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r-coxphf
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public |
Implements Firth's penalized maximum likelihood bias reduction method for Cox regression which has been shown to provide a solution in case of monotone likelihood (nonconvergence of likelihood function), see Heinze and Schemper (2001) and Heinze and Dunkler (2008). The program fits profile penalized likelihood confidence intervals which were proved to outperform Wald confidence intervals.
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2024-01-16 |
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r-covtestr
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public |
Testing functions for Covariance Matrices. These tests include high-dimension homogeneity of covariance matrix testing described by Schott (2007) <doi:10.1016/j.csda.2007.03.004> and high-dimensional one-sample tests of covariance matrix structure described by Fisher, et al. (2010) <doi:10.1016/j.jmva.2010.07.004>. Covariance matrix tests use C++ to speed performance and allow larger data sets.
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2024-01-16 |
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r-conquer
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public |
Estimation and inference for conditional linear quantile regression models using a convolution smoothed approach. In the low-dimensional setting, efficient gradient-based methods are employed for fitting both a single model and a regression process over a quantile range. Normal-based and (multiplier) bootstrap confidence intervals for all slope coefficients are constructed. In high dimensions, the conquer method is complemented with flexible types of penalties (Lasso, elastic-net, group lasso, sparse group lasso, scad and mcp) to deal with complex low-dimensional structures.
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2024-01-16 |
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r-covr
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public |
Track and report code coverage for your package and (optionally) upload the results to a coverage service like 'Codecov' <https://codecov.io> or 'Coveralls' <https://coveralls.io>. Code coverage is a measure of the amount of code being exercised by a set of tests. It is an indirect measure of test quality and completeness. This package is compatible with any testing methodology or framework and tracks coverage of both R code and compiled C/C++/FORTRAN code.
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2024-01-16 |
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r-covbm
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public |
Allows Brownian motion, fractional Brownian motion, and integrated Ornstein-Uhlenbeck process components to be added to linear and non-linear mixed effects models using the structures and methods of the 'nlme' package.
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2024-01-16 |
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r-corrcoverage
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public |
Using a computationally efficient method, the package can be used to find the corrected coverage estimate of a credible set of putative causal variants from Bayesian genetic fine-mapping. The package can also be used to obtain a corrected credible set if required; that is, the smallest set of variants required such that the corrected coverage estimate of the resultant credible set is within some user defined accuracy of the desired coverage. Maller et al. (2012) <doi:10.1038/ng.2435>, Wakefield (2009) <doi:10.1002/gepi.20359>, Fortune and Wallace (2018) <doi:10.1093/bioinformatics/bty898>.
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2024-01-16 |
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r-covafillr
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public |
Facilitates local polynomial regression for state dependent covariates in state-space models. The functionality can also be used from 'C++' based model builder tools such as 'Rcpp'/'inline', 'TMB', or 'JAGS'.
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2024-01-16 |
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r-costsensitive
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public |
Reduction-based techniques for cost-sensitive multi-class classification, in which each observation has a different cost for classifying it into one class, and the goal is to predict the class with the minimum expected cost for each new observation. Implements Weighted All-Pairs (Beygelzimer, A., Langford, J., & Zadrozny, B., 2008, <doi:10.1007/978-0-387-79361-0_1>), Weighted One-Vs-Rest (Beygelzimer, A., Dani, V., Hayes, T., Langford, J., & Zadrozny, B., 2005, <https://dl.acm.org/citation.cfm?id=1102358>) and Regression One-Vs-Rest. Works with arbitrary classifiers taking observation weights, or with regressors. Also implements cost-proportionate rejection sampling for working with classifiers that don't accept observation weights.
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2024-01-16 |
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r-corelearn
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public |
A suite of machine learning algorithms written in C++ with the R interface contains several learning techniques for classification and regression. Predictive models include e.g., classification and regression trees with optional constructive induction and models in the leaves, random forests, kNN, naive Bayes, and locally weighted regression. All predictions obtained with these models can be explained and visualized with the 'ExplainPrediction' package. This package is especially strong in feature evaluation where it contains several variants of Relief algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, and DKM. These methods can be used for feature selection or discretization of numeric attributes. The OrdEval algorithm and its visualization is used for evaluation of data sets with ordinal features and class, enabling analysis according to the Kano model of customer satisfaction. Several algorithms support parallel multithreaded execution via OpenMP. The top-level documentation is reachable through ?CORElearn.
