/dports/math/py-sympy/sympy-1.9/sympy/series/tests/ |
H A D | test_limitseq.py | 1 from sympy import (symbols, Symbol, oo, Sum, harmonic, exp, Add, S, binomial, 64 e = binomial(2*n, n) / Sum(binomial(2*k, k), (k, 1, n)) 77 e = (Sum(binomial(3*k, k) * binomial(5*k, k), (k, 1, n)) / 78 (binomial(3*n, n) * binomial(5*n, n))) 113 assert (limit_seq(binomial(2*x, x) / Sum(binomial(2*y, y), (y, 1, x)), x) == 148 e = (Sum(2**k * binomial(2*k, k) / k**2, (k, 1, n)) / 149 (Sum(2**k/k*2, (k, 1, n)) * Sum(binomial(2*k, k), (k, 1, n))))
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/dports/math/R-cran-lme4/lme4/man/ |
H A D | glmer.nb.Rd | 4 \alias{negative.binomial}% re-exported, needed e.g. in update() 8 binomial family, building on \code{\link{glmer}}, and initializing via 35 value of the negative binomial parameter \code{theta}. 45 binomial and the random effects parameters in our (G)LMM models are 48 The negative binomial \eqn{\theta} can be extracted from a fit 55 To fit a negative binomial model with \emph{known} overdispersion 57 \code{glmer} with the \code{\link[MASS]{negative.binomial}} family from the 59 \code{glmer(...,family=MASS::negative.binomial(theta=1.75))}. 64 \code{\link[MASS]{negative.binomial}} (which we re-export currently) and 69 the negative binomial distribution. [all …]
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/dports/comms/wsjtz/wsjtx/lib/ftrsd/ftrsd_paper/ |
H A D | binomial_subs.c | 19 unsigned long long binomial(unsigned long long n, unsigned long long k) { in binomial() function 45 return binomial(*n,*k); in binomial_() 51 a=(double)binomial(*XX, *x); in hypergeo_() 52 b=(double)binomial(*NN-*XX, *s-*x); in hypergeo_() 53 c=(double)binomial(*NN, *s); in hypergeo_()
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/dports/graphics/R-cran-qcc/qcc/man/ |
H A D | qcc.overdispersion.test.Rd | 4 \title{Overdispersion test for binomial and poisson data} 5 \description{This function allows to test for overdispersed data in the binomial and poisson case.} 7 qcc.overdispersion.test(x, size, type=ifelse(missing(size), "poisson", "binomial")) 11 \item{size}{for binomial data, a vector of sample sizes} 12 …or testing, either \code{"poisson"} or \code{"binomial"}. By default, if \code{size} is provided a…
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/dports/math/dune-grid/dune-grid-de36e4b4e99da4cd7a120a39779345e701821115/dune/grid/albertagrid/ |
H A D | entity.hh | 112 int binomial=1; in subEntities() local 114 binomial *= i; in subEntities() 116 binomial /= i; in subEntities() 118 return binomial; in subEntities() 255 int binomial=1; in subEntities() local 257 binomial *= i; in subEntities() 259 binomial /= i; in subEntities() 261 return binomial; in subEntities()
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/dports/math/fricas/fricas-1.3.7/pre-generated/target/share/spadhelp/ |
H A D | CombinatorialFunction.help | 22 The binomial(n, r) returns the number of subsets of r objects 25 The binomial coefficients are the coefficients of the series expansion 26 of a power of a binomial, that is 39 Next we define a function that will output the list of binomial coefficients 42 pascalRow(n) == [right(binomial(n,i),4) for i in 0..n] 68 o )d op binomial
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/dports/devel/R-cran-doParallel/doParallel/inst/examples/ |
H A D | bootParallel.R | 17 result1 <- glm(x[ind,2]~x[ind,1], family=binomial(logit)) 28 result1 <- glm(x[ind,2]~x[ind,1], family=binomial(logit)) 41 result1 <- glm(x[ind,2]~x[ind,1], family=binomial(logit)) 55 result1 <- glm(x[ind,2]~x[ind,1], family=binomial(logit)) 67 result1 <- glm(x[ind,2]~x[ind,1], family=binomial(logit)) 77 result1 <- glm(x[ind,2]~x[ind,1], family=binomial(logit))
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/dports/math/R-cran-lme4/lme4/tests/ |
H A D | respiratory.R | 8 family=binomial,data=respiratory) 11 family=binomial,data=respiratory,nAGQ=5) 14 family=binomial,data=respiratory,nAGQ=8) 17 family=binomial,data=respiratory,nAGQ=16)
