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/dports/math/R-cran-forecast/forecast/R/
H A DHoltWintersNew.R209 damped <- (phi < 1.0) functionVar
211 components <- c("A", trendtype, seasontype, damped)
213 components <- c("M", trendtype, seasontype, damped)
215 components <- c("M", trendtype, seasontype, damped)
217 components <- c("A", trendtype, seasontype, damped)
231 if (damped) {
472 holt <- function(y, h = 10, damped = FALSE, level = c(80, 95), fan = FALSE, argument
479 if (initial == "optimal" || damped) {
492 if (damped) {
525 if (initial == "optimal" || damped) {
[all …]
H A Dets.R271 if (!is.null(damped)) {
286 if (!is.null(damped)) {
292 if (!is.null(damped)) {
293 if (damped) {
387 if (is.null(damped)) {
388 damped <- c(TRUE, FALSE)
766 if (damped) {
813 if (!damped) {
838 damped = damped, lowerb = lowerb, upperb = upperb,
1050 if (damped) {
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H A Detsforecast.R95 damped <- as.logical(object$components[4]) functionVar
103 …f <- class1(h, object$states[n + 1, ], object$components[2], object$components[3], damped, object$…
105 …f <- class2(h, object$states[n + 1, ], object$components[2], object$components[3], damped, object$…
107 …f <- class3(h, object$states[n + 1, ], object$components[2], object$components[3], damped, object$…
199 class1 <- function(h, last.state, trendtype, seasontype, damped, m, sigma2, par) { argument
206 if (damped) {
215 if (damped) {
254 class2 <- function(h, last.state, trendtype, seasontype, damped, m, sigma2, par) { argument
255 tmp <- class1(h, last.state, trendtype, seasontype, damped, m, sigma2, par)
266 class3 <- function(h, last.state, trendtype, seasontype, damped, m, sigma2, par) { argument
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/dports/shells/ksh93-devel/ast-cc1bca27/src/cmd/dsslib/bgp/data/
H A Da02.out19 damped 0
99 damped 0
179 damped 0
259 damped 0
339 damped 0
419 damped 0
499 damped 0
579 damped 0
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H A Da08.out19 damped 0
99 damped 0
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339 damped 0
419 damped 0
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579 damped 0
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H A Da05.out19 damped 0
99 damped 0
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259 damped 0
339 damped 0
419 damped 0
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579 damped 0
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H A Da07.out19 damped 0
99 damped 0
179 damped 0
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339 damped 0
419 damped 0
499 damped 0
579 damped 0
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H A Da01.out19 damped 0
99 damped 0
179 damped 0
259 damped 0
339 damped 0
419 damped 0
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579 damped 0
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H A Da03.out19 damped 0
99 damped 0
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259 damped 0
339 damped 0
419 damped 0
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H A Da06.out19 damped 0
99 damped 0
179 damped 0
259 damped 0
339 damped 0
419 damped 0
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579 damped 0
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H A Da04.out19 damped 0
99 damped 0
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259 damped 0
339 damped 0
419 damped 0
499 damped 0
579 damped 0
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/dports/shells/ksh93/ast-93u/src/cmd/dsslib/bgp/data/
H A Da02.out19 damped 0
99 damped 0
179 damped 0
259 damped 0
339 damped 0
419 damped 0
499 damped 0
579 damped 0
659 damped 0
739 damped 0
[all …]
H A Da08.out19 damped 0
99 damped 0
179 damped 0
259 damped 0
339 damped 0
419 damped 0
499 damped 0
579 damped 0
659 damped 0
739 damped 0
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H A Da05.out19 damped 0
99 damped 0
179 damped 0
259 damped 0
339 damped 0
419 damped 0
499 damped 0
579 damped 0
659 damped 0
739 damped 0
[all …]
H A Da01.out19 damped 0
99 damped 0
179 damped 0
259 damped 0
339 damped 0
419 damped 0
499 damped 0
579 damped 0
659 damped 0
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H A Da03.out19 damped 0
99 damped 0
179 damped 0
259 damped 0
339 damped 0
419 damped 0
499 damped 0
579 damped 0
659 damped 0
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H A Da07.out19 damped 0
99 damped 0
179 damped 0
259 damped 0
339 damped 0
419 damped 0
499 damped 0
579 damped 0
659 damped 0
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H A Da06.out19 damped 0
99 damped 0
179 damped 0
259 damped 0
339 damped 0
419 damped 0
499 damped 0
579 damped 0
659 damped 0
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H A Da04.out19 damped 0
99 damped 0
179 damped 0
259 damped 0
339 damped 0
419 damped 0
499 damped 0
579 damped 0
659 damped 0
739 damped 0
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/dports/math/vtk9/VTK-9.1.0/ThirdParty/vtkm/vtkvtkm/vtk-m/examples/oscillator/inputs/
H A Dsample.osc2 damped 32 32 32 10. 3.14 .3
3 damped 16 32 16 10. 9.5 .1
4 damped 48 32 48 5. 3.14 .1
/dports/math/vtk8/VTK-8.2.0/ThirdParty/vtkm/vtk-m/examples/oscillator/inputs/
H A Dsample.osc2 damped 32 32 32 10. 3.14 .3
3 damped 16 32 16 10. 9.5 .1
4 damped 48 32 48 5. 3.14 .1
/dports/math/openturns/openturns-1.18/python/src/
H A DExponentiallyDampedCosineModel_doc.i.in2 "Exponentially damped cosine covariance function.
26 The *exponentially damped cosine* function is a stationary covariance function with dimension :math…
30 The *exponentially damped cosine* covariance function is defined by:
51 Create a standard exponentially damped cosine covariance function:
63 Create a exponentially damped cosine covariance function specifying the amplitude and the scale:
67 Create a exponentially damped cosine covariance function specifying the amplitude and the scale:
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/tests/results/
H A Dfit_ets.R27 for (damped in c(TRUE, FALSE)) { functionVar
31 fit <- ets(data, model = model, damped = damped)
50 results[[as.character(damped)]] <- results_damped
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/tests/
H A Dtest_exponential_smoothing.py72 + short_name[damped]
92 error, trend, seasonal, damped = params[0:4]
101 results = ets_oildata_fit_results_R[damped]
116 damped_trend=damped,
296 for damped in R_results:
297 new_key = damped == "TRUE"
299 for model in R_results[damped]:
300 if len(R_results[damped][model]):
304 for damped in results:
305 for model in results[damped]:
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/dports/graphics/plotutils/plotutils-2.6/ode-examples/
H A Dddho.ode15 # If R^2 = 4km, motion is critically damped
16 # If R^2 < 4km, motion is damped
18 # With the choice of parameters below, motion is damped but not

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