Files
file	baranov.cpp
	Routines for iteratively solving the Baranov catch equation.
file	dbeta.cpp
	Beta density functions.
file	dbinom.cpp
	Binomial density functions.
file	dgamma.cpp
	Gamma density functions.
file	dinvgamma.cpp
	Inverse gamma distribution.
file	dlnorm.cpp
	Lognormal density functions.
file	dmultinom.cpp
	Multinomial distribution.
file	dmvlogistic.cpp
	Multivariate logistic negative log likelihood.
file	dnorm.cpp
	Normal density functions.
file	dpois.cpp
	Poisson density functions.
file	dunif.cpp
	Uniform distribution.
file	eplogis.cpp
	// Exponential logistic
file	fill.cpp
	Fills a matrix with a vector This function fills a matrix m with a vector v.
file	multifan.cpp
	/Robust normal approximation to the multinomial distribution
file	pearsonresiduals.cpp
	// Pearson residuals
file	rmvlogistic.cpp
	Random multivariate logistic negative log likelihood.
file	statsLib.h
	Library of statistic functions.
file	studentT.cpp
	Student T density functions.
file	vcubicspline.cpp
	Cubic spline functions.
Functions
dvar_matrix	ageLengthKey (const dvar_vector &mu, const dvar_vector &sig, const dvector &x)
	Age Length Key.
dvariable	dnbinom (const double &x, const prevariable &mu, const prevariable &k)
	negative log likelihood of negative binomial with mean=mu and variance = mu + mu^2 /k
df1b2variable	dnbinom (const double &x, const df1b2variable &mu, const df1b2variable &k)
	negative log likelihood of negative binomial with mean=mu and variance = mu + mu^2 /k
df1b2variable	dnbinom (const dvector &x, const df1b2vector &mu, const df1b2variable &k)
	negative log likelihood of negative binomial with mean=mu and variance = mu + mu^2 /k
df1b2variable	dnbinom (const dvector &x, const df1b2vector &mu, const df1b2vector &k)
	negative log likelihood of negative binomial with mean=mu and variance = mu + mu^2 /k
dvariable	dnbinom (const dvector &x, const dvar_vector &mu, const prevariable &k)
	negative log likelihood of negative binomial with mean=mu and variance = mu + mu^2 /k
dvariable	dnbinom (const dvector &x, const dvar_vector &mu, const dvar_vector &k)
	negative log likelihood of negative binomial with mean=mu and variance = mu + mu^2 /k
dvariable	dnbinom_tau (const double &x, const prevariable &mu, const prevariable &tau)
	negative log likelihood of negative binomial with mean and tau
df1b2variable	dnbinom_tau (const double &x, const df1b2variable &mu, const df1b2variable &tau)
	negative log likelihood of negative binomial with mean and tau
df1b2variable	dnbinom_tau (const dvector &x, const df1b2vector &mu, const df1b2variable &tau)
	negative log likelihood of negative binomial with mean and tau
df1b2variable	dnbinom_tau (const dvector &x, const df1b2vector &mu, const df1b2vector &tau)
	negative log likelihood of negative binomial with mean and tau
dvariable	dnbinom_tau (const dvector &x, const dvar_vector &mu, const prevariable &tau)
	negative log likelihood of negative binomial with mean and tau
dvariable	dnbinom_tau (const dvector &x, const dvar_vector &mu, const dvar_vector &tau)
	negative log likelihood of negative binomial with mean and tau
df1b2variable	dzinbinom (const double &x, const df1b2variable &mu, const df1b2variable &k, const df1b2variable &p)
	ecologically parametarized negative binomial with zero inflation
dvariable	dzinbinom (const double &x, const prevariable &mu, const prevariable &k, const prevariable &p)
	ecologically parametarized negative binomial with zero inflation
df1b2variable	dzinbinom (const dvector &x, const df1b2vector &mu, const df1b2variable &k, const df1b2variable &p)
	ecologically parametarized negative binomial with zero inflation
df1b2variable	dzinbinom (const dvector &x, const df1b2vector &mu, const df1b2vector &k, const df1b2variable &p)
	ecologically parametarized negative binomial with zero inflation
dvariable	dzinbinom (const dvector &x, const dvar_vector &mu, const prevariable &k, const prevariable &p)
	ecologically parametarized negative binomial with zero inflation
dvariable	dzinbinom (const dvector &x, const dvar_vector &mu, const dvar_vector &k, const prevariable &p)
	ecologically parametarized negative binomial with zero inflation
df1b2variable	dzinbinom (const dvector &x, const df1b2vector &mu, const df1b2variable &k, const df1b2vector &p)
	now p is a vector///
df1b2variable	dzinbinom (const dvector &x, const df1b2vector &mu, const df1b2vector &k, const df1b2vector &p)
	ecologically parametarized negative binomial with zero inflation
dvariable	dzinbinom (const dvector &x, const dvar_vector &mu, const prevariable &k, const dvar_vector &p)
	ecologically parametarized negative binomial with zero inflation
dvariable	dzinbinom (const dvector &x, const dvar_vector &mu, const dvar_vector &k, const dvar_vector &p)
	ecologically parametarized negative binomial with zero inflation

