lp_solve <http://lpsolve.sourceforge.net/> [aut],
Kjell Konis [aut],
Florian Schwendinger [aut, cre]

lp_solve <http://lpsolve.sourceforge.net/> [aut],
Kjell Konis [aut],
Florian Schwendinger [aut, cre],
Kurt Hornik [ctb]

lp_solve <https://lpsolve.sourceforge.net/> [aut],
Kjell Konis [aut],
Florian Schwendinger [aut, cre],
Kurt Hornik [ctb]

The lpSolveAPI package provides an R interface for
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.

lp_solve <http://lpsolve.sourceforge.net/>, Kjell Konis
<kjell.konis@me.com>.

Timothee Poisot <tim@poisotlab.io>, Daniel B Stouffer <daniel.stouffer@canterbury.ac.nz>
Optimization of bipartite modularity using LP-BRIM (Label propagation
followed by Bipartite Recursively Induced Modularity).

Lassoed principal components for testing significance of
features
Implements the LPC method of Witten&Tibshirani(2008) for
identification of significant genes in a microarray experiment.

Lassoed Principal Components for Testing Significance of
Features
Implements the LPC method of Witten&Tibshirani(Annals of Applied Statistics 2008) for identification of significant genes in a microarray experiment.

Lassoed principal components for testing significance of features
Implements the LPC method of Witten&Tibshirani(2008) for identification of significant genes in a microarray experiment.

Boundary Adaptive Local Polynomial Conditional Density Estimator
person(given = "Rajita",
family = "Chandak",
role = c("aut", "cre"),
email = "rchandak@princeton.edu" )
Tools for estimation and inference of conditional densities, derivatives and functions. This is the companion software for Cattaneo, Chandak, Jansson and Ma (2022).
Rcpp (>= 0.12.8), ggplot2, purrr, MASS, mvtnorm, stats

Rcpp (>= 0.12.8), ggplot2, purrr, MASS, mvtnorm, combinat,
Matrix, stats

Local Polynomial Density Estimation and Inference
Matias D. Cattaneo, Michael Jansson, Xinwei Ma
Without imposing stringent distributional assumptions or shape restrictions, nonparametric density estimation has been popular in economics and other social sciences for counterfactual analysis, program evaluation, and policy recommendations. This package implements a novel density estimator based on local polynomial regression, documented in Cattaneo, Jansson and Ma (2017): lpdensity() to construct local polynomial based density (and derivatives) estimator; lpbwdensity() to perform data-driven bandwidth selection; and lpdensity.plot() for density plot with robust confidence interval.