|
r-cpgfilter
|
public |
Filter CpGs based on Intra-class Correlation Coefficients (ICCs) when replicates are available. ICCs are calculated by fitting linear mixed effects models to all samples including the un-replicated samples. Including the large number of un-replicated samples improves ICC estimates dramatically. The method accommodates any replicate design.
|
2024-01-16 |
|
r-cpgassoc
|
public |
Is designed to test for association between methylation at CpG sites across the genome and a phenotype of interest, adjusting for any relevant covariates. The package can perform standard analyses of large datasets very quickly with no need to impute the data. It can also handle mixed effects models with chip or batch entering the model as a random intercept. Also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots, and scatterplots for individual CpG sites.
|
2024-01-16 |
|
r-cpa
|
public |
Functions to test and compare causal models using Confirmatory Path Analysis.
|
2024-01-16 |
|
r-cp
|
public |
Functions for calculating the conditional power for different models in survival time analysis within randomized clinical trials with two different treatments to be compared and survival as an endpoint.
|
2024-01-16 |
|
r-coxphsgd
|
public |
Estimate coefficients of Cox proportional hazards model using stochastic gradient descent algorithm for batch data.
|
2024-01-16 |
|
r-coxphmic
|
public |
Sparse estimation for Cox PH models is done via Minimum approximated Information Criterion (MIC) by Su, Wijayasinghe, Fan, and Zhang (2016) <DOI:10.1111/biom.12484>. MIC mimics the best subset selection using a penalized likelihood approach yet with no need of a tuning parameter. The problem is further reformulated with a re-parameterization step so that it reduces to one unconstrained non-convex yet smooth programming problem, which can be solved efficiently. Furthermore, the re-parameterization tactic yields an additional advantage in terms of circumventing post-selection inference.
|
2024-01-16 |
|
r-covid19.analytics
|
public |
Load and analyze updated time series worldwide data of reported cases for the Novel Coronavirus Disease (COVID-19) from different sources, including the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) data repository <https://github.com/CSSEGISandData/COVID-19>, "Our World in Data" <https://github.com/owid/> among several others. The datasets reporting the COVID-19 cases are available in two main modalities, as a time series sequences and aggregated data for the last day with greater spatial resolution. Several analysis, visualization and modelling functions are available in the package that will allow the user to compute and visualize total number of cases, total number of changes and growth rate globally or for an specific geographical location, while at the same time generating models using these trends; generate interactive visualizations and generate Susceptible-Infected-Recovered (SIR) model for the disease spread.
|
2024-01-16 |
|
r-count
|
public |
Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2nd Edition (Cambridge University Press) and Hilbe, J.M. 2014. Modeling Count Data (Cambridge University Press).
|
2024-01-16 |
|
r-covid19
|
public |
Provides a daily summary of COVID-19 cases, deaths, recovered, tests, vaccinations, and hospitalizations for 230+ countries, 760+ regions, and 12000+ administrative divisions of lower level. Includes policy measures, mobility data, and geospatial identifiers. Data source: COVID-19 Data Hub <https://covid19datahub.io>.
|
2024-01-16 |
|
r-cowplot
|
public |
Provides various features that help with creating publication-quality figures with 'ggplot2', such as a set of themes, functions to align plots and arrange them into complex compound figures, and functions that make it easy to annotate plots and or mix plots with images. The package was originally written for internal use in the Wilke lab, hence the name (Claus O. Wilke's plot package). It has also been used extensively in the book Fundamentals of Data Visualization.
|
2024-01-16 |
|
r-covsep
|
public |
Functions for testing if the covariance structure of 2-dimensional data (e.g. samples of surfaces X_i = X_i(s,t)) is separable, i.e. if covariance(X) = C_1 x C_2. A complete descriptions of the implemented tests can be found in the paper Aston, John A. D.; Pigoli, Davide; Tavakoli, Shahin. Tests for separability in nonparametric covariance operators of random surfaces. Ann. Statist. 45 (2017), no. 4, 1431--1461. <doi:10.1214/16-AOS1495> <https://projecteuclid.org/euclid.aos/1498636862> <arXiv:1505.02023>.
|
2024-01-16 |
|
r-covrobust
|
public |
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>.