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2024-01-16 |
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r-coranking
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public |
Calculates the co-ranking matrix to assess the quality of a dimensionality reduction.
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2024-01-16 |
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r-cord
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public |
Partition data points (variables) into communities/clusters, similar to clustering algorithms, such as k-means and hierarchical clustering. This package implements a clustering algorithm based on a new metric CORD, defined for high dimensional parametric or semi-parametric distributions. Read http://arxiv.org/abs/1508.01939 for more details.
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2024-01-16 |
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r-compositions
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public |
Provides functions for the consistent analysis of compositional data (e.g. portions of substances) and positive numbers (e.g. concentrations) in the way proposed by J. Aitchison and V. Pawlowsky-Glahn.
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2024-01-16 |
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r-copula
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None |
Classes (S4) of commonly used elliptical, Archimedean, extreme-value and other copula families, as well as their rotations, mixtures and asymmetrizations. Nested Archimedean copulas, related tools and special functions. Methods for density, distribution, random number generation, bivariate dependence measures, Rosenblatt transform, Kendall distribution function, perspective and contour plots. Fitting of copula models with potentially partly fixed parameters, including standard errors. Serial independence tests, copula specification tests (independence, exchangeability, radial symmetry, extreme-value dependence, goodness-of-fit) and model selection based on cross-validation. Empirical copula, smoothed versions, and non-parametric estimators of the Pickands dependence function.
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2024-01-16 |
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r-coop
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public |
Fast implementations of the co-operations: covariance, correlation, and cosine similarity. The implementations are fast and memory-efficient and their use is resolved automatically based on the input data, handled by R's S3 methods. Full descriptions of the algorithms and benchmarks are available in the package vignettes.
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2024-01-16 |
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r-conquestr
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public |
Extends 'ACER ConQuest' through a family of functions designed to improve graphical outputs and help with advanced analysis (e.g., differential item functioning). Allows R users to call 'ACER ConQuest' from within R and read 'ACER ConQuest' System Files (generated by the command `put` <https://conquestmanual.acer.org/s4-00.html#put>). Requires 'ACER ConQuest' version 5.29.5 or later. A demonstration version can be downloaded from <https://shop.acer.org/acer-conquest-5.html>.
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2024-01-16 |
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r-contfrac
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public |
Various utilities for evaluating continued fractions.
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2024-01-16 |
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r-coneproj
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public |
Routines doing cone projection and quadratic programming, as well as doing estimation and inference for constrained parametric regression and shape-restricted regression problems. See Mary C. Meyer (2013)<doi:10.1080/03610918.2012.659820> for more details.
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2024-01-16 |
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r-concreg
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public |
Implements concordance regression which can be used to estimate generalized odds of concordance. Can be used for non- and semi-parametric survival analysis with non-proportional hazards, for binary and for continuous outcome data. The method was introduced by Dunkler, Schemper and Heinze (2010) <doi:10.1093/bioinformatics/btq035>.
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2024-01-16 |
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r-conconpiwifun
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public |
Continuous convex piecewise linear (ccpl) resp. quadratic (ccpq) functions can be implemented with sorted breakpoints and slopes. This includes functions that are ccpl (resp. ccpq) on a convex set (i.e. an interval or a point) and infinite out of the domain. These functions can be very useful for a large class of optimisation problems. Efficient manipulation (such as log(N) insertion) of such data structure is obtained with map standard template library of C++ (that hides balanced trees). This package is a wrapper on such a class based on Rcpp modules.
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2024-01-16 |
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r-compas
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public |
Manipulate and analyze 3-D structural geometry of Protein Data Bank (PDB) files.
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2024-01-16 |
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r-compquadform
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public |
Computes the distribution function of quadratic forms in normal variables using Imhof's method, Davies's algorithm, Farebrother's algorithm or Liu et al.'s algorithm.
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2024-01-16 |
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r-compoissonreg
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public |
Fit Conway-Maxwell Poisson (COM-Poisson or CMP) regression models to count data (Sellers & Shmueli, 2010) <doi:10.1214/09-AOAS306>. The package provides functions for model estimation, dispersion testing, and diagnostics. Zero-inflated CMP regression (Sellers & Raim, 2016) <doi:10.1016/j.csda.2016.01.007> is also supported.
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2024-01-16 |
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r-comphclust
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public |
Performs the complementary hierarchical clustering procedure and returns X' (the expected residual matrix) and a vector of the relative gene importances.
