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r-twosamples
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Fast randomization based two sample tests. Testing the hypothesis that two samples come from the same distribution using randomization to create p-values. Included tests are: Kolmogorov-Smirnov, Kuiper, Cramer-von Mises, Anderson-Darling, Wasserstein, and DTS. The default test (two_sample) is based on the DTS test statistic, as it is the most powerful, and thus most useful to most users. The DTS test statistic builds on the Wasserstein distance by using a weighting scheme like that of Anderson-Darling. See the companion paper at <arXiv:2007.01360> or <https://codowd.com/public/DTS.pdf> for details of that test statistic, and non-standard uses of the package (parallel for big N, weighted observations, one sample tests, etc). We also include the permutation scheme to make test building simple for others.
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2025-04-22 |
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r-twophaseind
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Estimation of gene-treatment interactions in randomized clinical trials exploiting gene-treatment independence. Methods used in the package refer to J. Y. Dai, M. LeBlanc, and C. Kooperberg (2009) Biometrics <doi:10.1111/j.1541-0420.2008.01046.x>.
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2025-04-22 |
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r-twocop
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This package implements the nonparametric test of equality between two copulas proposed by Remillard and Scaillet in their 2009 JMVA paper.
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2025-04-22 |
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r-tweedie
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Maximum likelihood computations for Tweedie families, including the series expansion (Dunn and Smyth, 2005; <doi:10.1007/s11222-005-4070-y>) and the Fourier inversion (Dunn and Smyth, 2008; <doi:10.1007/s11222-007-9039-6>), and related methods.
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2025-04-22 |
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r-tvsmiss
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Use a regularization likelihood method to achieve variable selection purpose. Likelihood can be worked with penalty lasso, smoothly clipped absolute deviations (SCAD), and minimax concave penalty (MCP). Tuning parameter selection techniques include cross validation (CV), Bayesian information criterion (BIC) (low and high), stability of variable selection (sVS), stability of BIC (sBIC), and stability of estimation (sEST). More details see Jiwei Zhao, Yang Yang, and Yang Ning (2018) <arXiv:1703.06379> "Penalized pairwise pseudo likelihood for variable selection with nonignorable missing data." Statistica Sinica.
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2025-04-22 |
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r-tvd
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Total Variation Denoising is a regularized denoising method which effectively removes noise from piecewise constant signals whilst preserving edges. This package contains a C++ implementation of Condat's very fast 1D squared error loss TVD algorithm. Additional methods and loss functions may be added in future versions.
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2025-04-22 |
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r-tuwmodel
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The model, developed at the Vienna University of Technology, is a lumped conceptual rainfall-runoff model, following the structure of the HBV model. The model can also be run in a semi-distributed fashion and with dual representation of soil layer. The model runs on a daily or shorter time step and consists of a snow routine, a soil moisture routine and a flow routing routine. See Parajka, J., R. Merz, G. Bloeschl (2007) <DOI:10.1002/hyp.6253> Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments, Hydrological Processes, 21, 435-446.
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2025-04-22 |
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r-tunepareto
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Generic methods for parameter tuning of classification algorithms using multiple scoring functions (Muessel et al. (2012), <doi:10.18637/jss.v046.i05>).
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2025-04-22 |
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r-tth
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C source code and R wrappers for the tth/ttm TeX-to-HTML/MathML translators.
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2025-04-22 |
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r-tsss
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Functions for statistical analysis, modeling and simulation of time series with state space model, based on the methodology in Kitagawa (2020, ISBN: 978-0-367-18733-0).
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2025-04-22 |
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r-tsp
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Basic infrastructure and some algorithms for the traveling salesperson problem (also traveling salesman problem; TSP). The package provides some simple algorithms and an interface to the Concorde TSP solver and its implementation of the Chained-Lin-Kernighan heuristic. The code for Concorde itself is not included in the package and has to be obtained separately. Hahsler and Hornik (2007) <doi:10.18637/jss.v023.i02>.
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2025-04-22 |
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r-tshrc
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Two-stage procedure compares hazard rate functions, which may or may not cross each other.
