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keywords: covariate
title: R function to model (and test) the dependence of a point pattern on a spatial numeric covariate
description: The function is a wrapper for a number of functions out of the extremely useful 'spatstat' package (specifically, ppm(), cdf.test(), auc(), roc(), effectfun()). It allows to test if there is a significant dependence of the input point pattern on a underlying spatial numeric covariate (first-order effect). The function takes as input three datasets: a point patter ('SpatialPointsDataFrame' class), a covariate layer (of 'RasterLayer' class), and a polygon feature ('SpatialPolygonsDataFrame' class) representing the study area and exactly matching the extent of the covariate layer. If the latter is not provided, it is internally worked out from the covariate raster and may make the whole function take a while to complete.

keywords: covariate balance
title: Check covariate balance of a design.
description: For a given match, this function evaluates the balance of variables before and after matching. Balance is evaluated using standardized differences.

keywords: covariate
title: Parse formula to obtain grouping variable and covariate vector
description: This function parses the formula used in matchit() to obtain the grouping variable and the covariate vector.

keywords: covariate
title: Plot covariate paths for BTLLasso
description: Plots paths for every covariate of a BTLLasso object or a cv.BTLLasso object. In contrast to plot.BTLLasso, only one plot is created, every covariate is illustrated by one path. For cv.BTLLasso objects, the optimal model according to the cross-validation is marked by a vertical dashed line.

keywords: covariate balance
title: Analyze covariate balance before and after matching.
description: Generic function for analyzing covariate balance. If match.out is NULL only balance statistics for the unmatched data are returned otherwise both before and after matching balance are given. The function is simply a wrapper calling MatchBalance, possibly after coercing the class of match.out. See MatchBalance for more detailed description.

keywords: covariate-by-covariate-constrained-randomization
title: Covariate-by-covariate constrained randomization for cluster randomized trials
description: cvrcov performs covariate-by-covariate constrained randomization for cluster randomized trials (CRTs), especially suited for CRTs with a small number of clusters. In constrained randomization, a randomization scheme is randomly sampled from a subset of all possible randomization schemes based on the constraints on each covariate. The cvrcov function enumerates all randomization schemes or simulates a fixed size of unique randomization schemes as specified by the user. A subset of the randomization schemes is chosen based on user-specified covariate-by-covariate constraints. cvrcov treats the subset as the constrained space of randomization schemes and samples one scheme from the constrained space as the final chosen scheme.

keywords: compound covariate
title: Compound shrinkage estimation under the Cox model
description: This function implements the "compound shrinkage estimator" to calculate the regression coefficients of the Cox model, which was proposed by Emura, Chen & Chen (2012). The method is a variant of the Cox partial likelihood estimator such that the regression coefficients are mixed with the univariate Cox regression estimators. The resultant estimator is applicable even when the number of covariates is greater than the number of samples (the p>n setting). The standard errors (SEs) are calculated based on the asymptotic theory (see Emura et al., 2012).

keywords: Covariate Mean Differences
title: Get Covariate Mean Differences
description: getCovMeanDiffs returns the covariate mean differences between two groups.

keywords: Covariate Mean Differences
title: Get Standardized Covariate Mean Differences
description: getStandardizedCovMeanDiffs returns the standardized covariate mean differences between two groups.