Detailed Description

Contributed by Steven Martell.

Function Documentation

dvar_matrix ageLengthKey	(	const dvar_vector &	mu,
		const dvar_vector &	sig,
		const dvector &	x
	)

Age Length Key.

Author:: Steven James Dean Martell UBC Fisheries Centre

Date:: 2011-06-28

Parameters:

mu	is the mean length-at-age
sig	is the std in mean length-at-age
x	is the vector of break points for the length bins

Returns:: dvar_matrix containing the probability of length(x) for a given age(a)

See also:

Definition at line 41 of file alk.cpp.

dvariable dnbinom	(	const double &	x,
		const prevariable &	mu,
		const prevariable &	k
	)

negative log likelihood of negative binomial with mean=mu and variance = mu + mu^2 /k

Negative binomial with mean and quadratic variance

Author:: Steven Martell and Mollie Brooks

Parameters:

x	observed count
mu	is the predicted mean
k	is the overdispersion parameter, i.e. shape parameter of underlying gamma heterogeneity (different from tau). should be >0

Returns:: negative log likelihood $-( \ln(\Gamma(x+k))-\ln(\Gamma(k))-\ln(x!)+k\ln(k)+x\ln(\mu)-(k+x)\ln(k+\mu) )$

Definition at line 12 of file dnbinom.cpp.

df1b2variable dnbinom	(	const double &	x,
		const df1b2variable &	mu,
		const df1b2variable &	k
	)

negative log likelihood of negative binomial with mean=mu and variance = mu + mu^2 /k

Negative binomial with mean and quadratic variance

Author:: Mollie Brooks

Parameters:

x	observed count
mu	is the predicted mean
k	is the overdispersion parameter, i.e. shape parameter of underlying gamma heterogeneity (different from tau). should be >0

Returns:: negative log likelihood $-( \ln(\Gamma(x+k))-\ln(\Gamma(k))-\ln(x!)+k\ln(k)+x\ln(\mu)-(k+x)\ln(k+\mu) )$

Definition at line 40 of file dnbinom.cpp.

df1b2variable dnbinom	(	const dvector &	x,
		const df1b2vector &	mu,
		const df1b2variable &	k
	)

negative log likelihood of negative binomial with mean=mu and variance = mu + mu^2 /k

Negative binomial with mean and quadratic variance

Author:: Mollie Brooks

Parameters:

x	observed counts
mu	is the predicted mean
k	is the overdispersion parameter, i.e. shape parameter of underlying gamma heterogeneity (different from tau). should be >0

Returns:: negative log likelihood $-( \ln(\Gamma(x+k))-\ln(\Gamma(k))-\ln(x!)+k\ln(k)+x\ln(\mu)-(k+x)\ln(k+\mu) )$

Definition at line 67 of file dnbinom.cpp.

df1b2variable dnbinom	(	const dvector &	x,
		const df1b2vector &	mu,
		const df1b2vector &	k
	)

negative log likelihood of negative binomial with mean=mu and variance = mu + mu^2 /k

Negative binomial with mean and quadratic variance

Author:: Mollie Brooks

Parameters:

x	observed counts
mu	is the predicted mean
k	is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying gamma heterogeneity (different from tau). should be >0

