Various Methods for Measuring Agreement.
Plots for visualization and point and interval estimates
for different metrics of agreement.

Plots for visualization and point and
interval estimates for different metrics of agreement.

Bland-Altman plot and scatter plot with identity line
for visualization and point and
interval estimates for different metrics related to
reproducibility/repeatability/agreement including
the concordance correlation coefficient,
intraclass correlation coefficient,
within-subject coefficient of variation,
smallest detectable difference,
and mean normalized smallest detectable difference.

This package contains some simple functions for the analysis of growth curve experiments.

person("Austin", "Anders", email = "nobilisvenator@hotmail.com", role = c("aut", "cre"))
Pair of simple convenience functions to convert a vector of birth dates to age and age distributions. These functions may be helpful when related age and custom age distributions are desired given a vector of birth dates.

Prior distributions for molecular dating
Calculation of the parameters of Bayesian prior
distributions for divergence dates based on fossil calibrations

Contains functionality for performing the following methods of p-value aggregation: Fisher's method [Fisher, RA (1932, ISBN: 9780028447308)], the Lancaster method (weighted Fisher's method) [Lancaster, HO (1961, <doi:10.1111/j.1467-842X.1961.tb00058.x>)], and Sidak correction [Sidak, Z (1967, <doi:10.1080/01621459.1967.10482935>)].  Please cite Yi et al., the manuscript corresponding to this package [Yi, L et al., (2017), <doi:10.1101/190199>].

Contains functionality for performing the following methods of p-value aggregation: Fisher's method [Fisher, RA (1932, ISBN: 9780028447308)], the Lancaster method (weighted Fisher's method) [Lancaster, HO (1961, <doi:10.1111/j.1467-842X.1961.tb00058.x>)], and Sidak correction (minimum p-value method with correction) [Sidak, Z (1967, <doi:10.1080/01621459.1967.10482935>)].

Estimate individual level risk using individual case data and
spatially aggregated control data
This package uses weighted estimating equations to
estimate regression parameters in the intensity function of an
inhomogeneous spatial Poisson process, when information on
risk-factors is available at the individual level for cases,
but only at a spatially aggregated level for the population at
risk. Data-driven methods are used to select the weights used
in the estimating equations. A formal test is available to
detect non-Poisson behaviour in the underlying point process.