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r-planor
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Automatic generation of regular factorial designs, including fractional designs, orthogonal block designs, row-column designs and split-plots.
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2025-04-22 |
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r-plac
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A semi-parametric estimation method for the Cox model with left-truncated data using augmented information from the marginal of truncation times.
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2025-04-22 |
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r-pivotalr
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Provides an R interface for the Pivotal Data stack running on 'PostgreSQL', 'Greenplum' or 'Apache HAWQ (incubating)' databases with parallel and distributed computation ability for big data processing. 'PivotalR' provides an R interface to various database operations on tables or views. These operations are almost the same as the corresponding native R operations. Thus users of R do not need to learn 'SQL' when they operate on objects in the database. It also provides a wrapper for 'Apache MADlib (incubating)', which is an open- source library for parallel and scalable in-database analytics.
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2025-04-22 |
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r-piton
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A wrapper around the 'Parsing Expression Grammar Template Library', a C++11 library for generating Parsing Expression Grammars, that makes it accessible within Rcpp. With this, developers can implement their own grammars and easily expose them in R packages.
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2025-04-22 |
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r-pingr
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Check if a remote computer is up. It can either just call the system ping command, or check a specified TCP port.
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2025-04-22 |
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r-pijavski
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Global univariate minimization of Lipschitz functions is performed by using Pijavski method, which was published in Pijavski (1972) <DOI:10.1016/0041-5553(72)90115-2>.
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2025-04-22 |
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r-pieceexpintensity
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This function fits a reversible jump Bayesian piecewise exponential model that also includes the intensity of each event considered along with the rate of events.
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2025-04-22 |
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r-picasso
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Computationally efficient tools for fitting generalized linear model with convex or non-convex penalty. Users can enjoy the superior statistical property of non-convex penalty such as SCAD and MCP which has significantly less estimation error and overfitting compared to convex penalty such as lasso and ridge. Computation is handled by multi-stage convex relaxation and the PathwIse CAlibrated Sparse Shooting algOrithm (PICASSO) which exploits warm start initialization, active set updating, and strong rule for coordinate preselection to boost computation, and attains a linear convergence to a unique sparse local optimum with optimal statistical properties. The computation is memory-optimized using the sparse matrix output.
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2025-04-22 |
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r-physiology
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A variety of formulae are provided for estimation of physiologic characteristics of infants, children, and adults. Calculations include: body surface area, ideal weight, airway dead-space, the alveolar gas equation, and GFR. Each formula is referenced to the original publication. Future functions will cover more material with a focus on anaesthesia, critical care and peri-operative medicine.
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2025-04-22 |
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r-phylomeasures
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Given a phylogenetic tree T and an assemblage S of species represented as a subset of tips in T, we want to compute a measure of the diversity of the species in S with respect to T. The current package offers efficient algorithms that can process large phylogenetic data for several such measures. Most importantly, the package includes algorithms for computing efficiently the standardized versions of phylogenetic measures and their p-values, which are essential for null model comparisons. Among other functions, the package provides efficient computation of richness-standardized versions for indices such as the net relatedness index (NRI), nearest taxon index (NTI), phylogenetic diversity index (PDI), and the corresponding indices of two-sample measures. The package also introduces a new single-sample measure, the Core Ancestor Cost (CAC); the package provides functions for computing the value and the standardised index of the CAC and, more than that, there is an extra function available that can compute exactly any statistical moment of the measure. The package supports computations under different null models, including abundance-weighted models.
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2025-04-22 |
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r-phyloland
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Phyloland package models a space colonization process mapped onto a phylogeny, it aims at estimating limited dispersal and ecological competitive exclusion in a Bayesian MCMC statistical phylogeographic framework (please refer to phyloland-package help for details.)
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2025-04-22 |
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r-phyclust
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Phylogenetic clustering (phyloclustering) is an evolutionary Continuous Time Markov Chain model-based approach to identify population structure from molecular data without assuming linkage equilibrium. The package phyclust (Chen 2011) provides a convenient implementation of phyloclustering for DNA and SNP data, capable of clustering individuals into subpopulations and identifying molecular sequences representative of those subpopulations. It is designed in C for performance, interfaced with R for visualization, and incorporates other popular open source programs including ms (Hudson 2002) <doi:10.1093/bioinformatics/18.2.337>, seq-gen (Rambaut and Grassly 1997) <doi:10.1093/bioinformatics/13.3.235>, Hap-Clustering (Tzeng 2005) <doi:10.1002/gepi.20063> and PAML baseml (Yang 1997, 2007) <doi:10.1093/bioinformatics/13.5.555>, <doi:10.1093/molbev/msm088>, for simulating data, additional analyses, and searching the best tree. See the phyclust website for more information, documentations and examples.
