Revision 593 trunk/contrib/statslib/dzinbinom.cpp
dzinbinom.cpp (revision 593)  

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\param k is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying heterogeneity (different from tau). should be >0 
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\param p is the zero inflation paramerer, i.e. extra chance of observing zeros. 0<p<1. 
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\return negative log likelihood 
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\ingroup STATLIB 

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**/ 
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df1b2variable dzinbinom(const double& x, const df1b2variable& mu, const df1b2variable& k, const df1b2variable& p) 
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\param k is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying heterogeneity (different from tau). should be >0 
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\param p is the zero inflation paramerer, i.e. extra chance of observing zeros. 0<p<1. 
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\return negative log likelihood 
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\ingroup STATLIB 

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**/ 
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dvariable dzinbinom(const double& x, const prevariable& mu, const prevariable& k, const prevariable& p) 
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\param k is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying heterogeneity (different from tau). should be >0 
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\param p is the zero inflation paramerer, i.e. extra chance of observing zeros. 0<p<1. 
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\return negative log likelihood 
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\ingroup STATLIB 

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**/ 
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df1b2variable dzinbinom(const dvector& x, const df1b2vector& mu, const df1b2variable& k, const df1b2variable& p) 
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\param k is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying heterogeneity (different from tau). should be >0 
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\param p is the zero inflation paramerer, i.e. extra chance of observing zeros. 0<p<1. 
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\return negative log likelihood 
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\ingroup STATLIB 

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**/ 
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df1b2variable dzinbinom(const dvector& x, const df1b2vector& mu, const df1b2vector& k, const df1b2variable& p) 
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{ 
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\param k is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying heterogeneity (different from tau). should be >0 
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\param p is the zero inflation paramerer, i.e. extra chance of observing zeros. 0<p<1. 
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\return negative log likelihood 
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\ingroup STATLIB 

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**/ 
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dvariable dzinbinom(const dvector& x, const dvar_vector& mu, const prevariable& k, const prevariable& p) 
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{ 
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\param k is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying heterogeneity (different from tau). should be >0 
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\param p is the zero inflation paramerer, i.e. extra chance of observing zeros. 0<p<1. 
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\return negative log likelihood 
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\ingroup STATLIB 

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**/ 
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dvariable dzinbinom(const dvector& x, const dvar_vector& mu, const dvar_vector& k, const prevariable& p) 
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{ 
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\param k is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying heterogeneity (different from tau). should be >0 
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\param p is the zero inflation paramerer, i.e. extra chance of observing zeros. 0<p<1. 
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\return negative log likelihood 
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\ingroup STATLIB 

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**/ 
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df1b2variable dzinbinom(const dvector& x, const df1b2vector& mu, const df1b2variable& k, const df1b2vector& p) 
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{ 
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\param k is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying heterogeneity (different from tau). should be >0 
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\param p is the zero inflation paramerer, i.e. extra chance of observing zeros. 0<p<1. 
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\return negative log likelihood 
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\ingroup STATLIB 

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**/ 
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df1b2variable dzinbinom(const dvector& x, const df1b2vector& mu, const df1b2vector& k, const df1b2vector& p) 
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{ 
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\param k is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying heterogeneity (different from tau). should be >0 
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\param p is the zero inflation paramerer, i.e. extra chance of observing zeros. 0<p<1. 
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\return negative log likelihood 
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\ingroup STATLIB 

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**/ 
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dvariable dzinbinom(const dvector& x, const dvar_vector& mu, const prevariable& k, const dvar_vector& p) 
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{ 
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\param k is the overdispersion parameter, i.e. size, i.e. shape parameter of underlying heterogeneity (different from tau). should be >0 
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\param p is the zero inflation paramerer, i.e. extra chance of observing zeros. 0<p<1. 
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\return negative log likelihood 
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\ingroup STATLIB 

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