Package dse provides tools for multivariate, linear, time-invariant,
time series models. It includes ARMA and state-space representations,
and methods for converting between them. It also includes simulation
methods and several estimation functions. The package has functions
for looking at model roots, stability, and forecasts at different
horizons. The ARMA model representation is general, so that VAR, VARX,
ARIMA, ARMAX, ARIMAX can all be considered to be special cases. Kalman
filter and smoother estimates can be obtained from the state space
model, and state-space model reduction techniques are implemented.
An introduction and User's Guide is available in a vignette.

Dynamic Systems Estimation (Time Series Package)
Tools for multivariate, linear, time-invariant,
time series models. This includes ARMA and state-space representations,
and methods for converting between them. It also includes simulation
methods and several estimation functions. The package has functions
for looking at model roots, stability, and forecasts at different
horizons. The ARMA model representation is general, so that VAR, VARX,
ARIMA, ARMAX, ARIMAX can all be considered to be special cases. Kalman
filter and smoother estimates can be obtained from the state space
model, and state-space model reduction techniques are implemented.
An introduction and User's Guide is available in a vignette.
tframe (>= 2007.5-3), stats, utils, graphics, grDevices,
setRNG (>= 2004.4-1)

syskern tframe dse1 dse2 dsepadi monitor juice curve

This package is only to aid transition to the unbundled
dse package. It has no functions, but simply requires package
EvalEst.

This package is only to aid transition to the unbundled dse
package. It has no functions, but simply requires package EvalEst.

Multivariate Time Series Library - Extensions

Multivariate Time Series Library - Extensions
See also the related bundle dse.

juice curve syskern padi dsepadi monitor

syskern padi dsepadi monitor juice curve

c(person(given = "Rouven",
family = "Schmidt",
role = c("aut", "cre"),
email = "rouven.schmidt@tu-clausthal.de"))
Framework to fit distributional stochastic frontier models. Casts the stochastic frontier model into the flexible framework of distributional regression or otherwise known as General Additive Models of Location, Scale and Shape (GAMLSS). Allows for linear, non-linear, random and spatial effects on all the parameters of the distribution of the output, e.g. effects on the production or cost function, heterogeneity of the noise and inefficiency. Available distributions are the normal-halfnormal and normal-exponential distribution. Estimation via the fast and reliable routines of the 'mgcv' package. For more details see Schmidt R, Kneib T (2022) <doi:10.48550/arXiv.2208.10294>.
mgcv, sn, gratia, graphics, stats, copula, numDeriv, Rdpack