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Showing results 1 to 10 of 1,032.


Function B.Iso [addreg v3.0]
keywords
smooth
title
Defining Smooths in addreg.smooth Formulae
description
Function used in the definition of smooth terms within addreg.smooth model formulae. The function does not evaluate a smooth --- it exists purely to help set up a model using smooths.
Function tricubeMovingAverage [limma v3.28.14]
keywords
smooth
title
Moving Average Smoother With Tricube Weights
description
Apply a moving average smoother with tricube distance weights to a numeric vector.
Function wp.twin [AGD v0.39]
keywords
smooth
title
Superposes two worm plots
description
Superposes two worm plots from GAMLSS fitted objects. This is a diagnostic tool for comparing two solutions.
Function ffcsaps [dplR v1.6.9]
keywords
smooth
title
Smoothing Spline with User-Specified Rigidity and Frequency Cutoff
description
Applies a smoothing spline to y with rigidity determined by two parameters: frequency response f at a wavelength of nyrs years.
Function pass.filt [dplR v1.6.9]
keywords
smooth
title
Low-pass, high-pass, band-pass, and stop-pass filtering
description
Applies low-pass, high-pass, band-pass, or stop-pass filtering to y with frequencies (or periods) supplied by the user.
Function heavyPS [heavy v0.38.19]
keywords
smooth
title
Fit a penalized spline under heavy-tailed distributions
description
Fits a penalized spline to the supplied data.
Function neighborhood [prodlim v2018.04.18]
keywords
smooth
title
Nearest neighborhoods for kernel smoothing
description
Nearest neighborhoods for the values of a continuous predictor. The result is used for the conditional Kaplan-Meier estimator and other conditional product limit estimators.
Function awssigmc [dti v1.4]
keywords
smooth
title
Estimate noise variance for multicoil MR systems
description
The distribution of image intensity values \(S_i\) divided by the noise standard deviation in \(K\)-space \(\sigma\) in dMRI experiments is assumed to follow a non-central chi-distribution with \(2L\) degrees of freedom and noncentrality parameter \(\eta\), where \(L\) refers to the number of receiver coils in the system and \(\sigma \eta\) is the signal of interest. This is an idealization in the sense that each coil is assumed to have the same contribution at each location. For realistic modeling \(L\) should be a locally smooth function in voxel space that reflects the varying local influence of the receiver coils in the the reconstruction algorithm used. The functions assume \(L\) to be known and estimate either a local (function awslsigmc) or global ( function awssigmc) \(\sigma\) employing an assumption of local homogeneity for the noncentrality parameter \(\eta\). Function afsigmc implements estimates from Aja-Fernandez (2009). Function aflsigmc implements the estimate from Aja-Fernandez (2013).
Function smooth.demogdata [demography v1.22]
keywords
smooth
title
Create smooth demogdata functions
description
Smooth demogdata data using one of four methods depending on the value of method
Function cm.spline [demography v1.22]
keywords
smooth
title
Monotonic interpolating splines
description
Perform cubic spline monotonic interpolation of given data points, returning either a list of points obtained by the interpolation or a function performing the interpolation. The splines are constrained to be monotonically increasing (i.e., the slope is never negative).