A comprehensive modeling framework for epidemiological outcomes and a multiple-imputation approach to haplotypic analysis of population-based data
SimHap is a package for genetic association testing. It can perform single SNP and multi-locus (haplotype) association analyses for continuous Normal, binary, longitudinal and right-censored outcomes measured in population-based samples. SimHap uses estimation maximisation techniques for inferring haplotypic phase in individuals, and incorporates a multiple-imputation approach to deal with the uncertainty of imputed haplotypes in association testing.

A comprehensive modeling framework for epidemiological outcomes
and a simulation-based approach to haplotypic analysis of
population-based data
SimHap is a package for genetic association testing. It
can perform single SNP and multi-locus (haplotype) association
analyses for continuous Normal, binary, longitudinal and
right-censored outcomes measured in population-based samples.
SimHap uses estimation maximisation techniques for inferring
haplotypic phase in individuals, and incorporates a novel
simulation-based approach to deal with the uncertainty of
imputed haplotypes in association testing.

Simulated Survival and Hazard Analysis for Time-Dependent
Exposure
Danyi Xiong, Teeranan Pokaprakarn, Hiroto Udagawa, Nusrat Rabbee
Generate power for the Cox proportional hazards model by simulating survival events data with time dependent exposure status for subjects. A dichotomous exposure variable is considered with a single transition from unexposed to exposed status during the subject's time on study.

A Framework for Stochastic Disease Spread Simulations
Stefan Widgren (National Veterinary Institute, Sweden),
Pavol Bauer (Uppsala University, Sweden),
Stefan Engblom (Uppsala University, Sweden)
Livestock movements are important for the spread of many
infectious diseases between herds. The package provides an
efficient and flexible framework for stochastic disease spread
modelling that integrates within-herd disease dynamics as
continuous-time Markov chains and livestock movements between
herds as scheduled events. The core simulation solver is
implemented in C and uses 'OpenMP' (if available) to divide work
over multiple processors. The package contains template models and
can be extended with user defined models.
This work was financially supported by the Swedish
Research Council within the UPMARC Linnaeus center of
Excellence (Pavol Bauer and Stefan Engblom).
'AllGenerics.R' 'check_arguments.R' 'scheduled_events.R'
'siminf_model.R' 'SISe.R' 'SISe3.R' 'demo_model.R'
'dependencies.R' 'siminf.R'