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r-poker
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public |
Type testRoundOfPoker() to demonstrate the game of Texas Hold ‘Em poker. Rotate the dealer button, deal cards, rank each hand, compare ranks, break ties (if necessary), determine the winner, output a textual summary, and output a graphical user interface.
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2024-01-16 |
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r-poistweedie
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public |
Simulation of models Poisson-Tweedie.
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2024-01-16 |
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r-poisson.glm.mix
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public |
Mixtures of Poisson Generalized Linear Models for high dimensional count data clustering. The (multivariate) responses can be partitioned into set of blocks. Three different parameterizations of the linear predictor are considered. The models are estimated according to the EM algorithm with an efficient initialization scheme <doi:10.1016/j.csda.2014.07.005>.
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2024-01-16 |
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r-poisnor
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public |
Generates multivariate data with count and continuous variables with a pre-specified correlation matrix. The count and continuous variables are assumed to have Poisson and normal marginals, respectively. The data generation mechanism is a combination of the normal to anything principle and a connection between Poisson and normal correlations in the mixture. The details of the method are explained in Yahav et al. (2012) <DOI:10.1002/asmb.901>.
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2024-01-16 |
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r-pnadcibge
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public |
Provides tools for downloading, reading and analyzing the PNADC, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website <https://www.ibge.gov.br/>. Further analysis must be made using package 'survey'.
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2024-01-16 |
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r-plsrglm
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public |
Provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria <arXiv:1810.01005>. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
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2024-01-16 |
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r-poisnonnor
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public |
Generation of count (assuming Poisson distribution) and continuous data (using Fleishman polynomials) simultaneously. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.
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2024-01-16 |
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r-poisbinnonnor
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public |
Generation of multiple count, binary and continuous variables simultaneously given the marginal characteristics and association structure. Throughout the package, the word 'Poisson' is used to imply count data under the assumption of Poisson distribution. The details of the method are explained in Amatya et al. (2015) <DOI:10.1080/00949655.2014.953534>.
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2024-01-16 |
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r-pointdensityp
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public |
The function pointdensity returns a density count and the temporal average for every point in the original list. The dataframe returned includes four columns: lat, lon, count, and date_avg. The "lat" column is the original latitude data; the "lon" column is the original longitude data; the "count" is the density count of the number of points within a radius of radius*grid_size (the neighborhood); and the date_avg column includes the average date of each point in the neighborhood.
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2024-01-16 |
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r-poiclaclu
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public |
Implements the methods described in the paper, Witten (2011) Classification and Clustering of Sequencing Data using a Poisson Model, Annals of Applied Statistics 5(4) 2493-2518.
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2024-01-16 |
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r-poet
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public |
Estimate large covariance matrices in approximate factor models by thresholding principal orthogonal complements.
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2024-01-16 |
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r-pod
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public |
This tool computes the probability of detection (POD) curve and the limit of detection (LOD), i.e. the number of copies of the target DNA sequence required to ensure a 95 % probability of detection (LOD95). Other quantiles of the LOD can be specified. This is a reimplementation of the mathematical-statistical modelling of the validation of qualitative polymerase chain reaction (PCR) methods within a single laboratory as provided by the commercial tool 'PROLab' <http://quodata.de/>. The modelling itself has been described by Uhlig et al. (2015) <doi:10.1007/s00769-015-1112-9>.
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2024-01-16 |
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r-plspm
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public |
Partial Least Squares Path Modeling (PLS-PM), Tenenhaus, Esposito Vinzi, Chatelin, Lauro (2005) <doi:10.1016/j.csda.2004.03.005>, analysis for both metric and non-metric data, as well as REBUS analysis, Esposito Vinzi, Trinchera, Squillacciotti, and Tenenhaus (2008) <doi:10.1002/asmb.728>.
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2024-01-16 |
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r-pocrm
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public |
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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2024-01-16 |
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r-pmultinom
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public |
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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2024-01-16 |
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r-pmr
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public |
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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2024-01-16 |
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r-pmsampsize
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public |
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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2024-01-16 |
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r-pmmltransformations
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public |
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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2024-01-16 |
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r-pmml
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public |
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://dmg.org/>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products. The package isofor (used for anomaly detection) can be installed with devtools::install_github("gravesee/isofor").
