Fits Regression & ARMA Models Subject to Constraints to the
Coefficient
Fits or generalized linear models either a regression with Autoregressive moving-average (ARMA) errors for time series data.
The package makes it easy to incorporate constraints into the model's coefficients.
The model is specified by an objective function (Gaussian, Binomial or Poisson) or an ARMA order (p,q),
a vector of bound constraints
for the coefficients (i.e beta1 > 0) and the possibility to incorporate restrictions
among coefficients (i.e beta1 > beta2).
The references of this packages are the same as 'stats' package for glm() and arima() functions.
See Brockwell, P. J. and Davis, R. A. (1996, ISBN-10: 9783319298528).
For the different optimizers implemented, it is recommended to consult the documentation of the corresponding packages.
data.table (>= 1.10), forecast (>= 8.0), rlang (>= 0.4),
nloptr (>= 1.2), FME (>= 1.3), MCMCpack (>= 1.4), Rsolnp (>=
1.15), DEoptim (>= 2.2), dfoptim, GA (>= 3.0), GenSA (>= 1.1),
Metrics, ggplot2, adaptMCMC, Rcpp

Predictive Probability for a Continuous Response with an ANOVA
Structure
c(
person(given = "Victoria",
family = "Sieck",
role = c("aut", "cre"),
email = "vcarrillo314@gmail.com",
comment = c(ORCID = "0000-0002-7169-7795")),
person(given = "Joshua",
family = "Clifford",
role = "aut",
email = "jcliff89@gmail.com",
comment = c(ORCID = "0000-0002-6969-2581")),
person(given = "Fletcher",
family = "Christensen",
role = "aut",
email = "ronald@stat.unm.edu",
comment = c(ORCID = "0000-0002-5303-8139"))
)
A Bayesian approach to using
predictive probability in an ANOVA construct with a continuous normal response,
when threshold values must be obtained for the question of interest to be
evaluated as successful (Sieck and Christensen (2021) <doi:10.1002/qre.2802>).
The Bayesian Mission Mean (BMM) is used to evaluate a question
of interest (that is, a mean that randomly selects combination of factor levels
based on their probability of occurring instead of averaging over the factor
levels, as in the grand mean). Under this construct, in contrast to a Gibbs
sampler (or Metropolis-within-Gibbs sampler), a two-stage sampling method is
required. The nested sampler determines the conditional posterior distribution
of the model parameters, given Y, and the outside sampler determines the marginal
posterior distribution of Y (also commonly called the predictive distribution for Y).
This approach provides a sample from the joint posterior distribution of Y and
the model parameters, while also accounting for the threshold value that must be
obtained in order for the question of interest to be evaluated as successful.
rjags, coda, knitr, devtools, rmarkdown, testthat (>= 3.0.0)
Victoria Sieck [aut, cre] (<https://orcid.org/0000-0002-7169-7795>),
Joshua Clifford [aut] (<https://orcid.org/0000-0002-6969-2581>),
Fletcher Christensen [aut] (<https://orcid.org/0000-0002-5303-8139>)