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H A D | HSAURtrees.R | 10 dfun <- glmer(modForm, data = trees513B, family = binomial, 38 system.time(mmodA <- glmer(modForm, data = trees513A, family = binomial())) 42 system.time(mmodB <- glmer(modForm, data = trees513B, family = binomial())) 48 lmer(modForm, data = trees513B, family = binomial()))
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H A D | simulate.R | 38 g1 <- glmer(y~(1|f),data=d,family=binomial) 44 (1 | herd), data = cbpp, family = binomial(link="logit")) 112 gm5 <- glmer(use ~ urban+age+livch+(1|district), Contraception, binomial) 126 family=binomial, 134 family=binomial, 143 family=binomial, 149 family=binomial, 155 family=binomial, 161 family=binomial,
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H A D | glmmExt.R | 31 nbinom = 1, family = binomial(link="cloglog")) 37 nbinom = 1, family = binomial(link="identity")) 113 gBc1 <- glmer(y ~ 1 + (1|block), data=dBc, family=binomial(link="cloglog")) 114 gBc2 <- glmer(y ~ x + (1|block), data=dBc, family=binomial(link="cloglog")) 128 gBi1 <- glmer(y ~ 1 + (1|block), data=dBi, family=binomial(link="identity")) 129 gBi2 <- glmer(y ~ x + (1|block), data=dBi, family=binomial(link="identity"))
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/dports/math/p5-Math-Random-MT-Auto/Math-Random-MT-Auto-6.23/t/ |
H A D | 10-deviates.t | 15 binomial shuffle)); 17 binomial shuffle)); 19 binomial shuffle)); 93 eval { $rn[$ii] = binomial(0.5, 15); }; 101 # Test several values from binomial() for small mean 104 eval { $rn[$ii] = binomial(0.01, 30); }; 112 # Test several values from binomial() 115 eval { $rn[$ii] = binomial(0.8, 50); }; 328 eval { $rn[$ii] = $prng->binomial(0.5, 15); }; 347 # Test several values from binomial() [all …]
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/dports/science/R-cran-epicalc/epicalc/man/ |
H A D | ci.rd | 4 \alias{ci.binomial} 14 \method{ci}{binomial}(x, size, precision, alpha = 0.05, ...) 21 …\item{x}{a variable for 'ci', number of success for 'ci.binomial', mean(s) for 'ci.numeric', and c… 32 …e of the variable 'x' and determine the appropriate method (between 'ci.binomial' and 'ci.numeric'… 34 The specific method, ie. 'ci.binomial', 'ci.numeric' or 'ci.poisson', should be used when the value… 36 'ci.binomial' and 'ci.numeric' employ exact probability computation while 'ci.numeric' is based on … 38 \value{'ci.binomial' and 'ci.poisson' return a data frame containing the number of events, the deno… 68 ci.binomial(death.by.group$sum.death1, death.by.group$length) 81 ci.binomial(40,40)
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/dports/math/lib2geom/lib2geom-1.1/src/2geom/ |
H A D | sbasis-to-bezier.cpp | 78 double binomial(unsigned int n, unsigned int k) in binomial() function 135 Tjk = binomial(n-2*k-1, j-k); in sbasis_to_bezier() 148 bz[j] /= binomial(n, j); in sbasis_to_bezier() 341 Tjk = sgn(j, k) * binomial(n-j-k, j-k) * binomial(n, k); in bezier_to_sbasis() 347 Tjk = sgn(j, k) * binomial(n-j-k-1, j-k-1) * binomial(n, k); in bezier_to_sbasis() 356 Tjk = sgn(q,k) * binomial(n, k); in bezier_to_sbasis() 359 sb[q][0] += (binomial(n, q) * bz[q]); in bezier_to_sbasis() 389 Tjk = sgn(j, k) * binomial(n-j-k, j-k) * binomial(n, k); in bezier_to_sbasis() 397 Tjk = sgn(j, k) * binomial(n-j-k-1, j-k-1) * binomial(n, k); in bezier_to_sbasis() 408 Tjk = sgn(q,k) * binomial(n, k); in bezier_to_sbasis() [all …]
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/dports/math/R-cran-VGAM/VGAM/man/ |
H A D | betabinomUC.Rd | 23 generation for the beta-binomial distribution 24 and the inflated beta-binomial distribution. 71 usual binomial distribution with probability \code{prob}. 80 % binomial distribution with probability \code{prob}. 97 usual binomial distribution. 138 behaves like the ordinary binomial distribution with respect 162 (i.e., ignoring the beta-binomial distribution). 172 beta-binomial distribution inflated only at 0. 200 The beta-binomial distribution is a binomial distribution whose 275 main = paste("Beta-binomial (size=",N,", shape1=", s1, [all …]