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2025-04-22 |
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r-phreeqc
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A geochemical modeling program developed by the US Geological Survey that is designed to perform a wide variety of aqueous geochemical calculations, including speciation, batch-reaction, one-dimensional reactive-transport, and inverse geochemical calculations.
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2025-04-22 |
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r-phonics
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Provides a collection of phonetic algorithms including Soundex, Metaphone, NYSIIS, Caverphone, and others.
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2025-04-22 |
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r-phmm
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Fits proportional hazards model incorporating random effects using an EM algorithm using Markov Chain Monte Carlo at E-step. Vaida and Xu (2000) <DOI:10.1002/1097-0258(20001230)19:24%3C3309::AID-SIM825%3E3.0.CO;2-9>.
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2025-04-22 |
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r-pheno
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Provides some easy-to-use functions for time series analyses of (plant-) phenological data sets. These functions mainly deal with the estimation of combined phenological time series and are usually wrappers for functions that are already implemented in other R packages adapted to the special structure of phenological data and the needs of phenologists. Some date conversion functions to handle Julian dates are also provided.
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2025-04-22 |
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r-phenex
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Provides some easy-to-use functions for spatial analyses of (plant-) phenological data sets and satellite observations of vegetation.
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2025-04-22 |
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r-phase123
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Contains three simulation functions for implementing the entire Phase 123 trial and the separate Eff-Tox and Phase 3 portions of the trial, which may be beneficial for use on clusters. The functions AssignEffTox() and RandomizeEffTox() assign doses to patient cohorts during phase 12 and Reoptimize() determines the optimal dose to continue with during Phase 3. The functions ReturnMeansAgent() and ReturnMeanControl() gives the true mean survival for the agent doses and control and ReturnOCS() gives the operating characteristics of the design.
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2025-04-22 |
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r-ph2hetero
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Implementation of Jones (2007) <doi:10.1016/j.cct.2007.02.008> , Tournoux-Facon (2011) <doi:10.1002/sim.4148> and Parashar (2016) <doi:10.1002/pst.1742> designs.
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2025-04-22 |
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r-ph2bayes
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An implementation of Bayesian single-arm phase II design methods for binary outcome based on posterior probability (Thall and Simon (1994) <doi:10.2307/2533377>) and predictive probability (Lee and Liu (2008) <doi:10.1177/1740774508089279>).
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2025-04-22 |
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r-pgsc
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Computes the generalized synthetic control estimator described in Powell (2017) <doi:10.7249/WR1142>. Provides both point estimates, and hypothesis testing.
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2025-04-22 |
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r-pgmm
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Carries out model-based clustering or classification using parsimonious Gaussian mixture models. McNicholas and Murphy (2008) <doi:10.1007/s11222-008-9056-0>, McNicholas (2010) <doi:10.1016/j.jspi.2009.11.006>, McNicholas and Murphy (2010) <doi:10.1093/bioinformatics/btq498>, McNicholas et al. (2010) <doi:10.1016/j.csda.2009.02.011>.
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2025-04-22 |
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r-pgee.mixed
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Perform simultaneous estimation and variable selection for correlated bivariate mixed outcomes (one continuous outcome and one binary outcome per cluster) using penalized generalized estimating equations. In addition, clustered Gaussian and binary outcomes can also be modeled. The SCAD, MCP, and LASSO penalties are supported. Cross-validation can be performed to find the optimal regularization parameter(s).
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2025-04-22 |
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r-pgdraw
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Generates random samples from the Polya-Gamma distribution using an implementation of the algorithm described in J. Windle's PhD thesis (2013) <https://repositories.lib.utexas.edu/bitstream/handle/2152/21842/WINDLE-DISSERTATION-2013.pdf>. The underlying implementation is in C.
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2025-04-22 |
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r-pgbart
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The Particle Gibbs sampler and Gibbs/Metropolis-Hastings sampler were implemented to fit Bayesian additive regression tree model. Construction of the model (training) and prediction for a new data set (testing) can be separated. Our reference papers are: Lakshminarayanan B, Roy D, Teh Y W. Particle Gibbs for Bayesian additive regression trees[C], Artificial Intelligence and Statistics. 2015: 553-561, <http://proceedings.mlr.press/v38/lakshminarayanan15.pdf> and Chipman, H., George, E., and McCulloch R. (2010) Bayesian Additive Regression Trees. The Annals of Applied Statistics, 4,1, 266-298, <doi:10.1214/09-aoas285>.
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2025-04-22 |