|
2024-01-16 |
|
r-corrr
|
public |
A tool for exploring correlations. It makes it possible to easily perform routine tasks when exploring correlation matrices such as ignoring the diagonal, focusing on the correlations of certain variables against others, or rearranging and visualizing the matrix in terms of the strength of the correlations.
|
2024-01-16 |
|
r-covfefe
|
public |
Converts any word, sentence or speech into Trump's infamous "covfefe" format. Reference: <https://www.nytimes.com/2017/05/31/us/politics/covfefe-trump-twitter.html>. Inspiration thanks to: <https://codegolf.stackexchange.com/questions/123685/covfefify-a-string>.
|
2024-01-16 |
|
r-counthmm
|
public |
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>.
|
2024-01-16 |
|
r-countrycode
|
public |
Standardize country names, convert them into one of 40 different coding schemes, convert between coding schemes, and assign region descriptors.
|
2024-01-16 |
|
r-countgmifs
|
public |
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.
|
2024-01-16 |
|
r-cost
|
public |
Parameter estimation, one-step ahead forecast and new location prediction methods for spatio-temporal data.
|
2024-01-16 |
|
r-cotrend
|
public |
Implements cointegration/co-trending rank selection algorithm in Guo and Shintani (2013) "Consistent co-trending rank selection when both stochastic and nonlinear deterministic trends are present". The Econometrics Journal 16: 473-483 <doi:10.1111/j.1368-423X.2012.00392.x>. Numbered examples correspond to Feb 2011 preprint <http://www.fas.nus.edu.sg/ecs/events/seminar/seminar-papers/05Apr11.pdf>.
|
2024-01-16 |
|
r-corrgram
|
public |
Calculates correlation of variables and displays the results graphically. Included panel functions can display points, shading, ellipses, and correlation values with confidence intervals. See Friendly (2002) <doi:10.1198/000313002533>.
|
2024-01-16 |
|
r-cosmofns
|
public |
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.
|
2024-01-16 |
|
r-correlation
|
public |
Lightweight package for computing different kinds of correlations, such as partial correlations, Bayesian correlations, multilevel correlations, polychoric correlations, biweight correlations, distance correlations and more. Part of the 'easystats' ecosystem. References: Makowski et al. (2020) <doi:10.21105/joss.02306>.
|
2024-01-16 |
|
r-cortest
|
public |
There are 6 novel robust tests for equal correlation. They are all based on logistic regressions. U are proportion to different types of correlation in 6 methods. The ST1() is based on Pearson correlation. ST2() improved ST1() by using median absolute deviation. ST3() utilized type M correlation and ST4() used Spearman correlation. ST5() and ST6() used two different ways to combine ST3() and ST4(). We highly recommend ST5() according to the passage New Statistical Methods for Constructing Robust Differential Correlation Networks to characterize the interactions among microRNAs published in Scientific Reports.
|
2024-01-16 |
|
r-corset
|
public |
Set of methods to constrain numerical series and time series within arbitrary boundaries.
|
2024-01-16 |
|
r-corrsieve
|
public |
Statistical summary of STRUCTURE output. STRUCTURE is a K-means clustering method for inferring population structure and assigning individuals to populations using genetic data. Pritchard JK, Stephens M, Donnelly PJ (2000) <DOI:10.1093/genetics/155.2.945>. <https://web.stanford.edu/group/pritchardlab/structure.html>.
|
2024-01-16 |
|
r-corrplot
|
public |
Provides a visual exploratory tool on correlation matrix that supports automatic variable reordering to help detect hidden patterns among variables.
|
2024-01-16 |
|
r-coro
|
public |
Provides 'coroutines' for R, a family of functions that can be suspended and resumed later on. This includes 'async' functions (which await) and generators (which yield). 'Async' functions are based on the concurrency framework of the 'promises' package. Generators are based on a dependency free iteration protocol defined in 'coro' and are compatible with iterators from the 'reticulate' package.
|
2024-01-16 |
|
r-corrmixed
|
public |
In clinical practice and research settings in medicine and the behavioral sciences, it is often of interest to quantify the correlation of a continuous endpoint that was repeatedly measured (e.g., test-retest correlations, ICC, etc.). This package allows for estimating these correlations based on mixed-effects models. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
|
2024-01-16 |
|
r-coronavirus
|
public |
Provides a daily summary of the Coronavirus (COVID-19) cases by state/province. Data source: Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus <https://systems.jhu.edu/research/public-health/ncov/>.
