'ACE' (Advanced Cohort Engine) is a powerful tool that allows constructing cohorts of patients
extremely quickly and efficiently. This package is designed to interface directly with an
instance of 'ACE' search engine and facilitates API queries and data dumps. Prerequisite
is a good knowledge of the temporal language to be able to efficiently construct a query.
More information available at <https://shahlab.stanford.edu/start>.

ACE Kin Pair Data Simulations and Model Fitting
c(person("Xuanyu", "Lyu", email = "lyux20@wfu.edu", role = c("aut", "cre")),
person("S.Mason", "Garrison",email = "garrissm@wfu.edu", role = "aut"))
A few functions aim to provide a statistic tool for three purposes. First, simulate kin pairs data based on the assumption that every trait is affected by genetic effects (A), common environmental effects (C) and unique environmental effects (E).Second, use kin pairs data to fit an ACE model and get model fit output.Third, calculate power of A estimate given a specific condition. For the mechanisms of power calculation, we suggest to check Visscher(2004)<doi:10.1375/twin.7.5.505>.
Xuanyu Lyu [aut, cre],
S.Mason Garrison [aut]

Estimating Age Modification Effect on Genetic and Environmental
Variance Components in Twin Models
Two novel twin models that are able to estimate the dynamic behaviour of the variance components in the classical twin models with respect to age.

Twin models that are able to estimate the dynamic behaviour of the variance components in the classical twin models with respect to age using B-splines and P-splines.

Estimating Dynamic Heritability and Twin Model Comparison

Estimation of Dynamic Heritability and Comparison of Twin Models
person("Liang", "He", email = "liang.he@duke.edu",role = c("aut", "cre"))
Estimate the dynamic behaviour of the variance components in the classical twin models with respect to age using B-splines and P-splines.

Rcpp (>= 0.11.1), splines, MASS, methods

Estimation of Interpretable eQTL Effect Sizes Using a Log of
Linear Model
We use a non-linear model, termed ACME,
that reflects a parsimonious biological model for
allelic contributions of cis-acting eQTLs.
With non-linear least-squares algorithm we
estimate maximum likelihood parameters. The ACME model
provides interpretable effect size estimates and
p-values with well controlled Type-I error.
Includes both R and (much faster) C implementations.