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r-cusumdesign
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Computation of decision intervals (H) and average run lengths (ARL) for CUSUM charts.
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2023-06-16 |
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r-cthresher
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Estimation and inference methods for the continuous threshold expectile regression. It can fit the continuous threshold expectile regression and test the existence of change point, for the paper, "Feipeng Zhang and Qunhua Li (2016). A continuous threshold expectile regression, submitted."
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2023-06-16 |
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r-csn
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Provides functions for computing the density and the log-likelihood function of closed-skew normal variates, and for generating random vectors sampled from this distribution. See Gonzalez-Farias, G., Dominguez-Molina, J., and Gupta, A. (2004). The closed skew normal distribution, Skew-elliptical distributions and their applications: a journey beyond normality, Chapman and Hall/CRC, Boca Raton, FL, pp. 25-42.
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2023-06-16 |
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r-cpca
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This package contains methods to perform Common Principal Component Analysis (CPCA). The stepwise method by Trendafilov is published in the current version. Please see Trendafilov (2010). Stepwise estimation of common principal components. Computational Statistics & Data Analysis, 54(12), 3446-3457. doi:10.1016/j.csda.2010.03.010
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2023-06-16 |
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r-cosmofns
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Package encapsulates standard expressions for distances, times, luminosities, and other quantities useful in observational cosmology, including molecular line observations. Currently coded for a flat universe only.
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2023-06-16 |
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r-corrsieve
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Statistical summary of Structure output.
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2023-06-16 |
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r-correlplot
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Correlplot contains diverse routines for the construction of different plots for representing correlation matrices.
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2023-06-16 |
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r-bigtime
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Estimation of large Vector AutoRegressive (VAR), Vector AutoRegressive with Exogenous Variables X (VARX) and Vector AutoRegressive Moving Average (VARMA) Models with Structured Lasso Penalties, see Nicholson, Bien and Matteson (2017) <arXiv:1412.5250v2> and Wilms, Basu, Bien and Matteson (2017) <arXiv:1707.09208>.
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2023-06-16 |
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r-corclass
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Perform a correlational class analysis of the data, resulting in a partition of the data into separate modules.
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2023-06-16 |
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r-coppecosenzar
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The program implements the COPPE-Cosenza Fuzzy Hierarchy Model. The model was based on the evaluation of local alternatives, representing regional potentialities, so as to fulfill demands of economic projects. After defining demand profiles in terms of their technological coefficients, the degree of importance of factors is defined so as to represent the productive activity. The method can detect a surplus of supply without the restriction of the distance of classical algebra, defining a hierarchy of location alternatives. In COPPE-Cosenza Model, the distance between factors is measured in terms of the difference between grades of memberships of the same factors belonging to two or more sets under comparison. The required factors are classified under the following linguistic variables: Critical (CR); Conditioning (C); Little Conditioning (LC); and Irrelevant (I). And the alternatives can assume the following linguistic variables: Excellent (Ex), Good (G), Regular (R), Weak (W), Empty (Em), Zero (Z) and Inexistent (In). The model also provides flexibility, allowing different aggregation rules to be performed and defined by the Decision Maker. Such feature is considered in this package, allowing the user to define other aggregation matrices, since it considers the same linguistic variables mentioned.
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2023-06-16 |
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r-confreq
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Offers several functions for Configural Frequencies Analysis (CFA), which is a useful statistical tool for the analysis of multiway contingency tables. CFA was introduced by G. A. Lienert as 'Konfigurations Frequenz Analyse - KFA'. Lienert, G. A. (1971). Die Konfigurationsfrequenzanalyse: I. Ein neuer Weg zu Typen und Syndromen. Zeitschrift für Klinische Psychologie und Psychotherapie, 19(2), 99–115.
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2023-06-16 |
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r-condvis
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Exploring fitted models by interactively taking 2-D and 3-D sections in data space.
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2023-06-16 |
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r-concatenate
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Simple functions for joining strings. Construct human-friendly messages whose elements aren't known in advance, like in stop, warning, or message, from clean code.
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2023-06-16 |
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r-comprehenr
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Provides 'Python'-style list comprehensions. List comprehension expressions use usual loops (for(), while() and repeat()) and usual if() as list producers. In many cases it gives more concise notation than standard "*apply + filter" strategy.
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2023-06-16 |
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r-covrobust
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The cov.nnve() function implements robust covariance estimation by the nearest neighbor variance estimation (NNVE) method of Wang and Raftery (2002) <DOI:10.1198/016214502388618780>.
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2023-06-16 |
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r-countgmifs
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Provides a function for fitting Poisson and negative binomial regression models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method.
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2023-06-16 |
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r-couchdb
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Interface to the couchDB document database <http://couchdb.apache.org>.
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2023-06-16 |
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r-cortools
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Designed for analysis of the results of a Genome Wide Association Study. Includes tools to pull lists of Chromosome number and SNP position below a certain significance threshold, refine gene networks (including data I/O for Cytoscape), and check SNP base pair changes.
