, S,
32 assert gammasimp(binomial(n, k)) == \
66 assert gammasimp(rf(x + n, k)*binomial(n, k)).simplify() == Piecewise(
84 assert gammasimp(binomial(n + 2, k + S.Half)) == gamma(n + 3)/ \
86 assert gammasimp(binomial(n + 2, k + 2.0)) == \
90 assert gammasimp(binomial(0, x)) == sin(pi*x)/(pi*x)
/dports/science/R-cran-epicalc/epicalc/man/
H A Dpoisgof.rd4 \description{Poisson and negative binomial regression are used for modeling count data. This comman…
9 \item{model}{A Poisson or negative binomial model}
12 To test the significance of overdispersion of the errors of a Poisson or negative binomial model, t…
/dports/finance/R-cran-AER/AER/man/
H A DMurderRates.Rd53 fm_logit <- glm(model, data = MurderRates, family = binomial)
56 fm_logit2 <- glm(model, data = MurderRates, family = binomial,
60 fm_probit <- glm(model, data = MurderRates, family = binomial(link = "probit"))
63 fm_probit2 <- glm(model, data = MurderRates , family = binomial(link = "probit"),
/dports/lang/v8/v8-9.6.180.12/tools/clang/translation_unit/test_files/
H A Dbinomial.h8 int binomial(int n, int k) { in binomial() function
9 return k > 0 ? binomial(n - 1, k - 1) * n / k : 1; in binomial()
/dports/www/qt5-webengine/qtwebengine-everywhere-src-5.15.2/src/3rdparty/chromium/tools/clang/translation_unit/test_files/
H A Dbinomial.h8 int binomial(int n, int k) { in binomial() function
9 return k > 0 ? binomial(n - 1, k - 1) * n / k : 1; in binomial()
/dports/www/chromium-legacy/chromium-88.0.4324.182/tools/clang/translation_unit/test_files/
H A Dbinomial.h8 int binomial(int n, int k) { in binomial() function
9 return k > 0 ? binomial(n - 1, k - 1) * n / k : 1; in binomial()
/dports/lang/chibi-scheme/chibi-scheme-0.10/tests/snow/repo3/pingala/
H A Dbinomial.scm1 (define-library (pingala binomial)
2 (export binomial)
/dports/math/py-Diofant/Diofant-0.13.0/diofant/functions/combinatorial/
H A Dnumbers.py26 from .factorials import binomial, factorial
255 a = int(binomial(n + 3, n - 6))
265 return s / binomial(n + 3, n)
865 return binomial(2*n, n)/(n + 1)
1256 from .factorials import binomial
1266 return binomial(n + k - 1, k)
1267 return binomial(n, k)
1300 return binomial(n, 2)
1302 return (3*n - 1)*binomial(n, 3)/4
1304 return binomial(n, 2)*binomial(n, 4)
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/dports/math/py-mpmath/mpmath-1.2.1/mpmath/tests/
H A Dtest_gammazeta.py222 assert binomial(0,0) == 1
223 assert binomial(1,0) == 1
224 assert binomial(0,-1) == 0
225 assert binomial(3,2) == 3
226 assert binomial(5,2) == 10
227 assert binomial(5,3) == 10
228 assert binomial(5,5) == 1
229 assert binomial(-1,0) == 1
230 assert binomial(-2,-4) == 3
232 assert binomial(1100,1) == 1100
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/dports/math/octave-forge-statistics/statistics-1.4.3/inst/
H A Drandom.m45 ## @itemx "binomial"
46 ## @itemx "binomial distribution"
76 ## @itemx "negative binomial"
77 ## @itemx "negative binomial distribution"
124 case {"bino", "binomial", "binomial distribution"}
146 case {"nbin", "negative binomial", "negative binomial distribution"}
/dports/finance/quantlib/QuantLib-1.20/ql/experimental/credit/
H A Dlossdistribution.cpp30 BinomialDistribution binomial (p[0], p.size()); in binomialProbabilityOfNEvents() local
31 return binomial(n); in binomialProbabilityOfNEvents()
37 CumulativeBinomialDistribution binomial(p[0], p.size()); in binomialProbabilityOfAtLeastNEvents() local
38 return 1.0 - binomial(n-1); in binomialProbabilityOfAtLeastNEvents()
153 BinomialDistribution binomial (probability, n); in operator ()() local
156 probability_[i] = binomial(i); in operator ()()
/dports/math/dune-localfunctions/dune-localfunctions-f6628171b2773065ab43f97a77f47cd8c4283d8f/dune/localfunctions/brezzidouglasfortinmarini/cube/
H A Dlocalinterpolation.hh45 static constexpr unsigned int interiorDofs = dim*binomial(dim+order-2, order-2);
46 static constexpr unsigned int faceDofs = binomial(dim+order-2, order-1);
85 assert( i < binomial(d+kMax, kMax)); in unrank()
92 for(;k <= kMax && b <= i; ++k, b = binomial(d+k-1, k)) in unrank()
99 for(; m <= k && c <= i; ++m, c = binomial(d-p+m-2, m)) in unrank()
/dports/math/dune-common/dune-common-df65b1282ea89ad40d2cb6565983f7e633ccce31/dune/common/
H A Dmath.hh128 constexpr inline static T binomial (const T& n, const T& k) noexcept in binomial() function
136 return binomial(n, n-k); in binomial()
146 …constexpr inline static auto binomial (std::integral_constant<T, n>, std::integral_constant<T, k>)… in binomial() function
148 return std::integral_constant<T, binomial(n, k)>{}; in binomial()
152 …constexpr inline static auto binomial (std::integral_constant<T, n>, std::integral_constant<T, n>)… in binomial() function

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