In addition to model fitting, 'missingHE' provides a set of specialised functions to assess model convergence and fit, and to summarise the statistical and economic results using different types of measures and graphs.
bayesplot, methods, R2jags, loo, coda, mcmcr

Imports: mcmcplots, ggpubr, ggmcmc, ggthemes, BCEA, ggplot2, grid,
gridExtra, bayesplot, methods, R2jags, loo, coda, mcmcr

Title: Missingness in Multi-Task Regression with Network Estimation
person("Yixiao", "Zeng", email = "yixiao.zeng@mail.mcgill.ca", role = c("aut", "cre", "cph")),
person("Celia", "Greenwood", email = "celia.greenwood@mcgill.ca", role = c("ths", "aut")),
person("Archer", "Yang", email = "archer.yang@mcgill.ca", role = c("ths", "aut")))
Description: Efficient procedures for fitting the conditional graphical
lasso models linking a set of predictor variables to a set of response
variables (or tasks), when the response data may contain missing
values. 'missoNet' simultaneously estimates the predictor coefficients
for all tasks by leveraging information from one another, in order to
provide more accurate predictions in comparison to modeling them
individually. Meanwhile, 'missoNet' is able to estimate the response
network structure influenced by conditioning predictor variables in a
L1-regularized conditional Gaussian graphical model. In contrast to
most penalized multi-task regression (conditional graphical lasso)
methods, 'missoNet' has the capability of obtaining estimates even
when the response data is corrupted by missing values. The method
automatically enjoys the theoretical and computational benefits of
convexity, and returns solutions that are comparable/close to the
estimates without any missing values. The package also includes
auxiliary functions for data simulation, goodness-of-fit evaluation,
regularization parameter tuning, and visualization of results, as well
Imports: circlize (>= 0.4.14), ComplexHeatmap, glasso (>= 1.11), glmnet

Title: Data sets, functions and examples from the book:"Modern
Industrial Statistics" by Kenett, Zacks and Amberti
Depends: R (>= 2.14), graphics, grDevices, methods, stats, utils
Suggests: e1071, mvtnorm, AcceptanceSampling, boot, car, Dodge,
tseries, qcc, DoE.base, FrF2, rsm, LearnBayes, ggplot2, grid,
DiceEval, DiceKriging, DiceDesign, DiceView, lhs, survival
Description: This R package is providing all the data sets and statistical analysis applications used in Modern Industrial Statistics, with applications using R, MINITAB and JMP by R.S. Kenett and S. Zacks with contributions by D. Amberti, John Wiley and Sons, 2013, which is a second revised and expanded revision of Modern Industrial Statistics: Design and Control of Quality and Reliability, R. Kenett and S. Zacks, Duxbury/Wadsworth Publishing, 1998.
Packaged: 2013-08-27 07:32:29 UTC; daniele.amberti