Returns:: negative log likelihood $-( \ln(\Gamma(x+k))-\ln(\Gamma(k))-\ln(x!)+k\ln(k)+x\ln(\mu)-(k+x)\ln(k+\mu) )$

Definition at line 99 of file dnbinom.cpp.

dvariable dnbinom	(	const dvector &	x,
		const dvar_vector &	mu,
		const prevariable &	k
	)

negative log likelihood of negative binomial with mean=mu and variance = mu + mu^2 /k

Negative binomial with mean and quadratic variance

Author:: Mollie Brooks

Parameters:

x	observed counts
mu	is the predicted mean
k	is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying gamma heterogeneity (different from tau). should be >0

Returns:: negative log likelihood $-( \ln(\Gamma(x+k))-\ln(\Gamma(k))-\ln(x!)+k\ln(k)+x\ln(\mu)-(k+x)\ln(k+\mu) )$

Definition at line 134 of file dnbinom.cpp.

dvariable dnbinom	(	const dvector &	x,
		const dvar_vector &	mu,
		const dvar_vector &	k
	)

negative log likelihood of negative binomial with mean=mu and variance = mu + mu^2 /k

Negative binomial with mean and quadratic variance

Author:: Mollie Brooks

Parameters:

x	observed counts
mu	is the predicted mean
k	is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying gamma heterogeneity (different from tau). should be >0

Returns:: negative log likelihood $-( \ln(\Gamma(x+k))-\ln(\Gamma(k))-\ln(x!)+k\ln(k)+x\ln(\mu)-(k+x)\ln(k+\mu) )$

Definition at line 166 of file dnbinom.cpp.

dvariable dnbinom_tau	(	const double &	x,
		const prevariable &	mu,
		const prevariable &	tau
	)

negative log likelihood of negative binomial with mean and tau

Negative binomial with mean=mu and variance = mu*tau

Author:: Mollie Brooks

Parameters:

x	observed count
mu	is the predicted mean
tau	is the overdispersion parameter like in the quasi-poisson. should be >1

Returns:: negative log likelihood $-( \ln(\Gamma(x+k))-\ln(\Gamma(k))-\ln(x!)+k\ln(k)+x\ln(\mu)-(k+x)\ln(k+\mu) )$ where $k=\mu/(10^{-120}+\tau-1.0)$

Definition at line 16 of file dnbinom_tau.cpp.

df1b2variable dnbinom_tau	(	const double &	x,
		const df1b2variable &	mu,
		const df1b2variable &	tau
	)

negative log likelihood of negative binomial with mean and tau

Negative binomial with mean=mu and variance = mu*tau

Author:: Mollie Brooks

Parameters:

x	observed count
mu	is the predicted mean
tau	is the overdispersion parameter like in the quasi-poisson. should be >1

Returns:: negative log likelihood $-( \ln(\Gamma(x+k))-\ln(\Gamma(k))-\ln(x!)+k\ln(k)+x\ln(\mu)-(k+x)\ln(k+\mu) )$ where $k=\mu/(10^{-120}+\tau-1.0)$

Definition at line 45 of file dnbinom_tau.cpp.

df1b2variable dnbinom_tau	(	const dvector &	x,
		const df1b2vector &	mu,
		const df1b2variable &	tau
	)

negative log likelihood of negative binomial with mean and tau

Negative binomial with mean=mu and variance = mu*tau

Author:: Mollie Brooks

Parameters:

x	observed counts
mu	is the predicted mean
tau	is the overdispersion parameter like in the quasi-poisson. should be >1

Returns:: negative log likelihood $-( \ln(\Gamma(x+k))-\ln(\Gamma(k))-\ln(x!)+k\ln(k)+x\ln(\mu)-(k+x)\ln(k+\mu) )$ where $k=\mu/(10^{-120}+\tau-1.0)$

Definition at line 73 of file dnbinom_tau.cpp.