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2025-04-22 |
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r-tserieschaos
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Routines for the analysis of nonlinear time series. This work is largely inspired by the TISEAN project, by Rainer Hegger, Holger Kantz and Thomas Schreiber: <http://www.mpipks-dresden.mpg.de/~tisean/>.
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2025-04-22 |
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r-tsentropies
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Computes various entropies of given time series. This is the initial version that includes ApEn() and SampEn() functions for calculating approximate entropy and sample entropy. Approximate entropy was proposed by S.M. Pincus in "Approximate entropy as a measure of system complexity", Proceedings of the National Academy of Sciences of the United States of America, 88, 2297-2301 (March 1991). Sample entropy was proposed by J. S. Richman and J. R. Moorman in "Physiological time-series analysis using approximate entropy and sample entropy", American Journal of Physiology, Heart and Circulatory Physiology, 278, 2039-2049 (June 2000). This package also contains FastApEn() and FastSampEn() functions for calculating fast approximate entropy and fast sample entropy. These are newly designed very fast algorithms, resulting from the modification of the original algorithms. The calculated values of these entropies are not the same as the original ones, but the entropy trend of the analyzed time series determines equally reliably. Their main advantage is their speed, which is up to a thousand times higher. A scientific article describing their properties has been submitted to The Journal of Supercomputing and in present time it is waiting for the acceptance.
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2025-04-22 |
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r-tsdfgs
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We propose an optimality criterion to determine the required training set, r-score, which is derived directly from Pearson's correlation between the genomic estimated breeding values and phenotypic values of the test set <doi:10.1007/s00122-019-03387-0>. This package provides two main functions to determine a good training set and its size.
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2025-04-22 |
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r-tsc
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Performs the two-sample comparisons using the following exact test procedures: the exact likelihood-ratio test (LRT) for equality of two normal populations proposed in Zhang et al. (2012); the combined test based on the LRT and Shapiro-Wilk test for normality via the Bonferroni correction technique; the newly proposed density-based empirical likelihood (DBEL) ratio test. To calculate p-values of the DBEL procedures, three procedures are used: (a) the traditional Monte Carlo (MC) method implemented in C++, (b) a new interpolation method based on regression techniques to operate with tabulated critical values of the test statistic; (c) a Bayesian type method that uses the tabulated critical values as the prior information and MC generated DBEL-test-statistic's values as data.
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2025-04-22 |
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r-trustoptim
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Trust region algorithm for nonlinear optimization. Efficient when the Hessian of the objective function is sparse (i.e., relatively few nonzero cross-partial derivatives). See Braun, M. (2014) <doi:10.18637/jss.v060.i04>.
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2025-04-22 |
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r-truncnorm
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Density, probability, quantile and random number generation functions for the truncated normal distribution.
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2025-04-22 |
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r-truncgof
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Goodness-of-fit tests and some adjusted exploratory tools allowing for left truncated data
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2025-04-22 |
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r-tripack
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A constrained two-dimensional Delaunay triangulation package providing both triangulation and generation of voronoi mosaics of irregular spaced data.
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2025-04-22 |
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r-triosgl
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Fit a trio model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. This package is based on Noah Simon, et al. (2011) <doi:10.1080/10618600.2012.681250>.
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2025-04-22 |
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r-triebeard
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'Radix trees', or 'tries', are key-value data structures optimised for efficient lookups, similar in purpose to hash tables. 'triebeard' provides an implementation of 'radix trees' for use in R programming and in developing packages with 'Rcpp'.
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2025-04-22 |
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r-trialsize
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Functions and Examples in Sample Size Calculation in Clinical Research.
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2025-04-22 |
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r-treeperm
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An implementation of permutation tests in R, supporting both exact and asymptotic K sample test of data locations. The p value of exact tests is found using tree algorithms. Tree algorithms treat permutations of input data as tree nodes and perform constraint depth-first searches for permutations that fall into the critical region of a test systematically. Pruning of tree search and optimisations at C level enable exact tests for certain large data sets.
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2025-04-22 |
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r-treeman
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S4 class and methods for intuitive and efficient phylogenetic tree manipulation.
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2025-04-22 |