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2024-01-16 |
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r-cmfrec
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public |
Collective matrix factorization (a.k.a. multi-view or multi-way factorization, Singh, Gordon, (2008) <doi:10.1145/1401890.1401969>) tries to approximate a (potentially very sparse or having many missing values) matrix 'X' as the product of two low-dimensional matrices, optionally aided with secondary information matrices about rows and/or columns of 'X', which are also factorized using the same latent components. The intended usage is for recommender systems, dimensionality reduction, and missing value imputation. Implements extensions of the original model (Cortes, (2018) <arXiv:1809.00366>) and can produce different factorizations such as the weighted 'implicit-feedback' model (Hu, Koren, Volinsky, (2008) <doi:10.1109/ICDM.2008.22>), the 'weighted-lambda-regularization' model, (Zhou, Wilkinson, Schreiber, Pan, (2008) <doi:10.1007/978-3-540-68880-8_32>), or the enhanced model with 'implicit features' (Rendle, Zhang, Koren, (2019) <arXiv:1905.01395>), with or without side information. Can use gradient-based procedures or alternating-least squares procedures (Koren, Bell, Volinsky, (2009) <doi:10.1109/MC.2009.263>), with either a Cholesky solver, a faster conjugate gradient solver (Takacs, Pilaszy, Tikk, (2011) <doi:10.1145/2043932.2043987>), or a non-negative coordinate descent solver (Franc, Hlavac, Navara, (2005) <doi:10.1007/11556121_50>), providing efficient methods for sparse and dense data, and mixtures thereof. Supports L1 and L2 regularization in the main models, offers alternative most-popular and content-based models, and implements functionality for cold-start recommendations and imputation of 2D data.
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2024-01-16 |
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r-comparec
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public |
Proposed by Harrell, the C index or concordance C, is considered an overall measure of discrimination in survival analysis between a survival outcome that is possibly right censored and a predictive-score variable, which can represent a measured biomarker or a composite-score output from an algorithm that combines multiple biomarkers. This package aims to statistically compare two C indices with right-censored survival outcome, which commonly arise from a paired design and thus resulting two correlated C indices.
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2024-01-16 |
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r-colorspace
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None |
Carries out mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB, and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided along with corresponding ggplot2 color scales. Color palette choice is aided by an interactive app (with either a Tcl/Tk or a shiny graphical user interface) and shiny apps with an HCL color picker and a color vision deficiency emulator. Plotting functions for displaying and assessing palettes include color swatches, visualizations of the HCL space, and trajectories in HCL and/or RGB spectrum. Color manipulation functions include: desaturation, lightening/darkening, mixing, and simulation of color vision deficiencies (deutanomaly, protanomaly, tritanomaly). Details can be found on the project web page at <https://colorspace.R-Forge.R-project.org/> and in the accompanying scientific paper: Zeileis et al. (2020, Journal of Statistical Software, <doi:10.18637/jss.v096.i01>).
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2024-01-16 |
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r-commonmark
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public |
The CommonMark specification defines a rationalized version of markdown syntax. This package uses the 'cmark' reference implementation for converting markdown text into various formats including html, latex and groff man. In addition it exposes the markdown parse tree in xml format. Also includes opt-in support for GFM extensions including tables, autolinks, and strikethrough text.
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2024-01-16 |
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r-colourvalues
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public |
Maps one of the viridis colour palettes, or a user-specified palette to values. Viridis colour maps are created by Stéfan van der Walt and Nathaniel Smith, and were set as the default palette for the 'Python' 'Matplotlib' library <https://matplotlib.org/>. Other palettes available in this library have been derived from 'RColorBrewer' <https://CRAN.R-project.org/package=RColorBrewer> and 'colorspace' <https://CRAN.R-project.org/package=colorspace> packages.
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2024-01-16 |
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r-comat
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public |
Builds co-occurrence matrices based on spatial raster data. It includes creation of weighted co-occurrence matrices (wecoma) and integrated co-occurrence matrices (incoma; Vadivel et al. (2007) <doi:10.1016/j.patrec.2007.01.004>).
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2024-01-16 |
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r-collutils
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public |
Provides some low level functions for processing PLINK input and output files.
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2024-01-16 |
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r-collections
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public |
Provides high performance container data types such as queues, stacks, deques, dicts and ordered dicts. Benchmarks <https://randy3k.github.io/collections/articles/benchmark.html> have shown that these containers are asymptotically more efficient than those offered by other packages.