|
2024-01-16 |
|
r-correlplot
|
public |
Correlplot contains diverse routines for the construction of different plots for representing correlation matrices.
|
2024-01-16 |
|
r-correctoverloadedpeaks
|
public |
Analyzes and modifies metabolomics raw data (generated using GC-APCI-MS, Gas Chromatography-Atmospheric Pressure Chemical Ionization-Mass Spectrometry) to correct overloaded signals, i.e. ion intensities exceeding detector saturation leading to a cut-off peak. Data in xcmsRaw format are accepted as input and mzXML files can be processed alternatively. Overloaded signals are detected automatically and modified using an Gaussian or Isotopic-Ratio approach, QC plots are generated and corrected data are stored within the original xcmsRaw or mzXML respectively to allow further processing.
|
2024-01-16 |
|
r-correctedfdr
|
public |
There are many estimators of false discovery rate. In this package we compute the Nonlocal False Discovery Rate (NFDR) and the estimators of local false discovery rate: Corrected False discovery Rate (CFDR), Re-ranked False Discovery rate (RFDR) and the blended estimator. Bickel, D.R., Rahal, A. (2019) <https://tinyurl.com/kkdc9rk8>.
|
2024-01-16 |
|
r-corrdna
|
public |
Can be useful for finding associations among different positions in a position-wise aligned sequence dataset. The approach adopted for finding associations among positions is based on the latent multivariate normal distribution.
|
2024-01-16 |
|
r-corpora
|
public |
Utility functions for the statistical analysis of corpus frequency data. This package is a companion to the open-source course "Statistical Inference: A Gentle Introduction for Computational Linguists and Similar Creatures" ('SIGIL').
|
2024-01-16 |
|
r-copbasic
|
public |
Extensive functions for bivariate copula (bicopula) computations and related operations for bicopula theory. The lower, upper, product, and select other bicopula are implemented along with operations including the diagonal, survival copula, dual of a copula, co-copula, and numerical bicopula density. Level sets, horizontal and vertical sections are supported. Numerical derivatives and inverses of a bicopula are provided through which simulation is implemented. Bicopula composition, convex combination, asymmetry extension, and products also are provided. Support extends to the Kendall Function as well as the Lmoments thereof. Kendall Tau, Spearman Rho and Footrule, Gini Gamma, Blomqvist Beta, Hoeffding Phi, Schweizer- Wolff Sigma, tail dependency, tail order, skewness, and bivariate Lmoments are implemented, and positive/negative quadrant dependency, left (right) increasing (decreasing) are available. Other features include Kullback-Leibler Divergence, Vuong Procedure, spectral measure, and Lcomoments for inference, maximum likelihood, and AIC, BIC, and RMSE for goodness-of-fit.
|
2024-01-16 |
|
r-corpcor
|
public |
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.
|
2024-01-16 |
|
r-cor
|
public |
An algorithm of optimal subset selection, related to Covariance matrices, Observation matrices and Response vectors (COR) to select the optimal subsets in distributed estimation. The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02331888.2020.1823979>.
|
2024-01-16 |
|
r-coroica
|
public |
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/>.
|
2024-01-16 |
|
r-corlink
|
public |
A matrix of agreement patterns and counts for record pairs is the input for the procedure. An EM algorithm is used to impute plausible values for missing record pairs. A second EM algorithm, incorporating possible correlations between per-field agreement, is used to estimate posterior probabilities that each pair is a true match - i.e. constitutes the same individual.
|
2024-01-16 |
|
r-corenlp
|
public |
Provides a minimal interface for applying annotators from the 'Stanford CoreNLP' java library. Methods are provided for tasks such as tokenisation, part of speech tagging, lemmatisation, named entity recognition, coreference detection and sentiment analysis.
|
2024-01-16 |
|
r-core
|
public |
Given a collection of intervals with integer start and end positions, find recurrently targeted regions and estimate the significance of finding. Randomization is implemented by parallel methods, either using local host machines, or submitting grid engine jobs.
