Statistical methods for safety assessment in agricultural field
trials
Collection of functions, data sets and code examples for
evaluations of field trials with the objective of safety
assessment.
R (>= 2.7.0), mvtnorm, multcomp, gamlss, mratios, MCPAN,
pairwiseCI, lattice, geepack, boot

This package models the quantile process as a function of
predictors.

Modelling Heterogeneity in Paired Comparison Data
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.

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.

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.

Performs 'BTLLasso' (Schauberger and Tutz, 2017: Subject-Specific Modelling of Paired Comparison Data - a Lasso-Type Penalty Approach), 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.