A method for obtaining nonparametric estimates of regression models
    with or without factor-by-curve interactions using local polynomial kernel
    smoothers or splines. Additionally, a parametric model (allometric model) can be
    estimated.

Nonparametric (and semiparametric) kernel methods that seamlessly
    handle a mix of continuous, unordered, and ordered factor data types. This
    package is a parallel implementation of the 'np' package based on the 'MPI'
    specification that incorporates the 'Rmpi' package (Hao Yu
    <hyu@stats.uwo.ca>) with minor modifications and we are extremely grateful
    to Hao Yu for his contributions to the 'R' community. We would like to
    gratefully acknowledge support from the Natural Sciences and Engineering
    Research Council of Canada (NSERC, <https://www.nserc-crsng.gc.ca/>), the
    Social Sciences and Humanities Research Council of Canada (SSHRC,
    <https://www.sshrc-crsh.gc.ca/>), and the Shared Hierarchical Academic
    Research Computing Network (SHARCNET, <https://sharcnet.ca/>). We would
    also like to acknowledge the contributions of the 'GNU GSL' authors. In
    particular, we adapt the 'GNU GSL' B-spline routine 'gsl_bspline.c' adding
    automated support for quantile knots (in addition to uniform knots),
    providing missing functionality for derivatives, and for extending the
    splines beyond their endpoints.

Estimation, inference, bandwidth selection, and graphical procedures for kernel density and local polynomial regression methods, including robust bias-corrected confidence intervals as described in Calonico, Cattaneo and Farrell (2018, <doi:10.1080/01621459.2017.1285776>). The package includes 'lprobust()' for local polynomial point estimation and robust bias-corrected inference, 'lpbwselect()' for local polynomial bandwidth selection, 'kdrobust()' for kernel density point estimation and robust bias-corrected inference, 'kdbwselect()' for kernel density bandwidth selection, and 'nprobust.plot()' for plotting results. The main methodological and numerical features are described in Calonico, Cattaneo and Farrell (2019, <doi:10.18637/jss.v091.i08>).