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% Please edit documentation in R/lassoinf.R
\name{LassoInference}
\alias{LassoInference}
\title{LassoInference Class}
\description{
Computes post-selection inference for the lasso.
This class mirrors the Python `lassoinf.LassoInference` dataclass, 
providing identical parameters and functional parity.
}
\section{Public fields}{
\if{html}{\out{<div class="r6-fields">}}
\describe{
\item{\code{beta_hat}}{The lasso solution from the randomized objective.}

\item{\code{G_hat}}{The gradient at beta_hat of the unpenalized loss.}

\item{\code{Q_hat}}{The Hessian / design matrix crossprod.}

\item{\code{D}}{The penalty weights.}

\item{\code{L}}{Lower bounds.}

\item{\code{U}}{Upper bounds.}

\item{\code{Z_full}}{The unpenalized score on the full data.}

\item{\code{Sigma}}{Covariance matrix of Z_full.}

\item{\code{Sigma_noise}}{Covariance matrix of the randomized score Z_noisy.}

\item{\code{scalar_noise}}{Scalar controlling variance of noise if Sigma_noise is not provided.}

\item{\code{A}}{Affine constraint matrix (from C++ backend).}

\item{\code{b}}{Affine constraint vector (from C++ backend).}

\item{\code{E}}{The active set (selected indices).}

\item{\code{E_c}}{The inactive set.}

\item{\code{s_E}}{Signs of the active set coefficients.}

\item{\code{v_Ec}}{Values at the bounds for the inactive set.}

\item{\code{si}}{AffineConstraints C++ wrapper instance.}

\item{\code{intervals}}{Selective inference intervals.}

\item{\code{splitting}}{Data splitting intervals.}

\item{\code{naive}}{Naive intervals.}

\item{\code{contrasts}}{The contrast vectors for inference.}

\item{\code{_naive}}{Data frame storing naive results.}

\item{\code{_splitting}}{Data frame storing data splitting results.}
}
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}
\section{Methods}{
\subsection{Public methods}{
\itemize{
\item \href{#method-LassoInference-new}{\code{LassoInference$new()}}
\item \href{#method-LassoInference-summary}{\code{LassoInference$summary()}}
\item \href{#method-LassoInference-clone}{\code{LassoInference$clone()}}
}
}
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\if{html}{\out{<a id="method-LassoInference-new"></a>}}
\if{latex}{\out{\hypertarget{method-LassoInference-new}{}}}
\subsection{Method \code{new()}}{
Initialize the LassoInference object.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{LassoInference$new(
  beta_hat,
  G_hat,
  Q_hat,
  D,
  L = NULL,
  U = NULL,
  Z_full,
  Sigma,
  Sigma_noise = NULL,
  scalar_noise = NaN,
  tol = 1e-06
)}\if{html}{\out{</div>}}
}

\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{beta_hat}}{The lasso solution.}

\item{\code{G_hat}}{The gradient at beta_hat.}

\item{\code{Q_hat}}{The Hessian / design matrix crossprod.}

\item{\code{D}}{The penalty weights.}

\item{\code{L}}{Lower bounds for beta. Default is \code{NULL} (equivalent to \code{-Inf}).}

\item{\code{U}}{Upper bounds for beta. Default is \code{NULL} (equivalent to \code{Inf}).}

\item{\code{Z_full}}{The unpenalized score.}

\item{\code{Sigma}}{Covariance of Z_full.}

\item{\code{Sigma_noise}}{Covariance of Z_noisy. Default is \code{NULL}.}

\item{\code{scalar_noise}}{Variance scaling if Sigma_noise is NULL. Default is \code{NaN}.}

\item{\code{tol}}{Tolerance for active set and KKT conditions. Default is \code{1e-6}.}
}
\if{html}{\out{</div>}}
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-LassoInference-summary"></a>}}
\if{latex}{\out{\hypertarget{method-LassoInference-summary}{}}}
\subsection{Method \code{summary()}}{
Summary of post-selection inference results.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{LassoInference$summary()}\if{html}{\out{</div>}}
}

\subsection{Returns}{
A data frame containing the parameter indices, estimates, selective confidence intervals, and selective p-values.
}
}
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\if{html}{\out{<a id="method-LassoInference-clone"></a>}}
\if{latex}{\out{\hypertarget{method-LassoInference-clone}{}}}
\subsection{Method \code{clone()}}{
The objects of this class are cloneable with this method.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{LassoInference$clone(deep = FALSE)}\if{html}{\out{</div>}}
}

\subsection{Arguments}{
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\describe{
\item{\code{deep}}{Whether to make a deep clone.}
}
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}
}
}
