Authors@R: person("Malcolm","Haddon", email = "malcolm.haddon@gmail.com",
published in September 2020 by Chapman & Hall in their "Using R series".

Maintainer: Adam J. Rothman <arothman@umn.edu>
Description: Multivariate regression with covariance estimation

Title: Multivariate regression with covariance estimation

Title: Multivariate Regression with Covariance Estimation
Description: 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.

Packaged: 2022-01-04 15:43:00 UTC; adamrothman

Title: Methods for Analyzing Multiple Response Categorical Variables
Description: 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

Description: 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.

Title: Fitting and Predicting Large-Scale Nonlinear Regression Problems
using Multi-Resolution Functional ANOVA (MRFA) Approach
Description: 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 (unpublished) to fit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.
Imports: fields, glmnet, grplasso, methods, plyr, randtoolbox, foreach,