Objects and Methods for Multi-Companion Matrices
Provides a class for multi-companion matrices with methods for
arithmetic and factorization.  A method for generation of
multi-companion matrices with prespecified spectral properties is
provided, as well as some utilities for periodically correlated and
multivariate time series models. See Boshnakov (2002)
<doi:10.1016/j.laa.2007.02.010> and Boshnakov & Iqelan (2009)
<doi:10.1111/j.1467-9892.2009.00617.x>.
matutils.R mc.R mc01zero.R mc02gen.R auto_sim.R classMC.R
auto_classMF.R classmcSpec.R spaces.R classJordan.R jd.R
jdecomp.R classSMC.R smc.R make_chains.R

Provides a class for multi-companion matrices with methods for
arithmetic and factorization.  A method for generation of
multi-companion matrices with prespecified spectral properties is
provided, as well as some utilities for periodically correlated and
multivariate time series models. See Boshnakov (2002)
<doi:10.1016/S0024-3795(01)00475-X> and Boshnakov & Iqelan (2009)
<doi:10.1111/j.1467-9892.2009.00617.x>.
mc.R mcompanion.R utils_Jordan.R mat.R sim.R class_MC.R
class_MF.R class_Jordan.R chains_smc.R class_SMC.R
class_mcSpec.R

methods, Matrix (>= 1.5-0), gbutils, MASS, Rdpack

Zhengyang Zhou [aut, cre]
Dateng Li [aut]
David Huh [aut]
Eun-Young Mun [aut]
Implementation of marginalized models for zero-inflated count data. This package provides a tool to implement an estimation algorithm for the marginalized count models, which
directly makes inference on the effect of each covariate on the marginal mean of the outcome.
The method involves the marginalized zero-inflated Poisson model described
in Long et al. (2014) <doi:10.1002/sim.6293>.

Flexible and informed regression with Multiple Change Points (MCP). 'mcp' can infer change points in means, variances, autocorrelation structure, and any combination of these, as well as the parameters of the segments in between. All parameters are estimated with uncertainty and prediction intervals are supported - also near the change points. 'mcp' supports hypothesis testing via Savage-Dickey density ratio, posterior contrasts, and cross-validation. 'mcp' provides a generalization of the approach described in Carlin, Gelfand, & Smith (1992) <doi:10.2307/2347570> and Stephens (1994) <doi:10.2307/2986119>.
hexbin, testthat (>= 2.1.0), knitr, rmarkdown, covr