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2024-01-16 |
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r-pmcmr
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public |
Note, that the 'PMCMR' package is superset 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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2024-01-16 |
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r-plottools
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public |
Annotate plots with legends for continuous variables and colour spectra using the base graphics plotting tools; and manipulate irregular polygons.
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2024-01-16 |
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r-pluscode
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public |
Retrieves a 'pluscode' by inputting latitude and longitude. Includes additional functions to retrieve neighbouring 'pluscodes'.
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2024-01-16 |
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r-plumber
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public |
Gives the ability to automatically generate and serve an HTTP API from R functions using the annotations in the R documentation around your functions.
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2024-01-16 |
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r-plsdof
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public |
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 (Kramer and Sugiyama 2012 <doi:10.1198/jasa.2011.tm10107>). Further, cross-validation procedures for Ridge Regression and Principal Components Regression are available.
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2024-01-16 |
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r-plsdepot
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public |
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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2024-01-16 |
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r-pls
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public |
Multivariate regression methods Partial Least Squares Regression (PLSR), Principal Component Regression (PCR) and Canonical Powered Partial Least Squares (CPPLS).
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2024-01-16 |
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r-plotrcs
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public |
Simple drawing of restricted cubic spline (RCS) curves through 'ggplot2' package from a linear regression model, a logistic regression model or a Cox proportional hazards regression model. This method was described by Harrell FE (2015) <doi:10.1007/978-3-319-19425-7>.
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2024-01-16 |
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r-plrmodels
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public |
Contains 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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2024-01-16 |
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r-plotwidgets
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public |
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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2024-01-16 |
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r-plotmo
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public |
Plot model surfaces for a wide variety of models using partial dependence plots and other techniques. Also plot model residuals and other information on the model.
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2024-01-16 |
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r-plotroc
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public |
Most ROC curve plots obscure the cutoff values and inhibit interpretation and comparison of multiple curves. This attempts to address those shortcomings by providing plotting and interactive tools. Functions are provided to generate an interactive ROC curve plot for web use, and print versions. A Shiny application implementing the functions is also included.
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2024-01-16 |
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r-plotscale
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public |
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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2024-01-16 |
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r-plotdap
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public |
Easily visualize and animate 'tabledap' and 'griddap' objects obtained via the 'rerddap' package in a simple one-line command, using either base graphics or 'ggplot2' graphics. 'plotdap' handles extracting and reshaping the data, map projections and continental outlines. Optionally the data can be animated through time using the 'gganmiate' package.
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2024-01-16 |
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r-plotrix
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public |
Lots of plots, various labeling, axis and color scaling functions.
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2024-01-16 |
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r-plotpc
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public |
Plot principal component histograms around a bivariate scatter plot.
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2024-01-16 |
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r-plot3drgl
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public |
The 'rgl' implementation of plot3D functions.
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2024-01-16 |
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r-plotmcmc
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public |
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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2024-01-16 |
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r-plotlygeoassets
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public |
Includes 'JavaScript' files that allow 'plotly' maps to render without an internet connection.
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2024-01-16 |
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r-plotly
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public |
Create interactive web graphics from 'ggplot2' graphs and/or a custom interface to the (MIT-licensed) JavaScript library 'plotly.js' inspired by the grammar of graphics.
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2024-01-16 |
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r-plink
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public |
Item response theory based methods are used to compute linking constants and conduct chain linking of unidimensional or multidimensional tests for multiple groups under a common item design. The unidimensional methods include the Mean/Mean, Mean/Sigma, Haebara, and Stocking-Lord methods for dichotomous (1PL, 2PL and 3PL) and/or polytomous (graded response, partial credit/generalized partial credit, nominal, and multiple-choice model) items. The multidimensional methods include the least squares method and extensions of the Haebara and Stocking-Lord method using single or multiple dilation parameters for multidimensional extensions of all the unidimensional dichotomous and polytomous item response models. The package also includes functions for importing item and/or ability parameters from common IRT software, conducting IRT true score and observed score equating, and plotting item response curves/surfaces, vector plots, information plots, and comparison plots for examining parameter drift.