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2023-06-16 |
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r-corrplot
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A graphical display of a correlation matrix or general matrix. It also contains some algorithms to do matrix reordering. In addition, corrplot is good at details, including choosing color, text labels, color labels, layout, etc.
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2023-06-16 |
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r-corpcor
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Implements a James-Stein-type shrinkage estimator for the covariance matrix, with separate shrinkage for variances and correlations. The details of the method are explained in Schafer and Strimmer (2005) <DOI:10.2202/1544-6115.1175> and Opgen-Rhein and Strimmer (2007) <DOI:10.2202/1544-6115.1252>. The approach is both computationally as well as statistically very efficient, it is applicable to "small n, large p" data, and always returns a positive definite and well-conditioned covariance matrix. In addition to inferring the covariance matrix the package also provides shrinkage estimators for partial correlations and partial variances. The inverse of the covariance and correlation matrix can be efficiently computed, as well as any arbitrary power of the shrinkage correlation matrix. Furthermore, functions are available for fast singular value decomposition, for computing the pseudoinverse, and for checking the rank and positive definiteness of a matrix.
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2023-06-16 |
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r-container
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Common container data structures deque, set and dict (resembling 'Python's dict type) with typical member functions to insert, delete and access container elements. Provides iterators and reference semantics.
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2023-06-16 |
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r-compglm
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A function (which uses a similar interface to the `glm' function) for the fitting of a Conway-Maxwell-Poisson GLM. There are also various methods for analysis of the model fit. The package also contains functions for the Conway-Maxwell-Poisson distribution in a similar interface to functions `dpois', `ppois' and `rpois'. The functions are generally quick, since the workhorse functions are written in C++ (thanks to the Rcpp package).
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2023-06-16 |
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r-covtestr
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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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2023-06-16 |
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r-corpus
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Text corpus data analysis, with full support for international text (Unicode). Functions for reading data from newline-delimited 'JSON' files, for normalizing and tokenizing text, for searching for term occurrences, and for computing term occurrence frequencies, including n-grams.
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2023-06-16 |
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r-comphclust
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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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2023-06-16 |
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r-deal
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Bayesian networks with continuous and/or discrete variables can be learned and compared from data. The method is described in Boettcher and Dethlefsen (2003), <doi:10.18637/jss.v008.i20>.
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2023-06-16 |
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r-date
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Functions for handling dates.
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2023-06-16 |
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r-cssam
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Cell-type specific differential expression of a microarray experiment of heterogeneous tissue samples, using SAM.
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2023-06-16 |
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r-creditmetrics
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A set of functions for computing the CreditMetrics risk model
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2023-06-16 |
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r-cpt
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Non-parametric test for equality of multivariate distributions. Trains a classifier to classify (multivariate) observations as coming from one of several distributions. If the classifier is able to classify the observations better than would be expected by chance (using permutation inference), then the null hypothesis that the distributions are equal is rejected.
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2023-06-16 |
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r-cmpprocess
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A toolkit for flexible modeling of count processes where data (over- or under-) dispersion exists. Estimations can be obtained under two data constructs where one has: (1) data on number of events in an s-unit time interval, or (2) only wait-time data. This package is supplementary to the work set forth in Zhu et al. (2016) <doi:10.1080/00031305.2016.1234976>.
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2023-06-16 |
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r-crskdiag
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Provides the implementation of analytical and graphical approaches for checking the assumptions of the Fine and Gray model.
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2023-06-16 |
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r-counthmm
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Provides tools for penalized estimation of flexible hidden Markov models for time series of counts w/o the need to specify a (parametric) family of distributions. These include functions for model fitting, model checking, and state decoding. For details, see Adam, T., Langrock, R., and Weiß, C.H. (2019): Penalized Estimation of Flexible Hidden Markov Models for Time Series of Counts. <arXiv:1901.03275>.
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2023-06-16 |
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r-configparser
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Enhances the 'ini' package by adding the ability to interpolate variables. The INI configuration file is read into an R6 ConfigParser object (loosely inspired by Pythons ConfigParser module) and the keys can be read, where '%(....)s' instances are interpolated by other included options or outside variables.
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2023-06-16 |
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r-compute.es
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This package contains several functions for calculating the most widely used effect sizes (ES), along with their variances, confidence intervals and p-values. The output includes ES's of d (mean difference), g (unbiased estimate of d), r (correlation coefficient), z' (Fisher's z), and OR (odds ratio and log odds ratio). In addition, NNT (number needed to treat), U3, CLES (Common Language Effect Size) and Cliff's Delta are computed. This package uses recommended formulas as described in The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009).
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2023-06-16 |
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r-covafillr
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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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2023-06-16 |
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r-costsensitive
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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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2023-06-16 |
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r-comparetests
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A standard test is observed on all specimens. We treat the second test (or sampled test) as being conducted on only a stratified sample of specimens. Verification Bias is this situation when the specimens for doing the second (sampled) test is not under investigator control. We treat the total sample as stratified two-phase sampling and use inverse probability weighting. We estimate diagnostic accuracy (category-specific classification probabilities; for binary tests reduces to specificity and sensitivity, and also predictive values) and agreement statistics (percent agreement, percent agreement by category, Kappa (unweighted), Kappa (quadratic weighted) and symmetry tests (reduces to McNemar's test for binary tests)). See: Katki HA, Li Y, Edelstein DW, Castle PE. Estimating the agreement and diagnostic accuracy of two diagnostic tests when one test is conducted on only a subsample of specimens. Stat Med. 2012 Feb 28; 31(5) <doi:10.1002/sim.4422>.