df1b2variable dnbinom_tau	(	const dvector &	x,
		const df1b2vector &	mu,
		const df1b2vector &	tau
	)

negative log likelihood of negative binomial with mean and tau

Negative binomial with mean=mu and variance = mu*tau

Author:: Mollie Brooks

Parameters:

x	observed counts
mu	is the predicted mean
tau	is the overdispersion parameter like in the quasi-poisson. should be >1

Returns:: negative log likelihood $-( \ln(\Gamma(x+k))-\ln(\Gamma(k))-\ln(x!)+k\ln(k)+x\ln(\mu)-(k+x)\ln(k+\mu) )$ where $k=\mu/(10^{-120}+\tau-1.0)$

Definition at line 106 of file dnbinom_tau.cpp.

dvariable dnbinom_tau	(	const dvector &	x,
		const dvar_vector &	mu,
		const prevariable &	tau
	)

negative log likelihood of negative binomial with mean and tau

Negative binomial with mean=mu and variance = mu*tau

Author:: Mollie Brooks

Parameters:

x	observed counts
mu	is the predicted mean
tau	is the overdispersion parameter like in the quasi-poisson. should be >1

Returns:: negative log likelihood $-( \ln(\Gamma(x+k))-\ln(\Gamma(k))-\ln(x!)+k\ln(k)+x\ln(\mu)-(k+x)\ln(k+\mu) )$ where $k=\mu/(10^{-120}+\tau-1.0)$

Definition at line 142 of file dnbinom_tau.cpp.

dvariable dnbinom_tau	(	const dvector &	x,
		const dvar_vector &	mu,
		const dvar_vector &	tau
	)

negative log likelihood of negative binomial with mean and tau

Negative binomial with mean=mu and variance = mu*tau

Author:: Mollie Brooks

Parameters:

x	observed counts
mu	is the predicted mean
tau	is the overdispersion parameter like in the quasi-poisson. should be >1

Returns:: negative log likelihood $-( \ln(\Gamma(x+k))-\ln(\Gamma(k))-\ln(x!)+k\ln(k)+x\ln(\mu)-(k+x)\ln(k+\mu) )$ where $k=\mu/(10^{-120}+\tau-1.0)$

Definition at line 175 of file dnbinom_tau.cpp.

df1b2variable dzinbinom	(	const double &	x,
		const df1b2variable &	mu,
		const df1b2variable &	k,
		const df1b2variable &	p
	)

ecologically parametarized negative binomial with zero inflation

Zero Inflated Negative binomial with size and mean

Author:: Mollie Brooks

Parameters:

x	observed count. should be greater than or equal to 0.
mu	is the mean of the negative binomial part
k	is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying heterogeneity (different from tau). should be >0
p	is the zero inflation paramerer, i.e. extra chance of observing zeros. 0<p<1.

Returns:: negative log-likelihood

Definition at line 20 of file dzinbinom.cpp.

dvariable dzinbinom	(	const double &	x,
		const prevariable &	mu,
		const prevariable &	k,
		const prevariable &	p
	)

ecologically parametarized negative binomial with zero inflation

Zero Inflated Negative binomial with size and mean

Author:: Mollie Brooks

Parameters:

x	observed count. should be greater than or equal to 0.
mu	is the mean of the negative binomial part
k	is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying heterogeneity (different from tau). should be >0
p	is the zero inflation paramerer, i.e. extra chance of observing zeros. 0<p<1.

Returns:: negative log-likelihood

Definition at line 57 of file dzinbinom.cpp.

df1b2variable dzinbinom	(	const dvector &	x,
		const df1b2vector &	mu,
		const df1b2variable &	k,
		const df1b2variable &	p
	)

ecologically parametarized negative binomial with zero inflation

Zero Inflated Negative binomial with size and mean

Author:: Mollie Brooks

Parameters:

x	observed counts. should be greater than or equal to 0.
mu	is the mean of the negative binomial part
k	is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying heterogeneity (different from tau). should be >0
p	is the zero inflation paramerer, i.e. extra chance of observing zeros. 0<p<1.

