= Package QltvRespModel =

Max-likelihood and bayesian estimation of qualitative response models.

== Weighted Boolean Regresions ==

Abstract class 
[source:/tolp/OfficialTolArchiveNetwork/QltvRespModel/WgtBoolReg.tol @WgtBoolReg] 
is the base to inherit weighted boolean regressions as logit or probit or any other 
given justthe scalar distribution function.

This class implements max-likelihood by means of package 
[wiki/OfficialTolArchiveNetworkNonLinGloOpt NonLinGloOpt] and bayesian estimation 
using [wiki/OfficialTolArchiveNetworkBysSampler BysSampler].

User can and should define scalar truncated normal or uniform prior information and 
bounds for all variables for which he/she has robust knowledge.[[BR]] [[BR]]
[[LatexEquation( \beta_k \sim N\left(\nu_k, \sigma_k \right) )]] [[BR]] [[BR]]
[[LatexEquation( l_k \le \beta_k \le u_k \wedge l_k < u_k)]] [[BR]] [[BR]]
When [[LatexEquation( \sigma_k )]] is infinite or unknown we will express a uniform
prior.
When [[LatexEquation( l_k = -\infty)]] or unknown we will express that variable 
has no lower bound.
When [[LatexEquation( u_k = +\infty)]] or unknown we will express that variable
has no upper bound.

It's also allowed to give any set of constraining linear inequations [[BR]] [[BR]]
[[LatexEquation( A \beta \le a )]] [[BR]] [[BR]]

=== Weighted Logit Regression ===
Class [source:/tolp/OfficialTolArchiveNetwork/QltvRespModel/WgtLogit.tol @WgtLogit] 
is an specialization of class 
[source:/tolp/OfficialTolArchiveNetwork/QltvRespModel/WgtBoolReg.tol @WgtBoolReg]
that handles with weighted logit regressions.


=== Weighted Probit Regression ===
Class [source:/tolp/OfficialTolArchiveNetwork/QltvRespModel/WgtProbit.tol @WgtProbit] 
is an specialization of class 
[source:/tolp/OfficialTolArchiveNetwork/QltvRespModel/WgtBoolReg.tol @WgtBoolReg]
that handles with weighted probit regressions.