This package provides a framework for handling
data and analysis for mark-recapture.
Currently it is limited to Cormack-Jolly-Seber and
Jolly-Seber models.
R2admb, truncnorm, coda, optimx (>= 2013.8.6), ggplot2,
Matrix, numDeriv, Rcpp (>= 0.9.13), plyr
'cjs.accumulate.R' 'cjs.initial.R' 'cjs.lnl.r' 'cjs.R'
'create.dm.R' 'create.dmdf.R' 'create.links.r' 'crm.R'
'fix.parameters.r' 'js.lnl.r' 'js.r' 'hmmDemo.r'
'make.design.data.R' 'marked-package.R'
'merge.design.covariates.r' 'print.crm.r' 'process.ch.R'
'process.data.R' 'setup.model.R' 'setup.parameters.R'
'utility.R' 'valid.parameters.R' 'zzz.R' 'probitCJS.R'
'splitCH.R' 'cjs.hessian.r' 'extract.fcts.r' 'summary.fcts.R'
'crm.wrapper.R' 'js.hessian.R' 'js.accumulate.R'
'compute.real.R' 'convert.link.to.real.r'
'deriv.inverse.link.R' 'inverse.link.R' 'predict.crm.r'
'scale.r' 'set.initial.r' 'mscjs.r' 'mixed.model.r'
'HMMLikelihood.r' 'Transition_functions.r'
'Observation_functions.r' 'simHMM.r'
'InitialDistribution_functions.r' 'probitMsCJS.R' 'set.fixed.R'
'proc.formula.r'

This package provides a framework for handling
data and analysis for mark-recapture.

R2admb, truncnorm, coda, ggplot2, Matrix, numDeriv, Rcpp (>=
0.9.13), plyr, optimx (>= 2013.8.6), expm

Jeff Laake <jefflaake@gmail.com>, Devin Johnson
<devin.johnson@noaa.gov>, Paul Conn <paul.conn@noaa.gov>, example for simHMM
from Jay Rotella
Functions for fitting various models to capture-recapture data
including mixed-effects Cormack-Jolly-Seber(CJS) and multistate models and
the multi-variate state model structure for survival
estimation and POPAN structured Jolly-Seber models for abundance estimation.
There are also Hidden Markov model (HMM) implementations of CJS and multistate
models with and without state uncertainty and a simulation capability for HMM
models.

graphics, stats, utils, R2admb, truncnorm, coda, Matrix,
numDeriv, expm, Rcpp (>= 0.9.13), TMB, optimx (>= 2013.8.6),
data.table, knitr, kableExtra, bookdown

Power calculation for marker detection strategies in Genome-wide
Association Studies
This package calcuates statistical power of detecting
associated markers based on one of the model selection
strategies: marginal selection, exhaustive search, or forward
selection. With assumed genetic effects (including interaction
effect), allele frequencies, noise level, sample size, number
of genotyped markers, and control level (i.e. number of
markers/models intended to select), this package provides fast
and accurate consultation on power of different model selection
strategies. It helps researchers to decide a more efficient way
for marker detection in genome-wide association studies.