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r-ordinalnet
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Fits ordinal regression models with elastic net penalty. Supported model families include cumulative probability, stopping ratio, continuation ratio, and adjacent category. These families are a subset of vector glm's which belong to a model class we call the elementwise link multinomial-ordinal (ELMO) class. Each family in this class links a vector of covariates to a vector of class probabilities. Each of these families has a parallel form, which is appropriate for ordinal response data, as well as a nonparallel form that is appropriate for an unordered categorical response, or as a more flexible model for ordinal data. The parallel model has a single set of coefficients, whereas the nonparallel model has a set of coefficients for each response category except the baseline category. It is also possible to fit a model with both parallel and nonparallel terms, which we call the semi-parallel model. The semi-parallel model has the flexibility of the nonparallel model, but the elastic net penalty shrinks it toward the parallel model. For details, refer to Wurm, Hanlon, and Rathouz (2017) <arXiv:1706.05003>.
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2025-04-22 |
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r-ordinallbm
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It implements functions for simulation and estimation of the ordinal latent block model (OLBM), as described in Corneli, Bouveyron and Latouche (2019).
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2025-04-22 |
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r-ordinalcont
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A regression framework for response variables which are continuous self-rating scales such as the Visual Analog Scale (VAS) used in pain assessment, or the Linear Analog Self-Assessment (LASA) scales in quality of life studies. These scales measure subjects' perception of an intangible quantity, and cannot be handled as ratio variables because of their inherent non-linearity. We treat them as ordinal variables, measured on a continuous scale. A function (the g function) connects the scale with an underlying continuous latent variable. The link function is the inverse of the CDF of the assumed underlying distribution of the latent variable. A variety of link functions are currently implemented.
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2025-04-22 |
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r-ordfacreg
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In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariates of any type. The outcome variable may be continuous, binary, or represent censored survival times. In the absence of a precise knowledge of the response function, using monotonicity constraints on the ordinal variables improves efficiency in estimating parameters, especially when sample sizes are small. This package implements an active set algorithm that efficiently computes such estimators.
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2025-04-22 |
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r-orderstats
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All the methods in this package generate a vector of uniform order statistics using a beta distribution and use an inverse cumulative distribution function for some distribution to give a vector of random order statistic variables for some distribution. This is much more efficient than using a loop since it is directly sampling from the order statistic distribution.
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2025-04-22 |
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r-ordering
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Functions to test/check/verify/investigate the ordering of vectors. The 'is_[strictly_]*' family of functions test vectors for 'sorted', 'monotonic', 'increasing', 'decreasing' order; 'is_constant' and 'is_incremental' test for the degree of ordering. `ordering` provides a numeric indication of ordering -2 (strictly decreasing) to 2 (strictly increasing).
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2025-04-22 |
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r-order2parent
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This package uses B-spline based nonparametric smooth estimators to estimate parent distributions given observations on multiple order statistics.
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2025-04-22 |
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r-orddom
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Computes ordinal, statistics and effect sizes as an alternative to mean comparison: Cliff's delta or success rate difference (SRD), Vargha and Delaney's A or the Area Under a Receiver Operating Characteristic Curve (AUC), the discrete type of McGraw & Wong's Common Language Effect Size (CLES) or Grissom & Kim's Probability of Superiority (PS), and the Number needed to treat (NNT) effect size. Moreover, comparisons to Cohen's d are offered based on Huberty & Lowman's Percentage of Group (Non-)Overlap considerations.
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2025-04-22 |
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r-orddisp
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Estimate location-shift models or rating-scale models accounting for response styles (RSRS) for the regression analysis of ordinal responses.
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2025-04-22 |
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r-orcutt
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Solve first order autocorrelation problems using an iterative method. This procedure estimates both autocorrelation and beta coefficients recursively until we reach the convergence (8th decimal as default). The residuals are computed after estimating Beta using EGLS approach and Rho is estimated using the previous residuals.
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2025-04-22 |
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r-orclus
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Functions to perform subspace clustering and classification.
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2025-04-22 |
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r-optrees
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Finds optimal trees in weighted graphs. In particular, this package provides solving tools for minimum cost spanning tree problems, minimum cost arborescence problems, shortest path tree problems and minimum cut tree problem.
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2025-04-22 |
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r-optr
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Solves linear systems of form Ax=b via Gauss elimination, LU decomposition, Gauss-Seidel, Conjugate Gradient Method (CGM) and Cholesky methods.
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2025-04-22 |
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r-optparse
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A command line parser inspired by Python's 'optparse' library to be used with Rscript to write "#!" shebang scripts that accept short and long flag/options.
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2025-04-22 |
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r-optismixture
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Code for optimal mixture weights in importance sampling. Workhorse functions penoptpersp() and penoptpersp.alpha.only() minimize estimated variances with and without control variates respectively. It can be used in adaptive mixture importance sampling, for example, function batch.estimation() does two stages, a pilot estimate of mixing alpha and a following importance sampling.
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2025-04-22 |
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r-optiscale
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Tools for performing an optimal scaling transformation on a data vector
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2025-04-22 |
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r-optionstrat
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Utilizes the Black-Scholes-Merton option pricing model to calculate key option analytics and perform graphical analysis of various option strategies. Provides functions to calculate the option premium and option greeks of European-style options.
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2025-04-22 |
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r-optionpricing
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Efficient Monte Carlo Algorithms for the price and the sensitivities of Asian and European Options under Geometric Brownian Motion.
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2025-04-22 |
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r-optional
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Introduces optional types with some() and none, as well as match_with() from functional languages.
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2025-04-22 |
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r-optinterim
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Optimal two and three stage designs monitoring time-to-event endpoints at a specified timepoint
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2025-04-22 |
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r-optimx
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Provides a replacement and extension of the optim() function to call to several function minimization codes in R in a single statement. These methods handle smooth, possibly box constrained functions of several or many parameters. Note that function 'optimr()' was prepared to simplify the incorporation of minimization codes going forward. Also implements some utility codes and some extra solvers, including safeguarded Newton methods. Many methods previously separate are now included here.
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2025-04-22 |
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r-optimstrat
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Intended to assist in the choice of the sampling strategy to implement in a survey.
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2025-04-22 |
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r-optimparallel
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Provides a parallel version of the L-BFGS-B method of optim(). The main function of the package is optimParallel(), which has the same usage and output as optim(). Using optimParallel() can significantly reduce the optimization time.
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2025-04-22 |
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r-optimbase
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Provides a set of commands to manage an abstract optimization method. The goal is to provide a building block for a large class of specialized optimization methods. This package manages: the number of variables, the minimum and maximum bounds, the number of non linear inequality constraints, the cost function, the logging system, various termination criteria, etc...
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2025-04-22 |
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r-optimaldesign
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Algorithms for D-, A- and IV-optimal designs of experiments. Some of the functions in this package require the 'gurobi' software and its accompanying R package. For their installation, please follow the instructions at <www.gurobi.com> and the file gurobi_inst.txt, respectively.
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2025-04-22 |
