Title: Statistical methods for safety assessment in agricultural field trials
for evaluations of field trials with the objective of safety assessment.
Depends: R (>= 2.7.0), mvtnorm, multcomp, gamlss, mratios, MCPAN, pairwiseCI, lattice, geepack, boot
Packaged: Tue Nov  4 13:41:47 2008; manager

Title: Statistical methods for safety assessment in agricultural field
Description: Collection of functions, data sets and code examples for
evaluations of field trials with the objective of safety
Depends: R (>= 2.7.0), mvtnorm, multcomp, gamlss, mratios, MCPAN,

Packaged: 2013-11-21 10:22:19 UTC; Schaarschmidt

Title: Bayesian Simultaneous Quantile Regression
Description: This package models the quantile process as a function of
Packaged: 2013-05-23 15:53:32 UTC; smithinger

Packaged: 2013-05-24 05:32:31 UTC; smithinger

Title: Modelling Heterogeneity in Paired Comparison Data
Description: Performs 'BTLLasso', a method to model heterogeneity in paired comparison data. Subject-specific covariates are allowed to have an influence on the attractivity/strength of the objects. An L1 penalty on the pairwise differences between the object-specific parameters allows for both clustering of objects with regard to covariates and elimination of irrelevant covariates.
Packaged: 2015-09-07 13:47:32 UTC; schaubergerg

Description: Performs 'BTLLasso' as described by Schauberger and Tutz (2019) <doi:10.18637/jss.v088.i09> and Schauberger and Tutz (2017) <doi:10.1177/1471082X17693086>. BTLLasso is a method to include different types of variables in paired comparison models and, therefore, to allow for heterogeneity between subjects. Variables can be subject-specific, object-specific and subject-object-specific and can have an influence on the attractiveness/strength of the objects. Suitable L1 penalty terms are used to cluster certain effects and to reduce the complexity of the models.
Imports: Rcpp (>= 0.11.3), stringr, psychotools

Packaged: 2015-09-28 13:14:46 UTC; schaubergerg

Description: Performs 'BTLLasso', a method to include different types of variables in paired
comparison models and, therefore, to allow for heterogeneity between subjects. Variables can be subject-specific, object-specific and subject-object-specific and
can have an influence on the attractiveness/strength of the objects. Suitable L1 penalty terms are used
to cluster certain effects and to reduce the complexity of the models.
Packaged: 2016-11-22 17:04:51 UTC; schaubergerg