Returns:: negative log-likelihood

Definition at line 92 of file dzinbinom.cpp.

df1b2variable dzinbinom	(	const dvector &	x,
		const df1b2vector &	mu,
		const df1b2vector &	k,
		const df1b2variable &	p
	)

ecologically parametarized negative binomial with zero inflation

Zero Inflated Negative binomial with size and mean

Author:: Mollie Brooks

Parameters:

x	observed counts
mu	is the mean of the negative binomial part
k	is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying heterogeneity (different from tau). should be >0
p	is the zero inflation paramerer, i.e. extra chance of observing zeros. 0<p<1.

Returns:: negative log-likelihood

Definition at line 133 of file dzinbinom.cpp.

dvariable dzinbinom	(	const dvector &	x,
		const dvar_vector &	mu,
		const prevariable &	k,
		const prevariable &	p
	)

ecologically parametarized negative binomial with zero inflation

Zero Inflated Negative binomial with size and mean

Author:: Mollie Brooks

Parameters:

x	observed counts
mu	is the mean of the negative binomial part
k	is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying heterogeneity (different from tau). should be >0
p	is the zero inflation paramerer, i.e. extra chance of observing zeros. 0<p<1.

Returns:: negative log-likelihood

Definition at line 175 of file dzinbinom.cpp.

dvariable dzinbinom	(	const dvector &	x,
		const dvar_vector &	mu,
		const dvar_vector &	k,
		const prevariable &	p
	)

ecologically parametarized negative binomial with zero inflation

Zero Inflated Negative binomial with size and mean

Author:: Mollie Brooks

Parameters:

x	observed counts
mu	is the mean of the negative binomial part
k	is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying heterogeneity (different from tau). should be >0
p	is the zero inflation paramerer, i.e. extra chance of observing zeros. 0<p<1.

Returns:: negative log-likelihood

Definition at line 216 of file dzinbinom.cpp.

df1b2variable dzinbinom	(	const dvector &	x,
		const df1b2vector &	mu,
		const df1b2variable &	k,
		const df1b2vector &	p
	)

now p is a vector///

ecologically parametarized negative binomial with zero inflation Zero Inflated Negative binomial with size and mean

Author:: Mollie Brooks

Parameters:

x	observed counts
mu	is the mean of the negative binomial part
k	is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying heterogeneity (different from tau). should be >0
p	is the zero inflation paramerer, i.e. extra chance of observing zeros. 0<p<1.

Returns:: negative log-likelihood

Definition at line 261 of file dzinbinom.cpp.

df1b2variable dzinbinom	(	const dvector &	x,
		const df1b2vector &	mu,
		const df1b2vector &	k,
		const df1b2vector &	p
	)

ecologically parametarized negative binomial with zero inflation

Zero Inflated Negative binomial with size and mean

Author:: Mollie Brooks

Parameters:

x	observed counts
mu	is the mean of the negative binomial part
k	is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying heterogeneity (different from tau). should be >0
p	is the zero inflation paramerer, i.e. extra chance of observing zeros. 0<p<1.

Returns:: negative log-likelihood

Definition at line 302 of file dzinbinom.cpp.

dvariable dzinbinom	(	const dvector &	x,
		const dvar_vector &	mu,
		const prevariable &	k,
		const dvar_vector &	p
	)

ecologically parametarized negative binomial with zero inflation

Zero Inflated Negative binomial with size and mean

Author:: Mollie Brooks

Parameters:

x	observed counts
mu	is the mean of the negative binomial part
k	is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying heterogeneity (different from tau). should be >0
p	is the zero inflation paramerer, i.e. extra chance of observing zeros. 0<p<1.

Returns:: negative log-likelihood

Definition at line 344 of file dzinbinom.cpp.

dvariable dzinbinom	(	const dvector &	x,
		const dvar_vector &	mu,
		const dvar_vector &	k,
		const dvar_vector &	p
	)

ecologically parametarized negative binomial with zero inflation

Zero Inflated Negative binomial with size and mean

Author:: Mollie Brooks

Parameters:

x	observed counts
mu	is the mean of the negative binomial part
k	is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying heterogeneity (different from tau). should be >0
p	is the zero inflation paramerer, i.e. extra chance of observing zeros. 0<p<1.

Returns:: negative log-likelihood

Definition at line 385 of file dzinbinom.cpp.

Files

Functions

Detailed Description

Function Documentation