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2024-01-16 |
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r-cold
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public |
Performs regression analysis for longitudinal count data, allowing for serial dependence among observations from a given individual and two dimensional random effects on the linear predictor. Estimation is via maximization of the exact likelihood of a suitably defined model. Missing values and unbalanced data are allowed. Details can be found in the accompanying scientific papers: Goncalves & Cabral (2021, Journal of Statistical Software, <doi:10.18637/jss.v099.i03>) and Goncalves et al. (2007, Computational Statistics & Data Analysis, <doi:10.1016/j.csda.2007.03.002>).
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2024-01-16 |
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r-clusterr
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public |
Gaussian mixture models, k-means, mini-batch-kmeans, k-medoids and affinity propagation clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of 'RcppArmadillo' to speed up the computationally intensive parts of the functions. For more information, see (i) "Clustering in an Object-Oriented Environment" by Anja Struyf, Mia Hubert, Peter Rousseeuw (1997), Journal of Statistical Software, <doi:10.18637/jss.v001.i04>; (ii) "Web-scale k-means clustering" by D. Sculley (2010), ACM Digital Library, <doi:10.1145/1772690.1772862>; (iii) "Armadillo: a template-based C++ library for linear algebra" by Sanderson et al (2016), The Journal of Open Source Software, <doi:10.21105/joss.00026>; (iv) "Clustering by Passing Messages Between Data Points" by Brendan J. Frey and Delbert Dueck, Science 16 Feb 2007: Vol. 315, Issue 5814, pp. 972-976, <doi:10.1126/science.1136800>.
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2024-01-16 |
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r-coin
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None |
Conditional inference procedures for the general independence problem including two-sample, K-sample (non-parametric ANOVA), correlation, censored, ordered and multivariate problems described in <doi:10.18637/jss.v028.i08>.
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2024-01-16 |
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r-coenoflex
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public |
Simulates the composition of samples of vegetation according to gradient-based vegetation theory. Features a flexible algorithm incorporating competition and complex multi-gradient interaction.
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2024-01-16 |
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r-clustersim
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public |
Distance measures (GDM1, GDM2, Sokal-Michener, Bray-Curtis, for symbolic interval-valued data), cluster quality indices (Calinski-Harabasz, Baker-Hubert, Hubert-Levine, Silhouette, Krzanowski-Lai, Hartigan, Gap, Davies-Bouldin), data normalization formulas (metric data, interval-valued symbolic data), data generation (typical and non-typical data), HINoV method, replication analysis, linear ordering methods, spectral clustering, agreement indices between two partitions, plot functions (for categorical and symbolic interval-valued data). (MILLIGAN, G.W., COOPER, M.C. (1985) <doi:10.1007/BF02294245>, HUBERT, L., ARABIE, P. (1985) <doi:10.1007%2FBF01908075>, RAND, W.M. (1971) <doi:10.1080/01621459.1971.10482356>, JAJUGA, K., WALESIAK, M. (2000) <doi:10.1007/978-3-642-57280-7_11>, MILLIGAN, G.W., COOPER, M.C. (1988) <doi:10.1007/BF01897163>, JAJUGA, K., WALESIAK, M., BAK, A. (2003) <doi:10.1007/978-3-642-55721-7_12>, DAVIES, D.L., BOULDIN, D.W. (1979) <doi:10.1109/TPAMI.1979.4766909>, CALINSKI, T., HARABASZ, J. (1974) <doi:10.1080/03610927408827101>, HUBERT, L. (1974) <doi:10.1080/01621459.1974.10480191>, TIBSHIRANI, R., WALTHER, G., HASTIE, T. (2001) <doi:10.1111/1467-9868.00293>, BRECKENRIDGE, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>, WALESIAK, M., DUDEK, A. (2008) <doi:10.1007/978-3-540-78246-9_11>).
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2024-01-16 |
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r-codep
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public |
Computation of Multiscale Codependence Analysis and spatial eigenvector maps, as an additional feature.
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2024-01-16 |
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r-coda.base
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public |
A minimum set of functions to perform compositional data analysis using the log-ratio approach introduced by John Aitchison (1982). Main functions have been implemented in c++ for better performance.
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2024-01-16 |
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r-cobs
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public |
Qualitatively Constrained (Regression) Smoothing Splines via Linear Programming and Sparse Matrices.
