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Also analyzed are 2 x t contingency tables and their blocked combinations using the Kruskal-Wallis criterion. Except for Steel's test and the JT test it also combines these tests across several blocks of samples. It computes asymptotic, simulated or (limited) exact P-values, all valid under randomization, with or without ties, or conditionally under random sampling from populations, given the observed tie pattern. We provide an ability to parse KVH files in R pretty fast due to 'Rcpp' use.Ĭompares k samples using the Anderson-Darling test, Kruskal-Wallis type tests with different rank score criteria, Steel's multiple comparison test, and the Jonckheere-Terpstra (JT) test. It can be useful in situations where XML or alike formats seem to be an overkill. The format KVH is a lightweight format that can be read/written both by humans and machines.
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Helper functions for creating formatted summary of regression models, writing publication-ready tables to latex files, and running Monte Carlo experiments.
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Time Series Analysis including break detection, spectral analysis, KZ Fourier Transforms. Adaptive ridge algorithms are used to fit the models. L1 estimation for linear regression, density, distribution function, quantile function and random number generation for univariate and multivariate Laplace distribution.Īn efficient procedure for feature selection for generalized linear models with L0 penalty, including linear, logistic, Poisson, gamma, inverse Gaussian regression. The LaF vignette gives an overview of the functionality provided. Furthermore, an opened file can be accessed as one would an ordinary ame. Methods are provided to access and process files blockwise. It is assumed that the files are too large to fit into memory, although the package can also be used to efficiently access files that do fit into memory. Currently the following file formats are supported: comma separated format (CSV) and fixed width format. Methods for fast access to large ASCII files. Outputs are raster dataset exportable to any common GIS format. The basic algorithm currently implemented uses a simple agent style/cellular automata growth model, with no rules (apart from areas of exclusion) and von Neumann neighbourhood (four cells, aka Rook case). In this it differs from other tools which generate null or neutral landscapes in a theoretical space. Allows to tweak or create landscapes while retaining a high degree of control on its features, without the hassle of specifying each location attribute. Simulates categorical maps on actual geographical realms, starting from either empty landscapes or landscapes provided by the user (e.g. (1998).Įstimate drift and diffusion functions from time series and generate synthetic time series from given drift and diffusion coefficients.įunctions to write or append a R list to a file, as well as read, remove, modify elements from it without restoring the whole list. Routines and documentation for solving regression problems while imposing an L1 constraint on the estimates, based on the algorithm of Osborne et al.
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If the NLopt library cannot be found, the code is compiled using the NLopt source included in the nloptr package. During installation of nloptr on Unix-based systems, the installer checks whether the NLopt library is installed on the system. See for more information on the available algorithms.
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NLopt is a free/open-source library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. Solve optimization problems using an R interface to NLopt. A few wrapper functions compatible with 'rzmq' are also provided. For convenience, a minimal 'ZeroMQ' library (4.2.2) is shipped with 'pbdZMQ', which can be used if no system installation of 'ZeroMQ' is available. We mainly focus on interactive client/server programming frameworks. This package provides high level R wrapper functions to easily utilize 'ZeroMQ'. 'ZeroMQ' is a well-known library for high-performance asynchronous messaging in scalable, distributed applications.
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R is a free software environment for statistical computing and graphics. Some essential packages for working with R