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r-onage
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Implementation of a likelihood ratio test of differential onset of senescence between two groups. Given two groups with measures of age and of an individual trait likely to be subjected to senescence (e.g. body mass), 'OnAge' provides an asymptotic p-value for the null hypothesis that senescence starts at the same age in both groups. The package implements the procedure used in Douhard et al. (2017) <doi:10.1111/oik.04421>.
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2025-04-22 |
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r-omisc
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Primarily devoted to implementing the Univariate Bootstrap (as well as the Traditional Bootstrap). In addition there are multiple functions for DeFries-Fulker behavioral genetics models. The univariate bootstrapping functions, DeFries-Fulker functions, regression and traditional bootstrapping functions form the original core. Additional features may come online later, however this software is a work in progress. For more information about univariate bootstrapping see: Lee and Rodgers (1998) and Beasley et al (2007) <doi.org/10.1037/1082-989X.12.4.414>.
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2025-04-22 |
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r-omickriging
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It provides functions to generate a correlation matrix from a genetic dataset and to use this matrix to predict the phenotype of an individual by using the phenotypes of the remaining individuals through kriging. Kriging is a geostatistical method for optimal prediction or best unbiased linear prediction. It consists of predicting the value of a variable at an unobserved location as a weighted sum of the variable at observed locations. Intuitively, it works as a reverse linear regression: instead of computing correlation (univariate regression coefficients are simply scaled correlation) between a dependent variable Y and independent variables X, it uses known correlation between X and Y to predict Y.
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2025-04-22 |
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r-omd
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This package including two useful function, which can be used for filter the molecular descriptors matrix for QSAR.
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2025-04-22 |
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r-olscurve
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Provides tools for more easily organizing and plotting individual ordinary least square (OLS) growth curve trajectories.
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2025-04-22 |
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r-oligospecificitysystem
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Calculate the theorical specificity of a system of multiple primers used for PCR, qPCR primers or degenerated primer design
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2025-04-22 |
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r-okmesonet
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okmesonet retrieves and summarizes Oklahoma (USA) Mesonet climatological data provided by the Oklahoma Climatological Survey. Measurements are recorded every five minutes at approximately 120 stations throughout Oklahoma and are available in near real-time.
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2025-04-22 |
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r-okcupiddata
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Cleaned profile data from "OkCupid Profile Data for Introductory Statistics and Data Science Courses" (Journal of Statistics Education 2015 <http://www.amstat.org/publications/jse/v23n2/kim.pdf>).
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2025-04-22 |
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r-oidata
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A collection of data sets from several sources that may be useful for teaching, practice, or other purposes. Functions have also been included to assist in the retrieval of table data from websites or in visualizing sample data.
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2025-04-22 |
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r-ohtadstats
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Calculate's Tomoka Ohta's partitioning of linkage disequilibrium, deemed D-statistics, for pairs of loci. Beissinger et al. (2016) <doi:10.1038/hdy.2015.81>.
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2025-04-22 |
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r-ohpl
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Ordered homogeneity pursuit lasso (OHPL) algorithm for group variable selection proposed in Lin et al. (2017) <DOI:10.1016/j.chemolab.2017.07.004>. The OHPL method exploits the homogeneity structure in high-dimensional data and enjoys the grouping effect to select groups of important variables automatically. This feature makes it particularly useful for high-dimensional datasets with strongly correlated variables, such as spectroscopic data.
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2025-04-22 |
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r-ohit
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Ing and Lai (2011) <doi:10.5705/ss.2010.081> proposed a high-dimensional model selection procedure that comprises three steps: orthogonal greedy algorithm (OGA), high-dimensional information criterion (HDIC), and Trim. The first two steps, OGA and HDIC, are used to sequentially select input variables and determine stopping rules, respectively. The third step, Trim, is used to delete irrelevant variables remaining in the second step. This package aims at fitting a high-dimensional linear regression model via OGA+HDIC+Trim.
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2025-04-22 |
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r-oglmx
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Ordered models such as ordered probit and ordered logit presume that the error variance is constant across observations. In the case that this assumption does not hold estimates of marginal effects are typically biased (Weiss (1997)). This package allows for generalization of ordered probit and ordered logit models by allowing the user to specify a model for the variance. Furthermore, the package includes functions to calculate the marginal effects. Wrapper functions to estimate the standard limited dependent variable models are also included.
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2025-04-22 |
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r-ogi
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Consider a data matrix of n individuals with p variates. The objective general index (OGI) is a general index that combines the p variates into a univariate index in order to rank the n individuals. The OGI is always positively correlated with each of the variates. More details can be found in Sei (2016) <doi:10.1016/j.jmva.2016.02.005>.
