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r-ccpop
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Tests of association between SNPs or pairs of SNPs and binary phenotypes, in case-control / case-population / case-control-population studies.
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2025-04-22 |
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r-ccp
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Significance tests for canonical correlation analysis, including asymptotic tests and a Monte Carlo method
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2025-04-22 |
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r-ccmm
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Estimate the direct and indirect (mediation) effects of treatment on the outcome when intermediate variables (mediators) are compositional and high-dimensional. Sohn, M.B. and Li, H. (2017). Compositional Mediation Analysis for Microbiome Studies. (AOAS: In revision).
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2025-04-22 |
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r-ccm
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Classification method described in Dancik et al (2011) <doi:10.1158/0008-5472.CAN-11-2427> that classifies a sample according to the class with the maximum mean (or any other function of) correlation between the test and training samples with known classes.
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2025-04-22 |
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r-cchs
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Contains a function, also called 'cchs', that calculates Estimator III of Borgan et al (2000), <DOI:10.1023/A:1009661900674>. This estimator is for fitting a Cox proportional hazards model to data from a case-cohort study where the subcohort was selected by stratified simple random sampling.
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2025-04-22 |
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r-ccda
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This package implements the combined cluster and discriminant analysis method for finding homogeneous groups of data with known origin as described in Kovacs et. al (2014): Classification into homogeneous groups using combined cluster and discriminant analysis (CCDA). Environmental Modelling & Software. DOI: http://dx.doi.org/10.1016/j.envsoft.2014.01.010
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2025-04-22 |
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r-cccrm
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Estimates the Concordance Correlation Coefficient to assess agreement. The scenarios considered are non-repeated measures, non-longitudinal repeated measures (replicates) and longitudinal repeated measures. The estimation approaches implemented are variance components and U-statistics approaches.
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2025-04-22 |
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r-ccchooser
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ccChooser can be used to developing and evaluation of core collections for germplasm collections (entire collection). This package used to develop a core collection for biological resources like genbanks. A core collection is defined as a sample of accessions that represent, with the lowest possible level of redundancy, the genetic diversity (the richness of gene or genotype categories) of the entire collection. The establishing a core collection that represents genetic diversity of the entire collection with minimum loss of its original diversity and minimum redundancies is an important problem for gene-banks curators and crop breeders. ccChooser establish core collection base on phenotypic data (agronomic, morphological, phenological).
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2025-04-22 |
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r-ccagfa
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Variational Bayesian algorithms for learning canonical correlation analysis (CCA), inter-battery factor analysis (IBFA), and group factor analysis (GFA). Inference with several random initializations can be run with the functions CCAexperiment() and GFAexperiment().
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2025-04-22 |
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r-cc
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Tools for creating and visualizing statistical process control charts. Control charts are used for monitoring measurement processes, such as those occurring in manufacturing. The objective is to monitor the history of such processes and flag outlying measurements: out-of-control signals. Montgomery, D. (2009, ISBN:978-0-470-16992-6) contains an extensive discussion of the methodology.
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2025-04-22 |
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r-cbt
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The Confidence Bound Target (CBT) algorithm is designed for infinite arms bandit problem. It is shown that CBT algorithm achieves the regret lower bound for general reward distributions. Reference: Hock Peng Chan and Shouri Hu (2018) <arXiv:1805.11793>.
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2025-04-22 |
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r-cbsodatar
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The data and meta data from Statistics Netherlands (<https://www.cbs.nl>) can be browsed and downloaded. The client uses the open data API of Statistics Netherlands.
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2025-04-22 |
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r-cbsem
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The composites are linear combinations of their indicators in composite based structural equation models. Structural models are considered consisting of two blocks. The indicators of the exogenous composites are named by X, the indicators of the endogenous by Y. Reflective relations are given by arrows pointing from the composite to their indicators. Their values are called loadings. In a reflective-reflective scenario all indicators have loadings. Arrows are pointing to their indicators only from the endogenous composites in the formative-reflective scenario. There are no loadings at all in the formative-formative scenario. The covariance matrices are computed for these three scenarios. They can be used to simulate these models. These models can also be estimated and a segmentation procedure is included as well.
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2025-04-22 |
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r-cbanalysis
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A set of functions that helps you to generate descriptive statistics based on the variable types.