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2024-01-16 |
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r-plotfunctions
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public |
When analyzing data, plots are a helpful tool for visualizing data and interpreting statistical models. This package provides a set of simple tools for building plots incrementally, starting with an empty plot region, and adding bars, data points, regression lines, error bars, gradient legends, density distributions in the margins, and even pictures. The package builds further on R graphics by simply combining functions and settings in order to reduce the amount of code to produce for the user. As a result, the package does not use formula input or special syntax, but can be used in combination with default R plot functions. Note: Most of the functions were part of the package 'itsadug', which is now split in two packages: 1. the package 'itsadug', which contains the core functions for visualizing and evaluating nonlinear regression models, and 2. the package 'plotfunctions', which contains more general plot functions.
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2024-01-16 |
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r-plotcontour
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public |
This function plots a contour line with a user-defined probability and tightness of fit.
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2024-01-16 |
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r-plot3d
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public |
Functions for viewing 2-D and 3-D data, including perspective plots, slice plots, surface plots, scatter plots, etc. Includes data sets from oceanography.
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2024-01-16 |
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r-plot.matrix
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public |
Visualizes a matrix object plainly as heatmap. It provides S3 functions to plot simple matrices and loading matrices.
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2024-01-16 |
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r-plogr
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public |
A simple header-only logging library for C++. Add 'LinkingTo: plogr' to 'DESCRIPTION', and '#include <plogr.h>' in your C++ modules to use it.
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2024-01-16 |
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r-plainview
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public |
Provides methods for plotting potentially large (raster) images interactively on a plain HTML canvas. In contrast to package 'mapview' data are plotted without background map, but data can be projected to any spatial coordinate reference system. Supports plotting of classes 'RasterLayer', 'RasterStack', 'RasterBrick' (from package 'raster') as well as 'png' files located on disk. Interactivity includes zooming, panning, and mouse location information. In case of multi-layer 'RasterStacks' or 'RasterBricks', RGB image plots are created (similar to 'raster::plotRGB' - but interactive).
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2024-01-16 |
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r-plm
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None |
A set of estimators for models and (robust) covariance matrices, and tests for panel data econometrics, including within/fixed effects, random effects, between, first-difference, nested random effects as well as instrumental-variable (IV) and Hausman-Taylor-style models, panel generalized method of moments (GMM) and general FGLS models, mean groups (MG), demeaned MG, and common correlated effects (CCEMG) and pooled (CCEP) estimators with common factors, variable coefficients and limited dependent variables models. Test functions include model specification, serial correlation, cross-sectional dependence, panel unit root and panel Granger (non-)causality. Typical references are general econometrics text books such as Baltagi (2021), Econometric Analysis of Panel Data (<doi:10.1007/978-3-030-53953-5>), Hsiao (2014), Analysis of Panel Data (<doi:10.1017/CBO9781139839327>), and Croissant and Millo (2018), Panel Data Econometrics with R (<doi:10.1002/9781119504641>).
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2024-01-16 |
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r-plis
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public |
A multiple testing procedure for testing several groups of hypotheses is implemented. Linear dependency among the hypotheses within the same group is modeled by using hidden Markov Models. It is noted that a smaller p value does not necessarily imply more significance due to the dependency. A typical application is to analyze genome wide association studies datasets, where SNPs from the same chromosome are treated as a group and exhibit strong linear genomic dependency. See Wei Z, Sun W, Wang K, Hakonarson H (2009) <doi:10.1093/bioinformatics/btp476> for more details.
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2024-01-16 |
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r-plde
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public |
We present a penalized log-density estimation method using Legendre polynomials with lasso penalty to adjust estimate's smoothness. Re-expressing the logarithm of the density estimator via a linear combination of Legendre polynomials, we can estimate parameters by maximizing the penalized log-likelihood function. Besides, we proposed an implementation strategy that builds on the coordinate decent algorithm, together with the Bayesian information criterion (BIC).
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2024-01-16 |
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r-plantecophys
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public |
Coupled leaf gas exchange model, A-Ci curve simulation and fitting, Ball-Berry stomatal conductance models, leaf energy balance using Penman-Monteith, Cowan-Farquhar optimization, humidity unit conversions. See Duursma (2015) <doi:10.1371/journal.pone.0143346>.
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2024-01-16 |
