Implements methods for analyzing latent variable models with measurement
    error correction, including Item Response Theory (IRT) models. Provides tools for
    various correction methods such as Bayesian Markov Chain Monte Carlo (MCMC),
    over-imputation, bootstrapping for robust standard errors, Ordinary Least Squares
    (OLS), and Instrumental Variables (IV) based approaches. Supports flexible
    specification of observable indicators and groupings for latent variable analyses
    in social sciences and other fields. Methods are described in a working paper
    (2025) <doi:10.48550/arXiv.2507.22218>.

Creates a consensus genetic map by merging linkage maps from different populations.  The software uses linear programming (LP) to efficiently minimize the mean absolute error between the consensus map and the linkage maps.  This minimization is performed subject to linear inequality constraints that ensure the ordering of the markers in the linkage maps is preserved.  When marker order is inconsistent between linkage maps, a minimum set of ordinal constraints is deleted to resolve the conflicts.

Local Polynomial Regression with Ridging.

Lp_solve is freely available (under LGPL 2) software for
        solving linear, integer and mixed integer programs. In this
        implementation we supply a "wrapper" function in C and some R
        functions that solve general linear/integer problems,
        assignment problems, and transportation problems. This version
        calls lp_solve version 5.5.

The lpSolveAPI package provides an R interface to 'lp_solve',
    a Mixed Integer Linear Programming (MILP) solver with support for pure
    linear, (mixed) integer/binary, semi-continuous and special ordered sets
    (SOS) models.

Learned Pattern Similarity (LPS) for time series, as described
    in Baydogan and Runger (2016) <doi:10.1007/s10618-015-0425-y>.
    Implements an approach to model the dependency structure in time
    series that generalizes the concept of autoregression to local
    auto-patterns. Generates a pattern-based representation of time
    series along with a similarity measure called Learned Pattern
    Similarity (LPS). Introduces a generalized autoregressive kernel.
    This package adapts C code from the 'randomForest' package by
    Andy Liaw and Matthew Wiener, itself based on original Fortran
    code by Leo Breiman and Adele Cutler.