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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.

keywords: smooth
title: Moving Average Smoother With Tricube Weights
description: Apply a moving average smoother with tricube distance weights to a numeric vector.

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.

keywords: smooth
title: Create smooth demogdata functions
description: Smooth demogdata data using one of four methods depending on the value of method

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).

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.

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.

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.

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).