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2025-04-22 |
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r-ods
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Outcome-dependent sampling (ODS) schemes are cost-effective ways to enhance study efficiency. In ODS designs, one observes the exposure/covariates with a probability that depends on the outcome variable. Popular ODS designs include case-control for binary outcome, case-cohort for time-to-event outcome, and continuous outcome ODS design (Zhou et al. 2002) <doi: 10.1111/j.0006-341X.2002.00413.x>. Because ODS data has biased sampling nature, standard statistical analysis such as linear regression will lead to biases estimates of the population parameters. This package implements four statistical methods related to ODS designs: (1) An empirical likelihood method analyzing the primary continuous outcome with respect to exposure variables in continuous ODS design (Zhou et al., 2002). (2) A partial linear model analyzing the primary outcome in continuous ODS design (Zhou, Qin and Longnecker, 2011) <doi: 10.1111/j.1541-0420.2010.01500.x>. (3) Analyze a secondary outcome in continuous ODS design (Pan et al. 2018) <doi: 10.1002/sim.7672>. (4) An estimated likelihood method analyzing a secondary outcome in case-cohort data (Pan et al. 2017) <doi: 10.1111/biom.12838>.
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2025-04-22 |
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r-odr
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Calculate the optimal sample allocation that minimizes the variance of treatment effect in multilevel randomized trials under fixed budget and cost structure, perform power analyses with and without accommodating costs and budget. The references for proposed methods are: (1) Shen, Z. (in progress). Using optimal sample allocation to improve statistical precision and design efficiency for multilevel randomized trials. (unpublished doctoral dissertation). University of Cincinnati, Cincinnati, OH. (2) Shen, Z., & Kelcey, B. (revise & resubmit). Optimal sample allocation accounts for the full variation of sampling costs in cluster-randomized trials. Journal of Educational and Behavioral Statistics. (3) Shen, Z., & Kelcey, B. (2018, April). Optimal design of cluster randomized trials under condition- and unit-specific cost structures. Roundtable discussion presented at American Educational Research Association (AERA) annual conference. (4) Champely., S. (2018). pwr: Basic functions for power analysis (Version 1.2-2) [Software]. Available from <https://CRAN.R-project.org/package=pwr>.
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2025-04-22 |
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r-odenetwork
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Simulates a network of ordinary differential equations of order two. The package provides an easy interface to construct networks. In addition you are able to define different external triggers to manipulate the trajectory. The method is described by Surmann, Ligges, and Weihs (2014) <doi:10.1109/ENERGYCON.2014.6850482>.
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2025-04-22 |
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r-odds.n.ends
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Computes odds ratios and 95% confidence intervals from a generalized linear model object. It also computes model significance with the chi-squared statistic and p-value and it computes model fit using a contingency table to determine the percent of observations for which the model correctly predicts the value of the outcome. Calculates model sensitivity and specificity.
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2025-04-22 |
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r-odds.converter
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Conversion between the most common odds types for sports betting. Hong Kong odds, US odds, Decimal odds, Indonesian odds, Malaysian odds, and raw Probability are covered in this package.
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2025-04-22 |
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r-odb
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This package provides functions to create, connect, update and query HSQL databases embedded in Open Document Databases (.odb) files, as OpenOffice and LibreOffice do.
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2025-04-22 |
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r-odata
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Helper methods for accessing data from web service based on OData Protocol. It provides several helper methods to access the service metadata, the data from datasets and to download some file resources (it only support CSV for now). For more information about OData go to <http://www.odata.org/documentation/>.
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2025-04-22 |
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r-ocp
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Implements the Bayesian online changepoint detection method by Adams and MacKay (2007) <arXiv:0710.3742> for univariate or multivariate data. Gaussian and Poisson probability models are implemented. Provides post-processing functions with alternative ways to extract changepoints.
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2025-04-22 |
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r-ocomposition
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Regression model where the response variable is a rank-indexed compositional vector (non-negative values that sum up to one and are ordered from the largest to the smallest). Parameters are estimated in the Bayesian framework using MCMC methods.
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2025-04-22 |
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r-ocedata
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Several important and Oceanographic data sets are provided. These are particularly useful to the 'oce' package, but can also be helpful in a general context.
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2025-04-22 |
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r-occ
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Generic function for estimating positron emission tomography (PET) neuroreceptor occupancies from the total volumes of distribution of a set of regions of interest. Fittings methods include the simple 'reference region' and 'ordinary least squares' (sometimes known as occupancy plot) methods, as well as the more efficient 'restricted maximum likelihood estimation'.
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2025-04-22 |