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/dports/science/dakota/dakota-6.13.0-release-public.src-UI/packages/external/LHS/ |
H A D | KEYWORD.DAT | 62 dist49 binomial .45 45 63 dist50 binomial .33 300 64 dist51 binomial .12 20 65 dist52 binomial .99 5000 66 dist53 negative binomial .45 45 67 dist54 negative binomial .33 300 68 dist55 negative binomial .12 20 69 dist56 negative binomial .99 5000
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/dports/math/singular/Singular-Release-4-2-1/IntegerProgramming/ |
H A D | ideal.h | 148 ideal& add_new_generator(binomial& bin); 149 ideal& add_generator(binomial& bin); 179 ideal& reduce_by(const binomial&, list_iterator&, list_iterator&); 303 binomial& reduce(binomial& bin, BOOLEAN complete=TRUE) const;
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/dports/math/reduce/Reduce-svn5758-src/doc/manual/ |
H A D | zeilberg.tex | 198 3: gosper(sub(n=n+1,binomial(n,k)^2/binomial(2*n,n))- 199 binomial(n,k)^2/binomial(2*n,n),k); 202 ((binomial(n + 1,k) *binomial(2*n,n) 205 - binomial(2*(n + 1),n + 1)*binomial(n,k) )*(2*k - 3*n - 1) 321 (k + 2)*binomial(---,n) + (k + 1)*binomial(-------,n) 448 20: sumrecursion(binomial(n,k)^2*binomial(2*k,n),k,n); 462 22: sub(n=0,k=0,binomial(n,k)^2*binomial(2*k,n)); 470 24: sub(n=1,k=0,binomial(n,k)^2*binomial(2*k,n))+ 471 sub(n=1,k=1,binomial(n,k)^2*binomial(2*k,n)); 516 26: sumrecursion(binomial(n,k)*binomial(k/2,n),k,n); [all …]
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/dports/math/R-cran-sm/sm/inst/scripts/ |
H A D | smackgam.q | 8 + lo(temperature), family = binomial) 10 family = binomial) 12 family = binomial) 14 family = binomial)
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/dports/math/R-cran-LearnBayes/LearnBayes/man/ |
H A D | pbetap.Rd | 3 \title{Predictive distribution for a binomial sample with a beta prior} 6 future binomial experiment 14 \item{n}{size of future binomial sample} 15 \item{s}{vector of number of successes for future binomial experiment}
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H A D | pdiscp.Rd | 3 \title{Predictive distribution for a binomial sample with a discrete prior} 6 future binomial experiment 15 \item{n}{size of future binomial sample} 16 \item{s}{vector of number of successes for future binomial experiment}
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/dports/science/nwchem-data/nwchem-7.0.2-release/src/NWints/ecp/ |
H A D | ecp_cart_xpd.F | 28 integer binomial(231) 34 data binomial/1, 1,1, 1,2,1, 1,3,3,1, 1,4,6,4,1, 1,5,10,10,5,1, 87 ifac = binomial(ii+a) 94 jfac = binomial(jj+b) 96 kfac = binomial(kk+c)
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/dports/science/nwchem/nwchem-7b21660b82ebd85ef659f6fba7e1e73433b0bd0a/src/NWints/ecp/ |
H A D | ecp_cart_xpd.F | 28 integer binomial(231) 34 data binomial/1, 1,1, 1,2,1, 1,3,3,1, 1,4,6,4,1, 1,5,10,10,5,1, 87 ifac = binomial(ii+a) 94 jfac = binomial(jj+b) 96 kfac = binomial(kk+c)
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/dports/math/mcsim/mcsim-6.2.0/examples/binomial/ |
H A D | binomial.MCMC.in | 2 # binomial.MCMC.in 4 # Input file for MCMC simulations of a binomial model. P is computed by 5 # a link function defined in the binomial.model file. 12 MCMC ("binomial.MCMC.out", # output file
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/dports/biology/p5-BioPerl/BioPerl-1.7.7/t/ |
H A D | Species.t | 24 is $sps->binomial, 'Homo sapiens'; 27 is $sps->binomial, 'Homo sapiens'; 28 is $sps->binomial('FULL'), 'Homo sapiens sapiensis'; 34 is $sps->binomial, 'Homo sapiens'; 44 is $species->binomial, 'Homo sapiens'; 64 is $species->binomial, 'Brassica rapa subsp.'; 65 is $species->binomial('FULL'), 'Brassica rapa subsp. pekinensis';
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