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r-pocrm
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Provides functions to implement and simulate the partial order continual reassessment method (PO-CRM) of Wages, Conaway and O'Quigley (2011) <doi:10.1177/1740774511408748> for use in Phase I trials of combinations of agents. Provides a function for generating a set of initial guesses (skeleton) for the toxicity probabilities at each combination that correspond to the set of possible orderings of the toxicity probabilities specified by the user.
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2025-04-22 |
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r-pnn
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The program pnn implements the algorithm proposed by Specht (1990). It is written in the R statistical language. It solves a common problem in automatic learning. Knowing a set of observations described by a vector of quantitative variables, we classify them in a given number of groups. Then, the algorithm is trained with this datasets and should guess afterwards the group of any new observation. This neural network has the main advantage to begin generalization instantaneously even with a small set of known observations. It is delivered with four functions (learn, smooth, perf and guess) and a dataset. The functions are documented with examples and provided with unit tests.
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2025-04-22 |
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r-pnmtrem
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An R package for Probit-Normal Marginalized Transition Random Effects Models
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2025-04-22 |
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r-pmultinom
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Implements multinomial CDF (P(N1<=n1, ..., Nk<=nk)) and tail probabilities (P(N1>n1, ..., Nk>nk)), as well as probabilities with both constraints (P(l1<N1<=u1, ..., lk<Nk<=uk)). Uses a method suggested by Bruce Levin (1981) <doi:10.1214/aos/1176345593>.
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2025-04-22 |
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r-pmsampsize
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Computes the minimum sample size required for the development of a new multivariable prediction model using the criteria proposed by Riley et al. (2018) <doi: 10.1002/sim.7992>. pmsampsize can be used to calculate the minimum sample size for the development of models with continuous, binary or survival (time-to-event) outcomes. Riley et al. (2018) <doi: 10.1002/sim.7992> lay out a series of criteria the sample size should meet. These aim to minimise the overfitting and to ensure precise estimation of key parameters in the prediction model.
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2025-04-22 |
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r-pmr
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Descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce models, distance-based models, and rank-ordered logit models) and visualization with multidimensional preference analysis for ranking data are provided. Current, only complete rankings are supported by this package.
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2025-04-22 |
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r-pmmltransformations
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Allows for data to be transformed before using it to construct models. Builds structures to allow functions in the PMML package to output transformation details in addition to the model in the resulting PMML file. The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <http://www.dmg.org>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products, which integrate with web services, relational database systems and deploy natively on Hadoop in conjunction with Hive, Spark or Storm, as well as allow predictive analytics to be executed for IBM z Systems mainframe applications and real-time, streaming analytics platforms.
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2025-04-22 |
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r-pmml
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The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <http://www.dmg.org>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products, which integrate with web services, relational database systems and deploy natively on Hadoop in conjunction with Hive, Spark or Storm, as well as allow predictive analytics to be executed for IBM z Systems mainframe applications and real-time, streaming analytics platforms. The package isofor (used for anomaly detection) can be installed with devtools::install_github("Zelazny7/isofor").
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2025-04-22 |
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r-pmcmr
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Note, that the 'PMCMR' package is superseded by the novel 'PMCMRplus' package. The 'PMCMRplus' package contains all functions from 'PMCMR' and many more parametric and non-parametric multiple comparison procedures, one-factorial trend tests, as well as improved method functions, such as print, summary and plot. The 'PMCMR' package is no longer maintained, but kept for compatibility of reverse depending packages for some time.
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2025-04-22 |
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r-plusser
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plusser provides an API interface to Google+ so that posts, profiles and pages can be automatically retrieved.
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2025-04-22 |
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r-pluscode
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Retrieves a 'pluscode' by inputting latitude and longitude. Includes additional functions to retrieve neighbouring 'pluscodes'.
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2025-04-22 |
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r-plus
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public |
Efficient procedures for fitting an entire regression sequences with different model types.
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2025-04-22 |
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r-plsdof
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The plsdof package provides Degrees of Freedom estimates for Partial Least Squares (PLS) Regression. Model selection for PLS is based on various information criteria (aic, bic, gmdl) or on cross-validation. Estimates for the mean and covariance of the PLS regression coefficients are available. They allow the construction of approximate confidence intervals and the application of test procedures. Further, cross-validation procedures for Ridge Regression and Principal Components Regression are available.
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2025-04-22 |
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r-plsdepot
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public |
plsdepot contains different methods for PLS analysis of one or two data tables such as Tucker's Inter-Battery, NIPALS, SIMPLS, SIMPLS-CA, PLS Regression, and PLS Canonical Analysis. The main reference for this software is the awesome book (in French) 'La Regression PLS: Theorie et Pratique' by Michel Tenenhaus.
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2025-04-22 |
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r-plsbiplot1
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Principal Component Analysis (PCA) biplots, Covariance monoplots and biplots, Partial Least Squares (PLS) biplots, Partial Least Squares for Generalized Linear Model (PLS-GLM) biplots, Sparse Partial Least Squares (SPLS) biplots and Sparse Partial Least Squares for Generalized Linear Model (SPLS-GLM) biplots.
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2025-04-22 |
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r-plrmodels
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This package provides statistical inference tools applied to Partial Linear Regression (PLR) models. Specifically, point estimation, confidence intervals estimation, bandwidth selection, goodness-of-fit tests and analysis of covariance are considered. Kernel-based methods, combined with ordinary least squares estimation, are used and time series errors are allowed. In addition, these techniques are also implemented for both parametric (linear) and nonparametric regression models.
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2025-04-22 |
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r-plrasch
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Fit Log Linear by Linear Association models and Rasch family models by pseudolikelihood estimation
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2025-04-22 |
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r-plotwidgets
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Small self-contained plots for use in larger plots or to delegate plotting in other functions. Also contains a number of alternative color palettes and HSL color space based tools to modify colors or palettes.
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2025-04-22 |
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r-plotscale
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Figures rendered on graphics devices are usually rescaled to fit pre-determined device dimensions. 'plotscale' implements the reverse: desired plot dimensions are specified and device dimensions are calculated to accommodate marginal material, giving consistent proportions for plot elements. Default methods support grid graphics such as lattice and ggplot. See "example('devsize')" and "vignette('plotscale')".
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2025-04-22 |
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r-plotrix
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Lots of plots, various labeling, axis and color scaling functions.
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2025-04-22 |
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r-plotregionhighlighter
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Creates an envelope around a set of plotted points. The envelope is compact with a boundary that is continuous, smooth and convex. Each point is represented as a circle and the circles and connecting lines are the solution to the multiple pulley problem. This method can be used to highlight regions in a two-dimensional space.
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2025-04-22 |
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r-plotpc
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Plot principal component histograms around a bivariate scatter plot.
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2025-04-22 |
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r-plotmcmc
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Markov chain Monte Carlo diagnostic plots. The purpose of the package is to combine existing tools from the 'coda' and 'lattice' packages, and make it easy to adjust graphical details.
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2025-04-22 |
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r-plot.matrix
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Visualizes a matrix object plainly as heatmap. It provides S3 functions to plot simple matrices and loading matrices.
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2025-04-22 |
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r-plotlygeoassets
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Includes 'JavaScript' files that allow 'plotly' maps to render without an internet connection.
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2025-04-22 |