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2024-01-16 |
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r-cna
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public |
Provides comprehensive functionalities for causal modeling with Coincidence Analysis (CNA), which is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009) <doi:10.1177/0049124109339369>, and generalized in Baumgartner & Ambuehl (2018) <doi:10.1017/psrm.2018.45>. CNA is designed to recover INUS-causation from data, which is particularly relevant for analyzing processes featuring conjunctural causation (component causation) and equifinality (alternative causation). CNA is currently the only method for INUS-discovery that allows for multiple effects (outcomes/endogenous factors), meaning it can analyze common-cause and causal chain structures.
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2024-01-16 |
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r-cmprskqr
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public |
Estimation, testing and regression modeling of subdistribution functions in competing risks using quantile regressions, as described in Peng and Fine (2009) <DOI:10.1198/jasa.2009.tm08228>.
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2024-01-16 |
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r-cmprsk
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public |
Estimation, testing and regression modeling of subdistribution functions in competing risks, as described in Gray (1988), A class of K-sample tests for comparing the cumulative incidence of a competing risk, Ann. Stat. 16:1141-1154 <DOI:10.1214/aos/1176350951>, and Fine JP and Gray RJ (1999), A proportional hazards model for the subdistribution of a competing risk, JASA, 94:496-509, <DOI:10.1080/01621459.1999.10474144>.
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2024-01-16 |
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r-clock
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public |
Provides a comprehensive library for date-time manipulations using a new family of orthogonal date-time classes (durations, time points, zoned-times, and calendars) that partition responsibilities so that the complexities of time zones are only considered when they are really needed. Capabilities include: date-time parsing, formatting, arithmetic, extraction and updating of components, and rounding.
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2024-01-16 |
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r-cmf
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public |
Collective matrix factorization (CMF) finds joint low-rank representations for a collection of matrices with shared row or column entities. This code learns a variational Bayesian approximation for CMF, supporting multiple likelihood potentials and missing data, while identifying both factors shared by multiple matrices and factors private for each matrix. For further details on the method see Klami et al. (2014) <arXiv:1312.5921>. The package can also be used to learn Bayesian canonical correlation analysis (CCA) and group factor analysis (GFA) models, both of which are special cases of CMF. This is likely to be useful for people looking for CCA and GFA solutions supporting missing data and non-Gaussian likelihoods. See Klami et al. (2013) <https://research.cs.aalto.fi/pml/online-papers/klami13a.pdf> and Virtanen et al. (2012) <http://proceedings.mlr.press/v22/virtanen12.html> for details on Bayesian CCA and GFA, respectively.
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2024-01-16 |
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r-clv
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public |
Package contains most of the popular internal and external cluster validation methods ready to use for the most of the outputs produced by functions coming from package "cluster". Package contains also functions and examples of usage for cluster stability approach that might be applied to algorithms implemented in "cluster" package as well as user defined clustering algorithms.
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2024-01-16 |
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r-clustvarlv
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public |
Functions for the clustering of variables around Latent Variables, for 2-way or 3-way data. Each cluster of variables, which may be defined as a local or directional cluster, is associated with a latent variable. External variables measured on the same observations or/and additional information on the variables can be taken into account. A "noise" cluster or sparse latent variables can also be defined.
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2024-01-16 |
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r-cluster
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None |
Methods for Cluster analysis. Much extended the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".
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2024-01-16 |
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r-clustering.sc.dp
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public |
A dynamic programming algorithm for optimal clustering multidimensional data with sequential constraint. The algorithm minimizes the sum of squares of within-cluster distances. The sequential constraint allows only subsequent items of the input data to form a cluster. The sequential constraint is typically required in clustering data streams or items with time stamps such as video frames, GPS signals of a vehicle, movement data of a person, e-pen data, etc. The algorithm represents an extension of 'Ckmeans.1d.dp' to multiple dimensional spaces. Similarly to the one-dimensional case, the algorithm guarantees optimality and repeatability of clustering. Method clustering.sc.dp() can find the optimal clustering if the number of clusters is known. Otherwise, methods findwithinss.sc.dp() and backtracking.sc.dp() can be used. See Szkaliczki, T. (2016) "clustering.sc.dp: Optimal Clustering with Sequential Constraint by Using Dynamic Programming" <doi: 10.32614/RJ-2016-022> for more information.
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2024-01-16 |
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r-clusrank
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public |
Non-parametric tests (Wilcoxon rank sum test and Wilcoxon signed rank test) for clustered data documented in Jiang et. al (2020) <doi:10.18637/jss.v096.i06>.
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2024-01-16 |
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r-clue
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public |
CLUster Ensembles.
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2024-01-16 |