|
2024-01-16 |
|
r-coordinatecleaner
|
public |
Automated flagging of common spatial and temporal errors in biological and paleontological collection data, for the use in conservation, ecology and paleontology. Includes automated tests to easily flag (and exclude) records assigned to country or province centroid, the open ocean, the headquarters of the Global Biodiversity Information Facility, urban areas or the location of biodiversity institutions (museums, zoos, botanical gardens, universities). Furthermore identifies per species outlier coordinates, zero coordinates, identical latitude/longitude and invalid coordinates. Also implements an algorithm to identify data sets with a significant proportion of rounded coordinates. Especially suited for large data sets. The reference for the methodology is: Zizka et al. (2019) <doi:10.1111/2041-210X.13152>.
|
2024-01-16 |
|
r-corclass
|
public |
Perform a correlational class analysis of the data, resulting in a partition of the data into separate modules.
|
2024-01-16 |
|
r-corbin
|
public |
We design algorithms with linear time complexity with respect to the dimension for three commonly studied correlation structures, including exchangeable, decaying-product and K-dependent correlation structures, and extend the algorithms to generate binary data of general non-negative correlation matrices with quadratic time complexity. Jiang, W., Song, S., Hou, L. and Zhao, H. "A set of efficient methods to generate high-dimensional binary data with specified correlation structures." The American Statistician. See <doi:10.1080/00031305.2020.1816213> for a detailed presentation of the method.
|
2024-01-16 |
|
r-contrast
|
public |
One degree of freedom contrasts for 'lm', 'glm', 'gls', and 'geese' objects.
|
2024-01-16 |
|
r-copuladata
|
public |
Data sets used for copula modeling in addition to those in the package 'copula'. These include a random subsample from the US National Education Longitudinal Study (NELS) of 1988 and nursing home data from Wisconsin.
|
2024-01-16 |
|
r-copula.surv
|
public |
Simulating bivariate survival data from copula models. Estimation of the association parameter in copula models. Two different ways to estimate the association parameter in copula models are implemented. A goodness-of-fit test for a given copula model is implemented. See Emura, Lin and Wang (2010) <doi:10.1016/j.csda.2010.03.013> for details.
|
2024-01-16 |
|
r-copula.markov
|
public |
Estimation and statistical process control are performed under copula-based time-series models. Available are statistical methods in Long and Emura (2014 JCSA), Emura et al. (2017 Commun Stat-Simul) <DOI:10.1080/03610918.2015.1073303>, Huang and Emura (2021 Commun Stat-Simul) <DOI:10.1080/03610918.2019.1602647>, Lin et al. (2021 Comm Stat-Simul) <DOI:10.1080/03610918.2019.1652318>, Sun et al. (2020 JSS Series in Statistics)<DOI:10.1007/978-981-15-4998-4>, and Huang and Emura (2021, in revision).
|
2024-01-16 |
|
r-coppecosenzar
|
public |
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.
|
2024-01-16 |
|
r-consrank
|
public |
Compute the median ranking according to the Kemeny's axiomatic approach. Rankings can or cannot contain ties, rankings can be both complete or incomplete. The package contains both branch-and-bound algorithms and heuristic solutions recently proposed. The searching space of the solution can either be restricted to the universe of the permutations or unrestricted to all possible ties. The package also provide some useful utilities for deal with preference rankings, including both element-weight Kemeny distance and correlation coefficient. This release declare as deprecated some functions that are still in the package for compatibility. Next release will not contains these functions. Please type '?ConsRank-deprecated' Essential references: Emond, E.J., and Mason, D.W. (2002) <doi:10.1002/mcda.313>; D'Ambrosio, A., Amodio, S., and Iorio, C. (2015) <doi:10.1285/i20705948v8n2p198>; Amodio, S., D'Ambrosio, A., and Siciliano R. (2016) <doi:10.1016/j.ejor.2015.08.048>; D'Ambrosio, A., Mazzeo, G., Iorio, C., and Siciliano, R. (2017) <doi:10.1016/j.cor.2017.01.017>; Albano, A., and Plaia, A. (2021) <doi:10.1285/i20705948v14n1p117>.
|
2024-01-16 |
