person("Malcolm","Haddon", email = "malcolm.haddon@gmail.com",
role = c("aut", "cre"))
Complements the book "Using R for Modelling and
Quantitative Methods in Fisheries" ISBN: 9780367469894, being
published in September 2020 by Chapman & Hall in their "Using R series".
There are numerous functions and data-sets that are used in the
book's many practical examples.

Multivariate regression with covariance estimation

Multivariate Regression with Covariance Estimation
Compute and select tuning parameters for the MRCE estimator proposed by Rothman, Levina, and Zhu (2010) <doi:10.1198/jcgs.2010.09188>.  This estimator fits the multiple output linear regression model with a sparse estimator of the error precision matrix and a sparse estimator of the regression coefficient matrix.

Methods for Analyzing Multiple Response Categorical Variables
(MRCVs)
This package provides functions for analyzing the
association between two or three MRCVs.  A modified Pearson
chi-square statistic can be used to test for marginal
independence, or a more general loglinear modeling approach can
be used to compare various other structures of association.
Bootstrap- and asymptotic-based standardized residuals and
model-predicted odds ratios are available, in addition to other
descriptive information.

The MRCV package provides functions for analyzing the association between
one single response categorical variable (SRCV) and one multiple response
categorical variable (MRCV), or between two or three MRCVs.  A modified Pearson
chi-square statistic can be used to test for marginal independence for the one or
two MRCV case, or a more general loglinear modeling approach can be used to examine
various other structures of association for the two or three MRCV case.  Bootstrap-
and asymptotic-based standardized residuals and model-predicted odds ratios are
available, in addition to other descriptive information.

Fitting and Predicting Large-Scale Nonlinear Regression Problems
using Multi-Resolution Functional ANOVA (MRFA) Approach
Performs the MRFA approach proposed by Sung et al. (unpublished) to fit
and predict nonlinear regression problems, particularly for large-scale and
high-dimensional problems. The application includes deterministic or stochastic
computer experiments, spatial datasets, and so on.
fields, glmnet, grplasso, methods, plyr, randtoolbox, foreach,
stats, graphics, utils