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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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2025-04-22 |
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r-crsnls
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Functions for nonlinear regression parameters estimation by algorithms based on Controlled Random Search algorithm. Both functions (crs4hc(), crs4hce()) adapt current search strategy by four heuristics competition. In addition, crs4hce() improves adaptability by adaptive stopping condition.
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2025-04-22 |
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r-crseeventstudy
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Based on Dutta et al. (2018) <doi:10.1016/j.jempfin.2018.02.004>, this package provides their standardized test for abnormal returns in long-horizon event studies. The methods used improve the major weaknesses of size, power, and robustness of long-run statistical tests described in Kothari/Warner (2007) <doi:10.1016/B978-0-444-53265-7.50015-9>. Abnormal returns are weighted by their statistical precision (i.e., standard deviation), resulting in abnormal standardized returns. This procedure efficiently captures the heteroskedasticity problem. Clustering techniques following Cameron et al. (2011) <10.1198/jbes.2010.07136> are adopted for computing cross-sectional correlation robust standard errors. The statistical tests in this package therefore accounts for potential biases arising from returns' cross-sectional correlation, autocorrelation, and volatility clustering without power loss.
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2025-04-22 |
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r-crrstep
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Performs forward and backwards stepwise regression for the Proportional subdistribution hazards model in competing risks (Fine & Gray 1999). Procedure uses AIC, BIC and BICcr as selection criteria. BICcr has a penalty of k = log(n*), where n* is the number of primary events.
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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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r-crossval
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Contains generic functions for performing cross validation and for computing diagnostic errors.
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2025-04-22 |
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r-crossva
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Enables transformation of Verbal Autopsy data collected with the WHO 2016 questionnaire (versions 1.4.1 & 1.5.1) or the WHO 2014 questionnaire for automated coding of Cause of Death using the InSilicoVA (data.type = "WHO2016") and InterVA5 algorithms. Previous versions of this package supported user-supplied mappings (via the map_records function), but this functionality has been removed. This package is made available by WHO and the Bloomberg Data for Health Initiative.
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2025-04-22 |
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r-crossreg
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This package provides functions to calculate confidence intervals for crossover points of two simple linear regression lines using the non-linear regression, the delta method, the Fieller method, and the bootstrap methods.
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2025-04-22 |
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r-crossdes
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Contains functions for the construction of carryover balanced crossover designs. In addition contains functions to check given designs for balance.
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2025-04-22 |
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r-cropdatape
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Provides peruvian agricultural production data from the Agriculture Minestry of Peru (MINAGRI). The first version includes 6 crops: rice, quinoa, potato, sweet potato, tomato and wheat; all of them across 24 departments. Initially, in excel files which has been transformed and assembled using tidy data principles, i.e. each variable is in a column, each observation is a row and each value is in a cell. The variables variables are sowing and harvest area per crop, yield, production and price per plot, every one year, from 2004 to 2014.
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2025-04-22 |
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r-crop
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A device closing function which is able to crop graphics (e.g., PDF, PNG files) on Unix-like operating systems with the required underlying command-line tools installed.
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2025-04-22 |
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r-cronr
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Create, edit, and remove 'cron' jobs on your unix-alike system. The package provides a set of easy-to-use wrappers to 'crontab'. It also provides an RStudio add-in to easily launch and schedule your scripts.
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2025-04-22 |
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r-crone
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Functions to carry out the most important crystallographic calculations for crystal structures made of 1d Gaussian-shaped atoms, especially useful for methods development. Main reference: E. Smith, G. Evans, J. Foadi (2017) <doi:10.1088/1361-6404/aa8188>.
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2025-04-22 |
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r-crochet
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Functions to help implement the extraction / subsetting / indexing function '[' and replacement function '[<-' of custom matrix-like types (based on S3, S4, etc.), modeled as closely to the base matrix class as possible (with tests to prove it).
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2025-04-22 |
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r-crn
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The crn package provides the core functions required to download and format data from the Climate Reference Network. Both daily and hourly data are downloaded from the ftp, a consolidated file of all stations is created, station metadata is extracted. In addition functions for selecting individual variables and creating R friendly datasets for them is provided.
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2025-04-22 |
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r-crisp
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Implements convex regression with interpretable sharp partitions (CRISP), which considers the problem of predicting an outcome variable on the basis of two covariates, using an interpretable yet non-additive model. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. More details are provided in Petersen, A., Simon, N., and Witten, D. (2016). Convex Regression with Interpretable Sharp Partitions. Journal of Machine Learning Research, 17(94): 1-31 <http://jmlr.org/papers/volume17/15-344/15-344.pdf>.
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2025-04-22 |
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r-credsubs
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Functions for constructing simultaneous credible bands and identifying subsets via the "credible subsets" (also called "credible subgroups") method.
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2025-04-22 |
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r-creditmetrics
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A set of functions for computing the CreditMetrics risk model
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2025-04-22 |
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r-credentials
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Setup and retrieve HTTPS and SSH credentials for use with 'git' and other services. For HTTPS remotes the package interfaces the 'git-credential' utility which 'git' uses to store HTTP usernames and passwords. For SSH remotes we provide convenient functions to find or generate appropriate SSH keys. The package both helps the user to setup a local git installation, and also provides a back-end for git/ssh client libraries to authenticate with existing user credentials.
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2025-04-22 |
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r-cream
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Provides a new method for identification of clusters of genomic regions within chromosomes. Primarily, it is used for calling clusters of cis-regulatory elements (COREs). 'CREAM' uses genome-wide maps of genomic regions in the tissue or cell type of interest, such as those generated from chromatin-based assays including DNaseI, ATAC or ChIP-Seq. 'CREAM' considers proximity of the elements within chromosomes of a given sample to identify COREs in the following steps: 1) It identifies window size or the maximum allowed distance between the elements within each CORE, 2) It identifies number of elements which should be clustered as a CORE, 3) It calls COREs, 4) It filters the COREs with lowest order which does not pass the threshold considered in the approach.
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2025-04-22 |
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r-crblocks
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Implements a statistical test for comparing bar plots or histograms of categorical data derived from a randomized block repeated measures layout.
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2025-04-22 |
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r-crantastic
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Various R tools for http://crantastic.org/
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2025-04-22 |
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r-cranlogs
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'API' to the database of 'CRAN' package downloads from the 'RStudio' 'CRAN mirror'. The database itself is at <http://cranlogs.r-pkg.org>, see <https://github.com/r-hub/cranlogs.app> for the raw 'API'.
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2025-04-22 |
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r-crank
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Functions for completing and recalculating rankings and sorting.
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2025-04-22 |
