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

Title: Various Methods for Measuring Agreement
Description: Bland-Altman plot and scatter plot with identity line
interval estimates for different metrics related to
and mean normalized smallest detectable difference.

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

Packaged: Wed Feb  8 12:58:30 2006; hornik

Authors@R: person("Austin", "Anders", email = "nobilisvenator@hotmail.com", role = c("aut", "cre"))
Description: 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.

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

Description: 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>].

Description: 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>)].

Title: Estimate individual level risk using individual case data and
Description: 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.