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2025-04-22 |
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r-causalsens
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The causalsens package provides functions to perform sensitivity analyses and to study how various assumptions about selection bias affects estimates of causal effects.
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2025-04-22 |
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r-causalmgm
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Allows users to learn undirected and directed (causal) graphs over mixed data types (i.e., continuous and discrete variables). To learn a directed graph over mixed data, it first calculates the undirected graph (Sedgewick et al, 2016) and then it uses local search strategies to prune-and-orient this graph (Sedgewick et al, 2017). AJ Sedgewick, I Shi, RM Donovan, PV Benos (2016) <doi:10.1186/s12859-016-1039-0>. AJ Sedgewick, JD Ramsey, P Spirtes, C Glymour, PV Benos (2017) <arXiv:1704.02621>.
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2025-04-22 |
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r-cattexact
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Provides functions for computing the one-sided p-values of the Cochran-Armitage trend test statistic for the asymptotic and the exact conditional test. The computation of the p-value for the exact test is performed using an algorithm following an idea by Mehta, et al. (1992) <doi:10.2307/1390598>.
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2025-04-22 |
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r-catt
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This function conducts the Cochran-Armitage trend test to a 2 by k contingency table. It will report the test statistic (Z) and p-value.A linear trend in the frequencies will be calculated, because the weights (0,1,2) will be used by default.
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2025-04-22 |
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r-catspec
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`ctab' creates (multiway) percentage tables. `sqtab' contains a set of functions for estimating models for square tables such as quasi-independence, symmetry, uniform association. Examples show how to use these models in a loglinear model using glm or in a multinomial logistic model using mlogit or clogit
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2025-04-22 |
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r-catseyes
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Provides the tools to produce catseye plots, principally by catseyesplot() function which calls R's standard plot() function internally, or alternatively by the catseyes() function to overlay the catseye plot onto an existing R plot window. Catseye plots illustrate the normal distribution of the mean (picture a normal bell curve reflected over its base and rotated 90 degrees), with a shaded confidence interval; they are an intuitive way of illustrating and comparing normally distributed estimates, and are arguably a superior alternative to standard confidence intervals, since they show the full distribution rather than fixed quantile bounds. The catseyesplot and catseyes functions require pre-calculated means and standard errors (or standard deviations), provided as numeric vectors; this allows the flexibility of obtaining this information from a variety of sources, such as direct calculation or prediction from a model. Catseye plots, as illustrations of the normal distribution of the means, are described in Cumming (2013 & 2014). Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 27, 7-29. <doi:10.1177/0956797613504966> pmid:24220629.
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2025-04-22 |
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r-catr
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Provides routines for the generation of response patterns under unidimensional dichotomous and polytomous computerized adaptive testing (CAT) framework. It holds many standard functions to estimate ability, select the first item(s) to administer and optimally select the next item, as well as several stopping rules. Options to control for item exposure and content balancing are also available (Magis and Barrada (2017) <doi:10.18637/jss.v076.c01>).
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2025-04-22 |
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r-cateselection
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A multi-factor dimensionality reduction based forward selection method for genetic association mapping.
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2025-04-22 |
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r-catencoders
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Contains some commonly used categorical variable encoders, such as 'LabelEncoder' and 'OneHotEncoder'. Inspired by the encoders implemented in Python 'sklearn.preprocessing' package (see <http://scikit-learn.org/stable/modules/preprocessing.html>).
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2025-04-22 |
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r-catdata
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This R-package contains examples from the book "Regression for Categorical Data", Tutz 2011, Cambridge University Press. The names of the examples refer to the chapter and the data set that is used.
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2025-04-22 |
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r-catchr
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R has a unique way of dealing with warnings, errors, messages, and other conditions, but it can often be troublesome to users coming from different programming backgrounds. The purpose of this package is to provide flexible and useful tools for handling R conditions with less hassle. In order to lower the barrier of entry, keep code clean and readable, and reduce the amount of typing required, `catchr` uses a very simple domain-specific language that simplifies things on the front-end. This package aims to maintain a continuous learning curve that lets new users jump straight in to condition-handling, while simultaneously offering depth and complexity for more advanced users.
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2025-04-22 |