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2023-06-16 |
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r-cold
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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; M. Helena Goncalves et al.(2007) <doi:10.1016/j.csda.2007.03.002>.
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2023-06-16 |
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r-cseqpat
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Mines contiguous sequential patterns in text.
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2023-06-16 |
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r-crp.csfp
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Modelling credit risks based on the concept of "CreditRisk+", First Boston Financial Products, 1997 and "CreditRisk+ in the Banking Industry", Gundlach & Lehrbass, Springer, 2003.
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2023-06-16 |
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r-cplots
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Provides functions to produce some circular plots for circular data, in a height- or area-proportional manner. They include barplots, smooth density plots, stacked dot plots, histograms, multi-class stacked smooth density plots, and multi-class stacked histograms. The new methodology for general area-proportional circular visualization is described in an article submitted (after revision) to Journal of Computational and Graphical Statistics.
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2023-06-16 |
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r-cts
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Functions to fit continuous time autoregressive models with the Kalman filter (Wang (2013) <doi:10.18637/jss.v053.i05>).
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2023-06-16 |
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r-crfsuite
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Wraps the 'CRFsuite' library <https://github.com/chokkan/crfsuite> allowing users to fit a Conditional Random Field model and to apply it on existing data. The focus of the implementation is in the area of Natural Language Processing where this R package allows you to easily build and apply models for named entity recognition, text chunking, part of speech tagging, intent recognition or classification of any category you have in mind. Next to training, a small web application is included in the package to allow you to easily construct training data.
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2023-06-16 |
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r-coroica
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Contains an implementation of a confounding robust independent component analysis (ICA) for noisy and grouped data. The main function coroICA() performs a blind source separation, by maximizing an independence across sources and allows to adjust for varying confounding based on user-specified groups. Additionally, the package contains the function uwedge() which can be used to approximately jointly diagonalize a list of matrices. For more details see the project website <https://sweichwald.de/coroICA/>.
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2023-06-16 |
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r-compound.cox
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Univariate feature selection and compound covariate methods under the Cox model with high-dimensional features (e.g., gene expressions). Available are survival data for non-small-cell lung cancer patients with gene expressions (Chen et al 2007 New Engl J Med) <DOI:10.1056/NEJMoa060096>, statistical methods in Emura et al (2012 PLoS ONE) <DOI:10.1371/journal.pone.0047627>, Emura & Chen (2016 Stat Methods Med Res) <DOI:10.1177/0962280214533378>, and Emura et al. (2019)<DOI:10.1016/j.cmpb.2018.10.020>. Algorithms for generating correlated gene expressions are also available.
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2023-06-16 |
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r-corrcoverage
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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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2023-06-16 |
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r-crwrm
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To re-calculate the coefficients and the standard deviation when changing the reference group.
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2023-06-16 |
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r-crso
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An algorithm for identifying candidate driver combinations in cancer. CRSO is based on a theoretical model of cancer in which a cancer rule is defined to be a collection of two or more events (i.e., alterations) that are minimally sufficient to cause cancer. A cancer rule set is a set of cancer rules that collectively are assumed to account for all of ways to cause cancer in the population. In CRSO every event is designated explicitly as a passenger or driver within each patient. Each event is associated with a patient-specific, event-specific passenger penalty, reflecting how unlikely the event would have happened by chance, i.e., as a passenger. CRSO evaluates each rule set by assigning all samples to a rule in the rule set, or to the null rule, and then calculating the total statistical penalty from all unassigned event. CRSO uses a three phase procedure find the best rule set of fixed size K for a range of Ks. A core rule set is then identified from among the best rule sets of size K as the rule set that best balances rule set size and statistical penalty. Users should consult the 'crso' vignette for an example walk through of a full CRSO run. The full description, of the CRSO algorithm is presented in: Klein MI, Cannataro V, Townsend J, Stern DF and Zhao H. "Identifying combinations of cancer driver in individual patients." BioRxiv 674234 [Preprint]. June 19, 2019. <doi:10.1101/674234>. Please cite this article if you use 'crso'.
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2023-06-16 |
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r-crov
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Fits a constrained regression model for an ordinal response with ordinal predictors and possibly others, Espinosa and Hennig (2018) <arXiv:1804.08715>. The parameter estimates associated with an ordinal predictor are constrained to be monotonic. If a monotonicity direction (isotonic or antitonic) is not specified for an ordinal predictor by the user, then the monotonicity direction classification procedure establishes it. A monotonicity test is also available to test the null hypothesis of monotonicity over a set of parameters associated with an ordinal predictor.
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2023-06-16 |
