While autoregressive distributed lag models allow for extremely flexible dynamics, interpreting substantive significance of complex lag structures remains difficult. This package is designed to assist users in dynamically simulating and plotting the results of various autoregressive distributed lag models. It also contains post-estimation diagnostics, including a test for cointegration when estimating the error-correction variant of the autoregressive distributed lag model (Pesaran, Shin, and Smith 2001 <doi:10.1002/jae.616>).

c(
person("Melissa", "G. Wolf", email = "missgord@gmail.com",
role = c("aut", "cre")),
person("Daniel", "McNeish", email = "dmcneish@asu.edu",
role = "aut"))
Returns dynamic fit index (DFI) cutoffs for latent variable models
that are tailored to the user's model statement, model type, and sample size.
This is the counterpart of the Shiny Application, <https://dynamicfit.app>.
Melissa G. Wolf [aut, cre],
Daniel McNeish [aut]

Interactive graphical tool for manipulating graphs

Fri Apr 3 23:25:55 2009; Jens Henrik Badsberg

2010-01-31 13:29:31 UTC; Jens Henrik Badsberg

Methods for detection of clusters in hierarchical clustering dendrograms.
Peter Langfelder <Peter.Langfelder@gmail.com> and Bin Zhang <binzhang.ucla@gmail.com>,
with contributions from Steve Horvath <SHorvath@mednet.ucla.edu>
Contains methods for detection of clusters in hierarchical clustering dendrograms.

Methods for detection of clusters in hierarchical clustering
dendrograms.
Peter Langfelder <Peter.Langfelder@gmail.com> and Bin Zhang
<binzhang.ucla@gmail.com>, with contributions from Steve
Horvath <SHorvath@mednet.ucla.edu>
Contains methods for detection of clusters in hierarchical
clustering dendrograms.

Methods for Detection of Clusters in Hierarchical Clustering
Dendrograms

Dynamic Hazard Models using State Space Models
c(person("Benjamin", "Christoffersen",
email = "boennecd@gmail.com",
role = c("cre", "aut")),
person("Alan", "Miller",
role = c("ctb")),
person("Anthony", "Williams",
role = c("ctb")),
person("Boost developers",
role = c("ctb", "cph")),
person("R-core", email = "R-core@R-project.org",
role = c("ctb", "cph")))
Contains functions that lets you fit dynamic hazard models with binary
outcomes using state space models. The methods are originally described in
Fahrmeir (1992) <doi:10.1080/01621459.1992.10475232> and Fahrmeir (1994)
<doi:10.1093/biomet/81.2.317>. The functions also provide an extension hereof where the
Extended Kalman filter is replaced by an Unscented Kalman filter. Models are
fitted with the regular coxph() like formula.
Rcpp (>= 0.12.6), stats, graphics, utils, parallel, stringr,
survival
testthat, knitr, rmarkdown, timereg, captioner, biglm, httr,
mgcv, shiny, formatR
Benjamin Christoffersen [cre, aut],
Alan Miller [ctb],
Anthony Williams [ctb],
Boost developers [ctb, cph],
R-core [ctb, cph]