Gapped-Kmer Support Vector Machine (gkmSVM)
Imports the ‘gkmSVM’ v2.0 functionalities into R <http://…/> It also uses the ‘kernlab’ library (separate R package by different authors) for various SVM algorithms.

Computing P-Values of the K-S Test for (Dis)Continuous Null Distribution (KSgeneral)
Computes a p-value of the one-sample two-sided (or one-sided, as a special case) Kolmogorov-Smirnov (KS) statistic, for any fixed critical level, and an arbitrary, possibly large sample size for a pre-specified purely discrete, mixed or continuous cumulative distribution function (cdf) under the null hypothesis. If a data sample is supplied, ‘KSgeneral’ computes the p-value corresponding to the value of the KS test statistic computed based on the user provided data sample. The package ‘KSgeneral’ implements a novel, accurate and efficient method named Exact-KS-FFT, expressing the p-value as a double-boundary non-crossing probability for a homogeneous Poisson process, which is then efficiently computed using Fast Fourier Transform (FFT). The package can also be used to compute and plot the complementary cdf of the KS statistic which is known to depend on the hypothesized distribution when the latter is discontinuous (i.e. purely discrete or mixed).

Access Elevation Data from Various APIs (elevatr)
Several web services are available that provide access to elevation data. This package provides access to several of those services and returns elevation data either as a SpatialPointsDataFrame from point elevation services or as a raster object from raster elevation services. Currently, the package supports access to the Mapzen Elevation Service <https://…/>, Mapzen Terrain Service <https://…/>, Amazon Web Services Terrain Tiles <https://…/> and the USGS Elevation Point Query Service <http://…/>.

Rank-Based Estimation and Prediction in Random Effects Nested Models (rlme)
Estimates robust rank-based fixed effects and predicts robust random effects in two- and three- level random effects nested models. The methodology is described in Bilgic & Susmann (2013) <https://…/>.

Estimating the Error Variance in a High-Dimensional Linear Model (natural)
Implementation of the two error variance estimation methods in high-dimensional linear models of Yu, Bien (2017) <arXiv:1712.02412>.

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