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r-pcapp
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public |
Provides functions for robust PCA by projection pursuit. The methods are described in Croux et al. (2006) <doi:10.2139/ssrn.968376>, Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>.
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2024-01-16 |
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r-parzer
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Parse messy geographic coordinates from various character formats to decimal degree numeric values. Parse coordinates into their parts (degree, minutes, seconds); calculate hemisphere from coordinates; pull out individually degrees, minutes, or seconds; add and subtract degrees, minutes, and seconds. C++ code herein originally inspired from code written by Jeffrey D. Bogan, but then completely re-written.
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2024-01-16 |
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r-pbsmodelling
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Provides software to facilitate the design, testing, and operation of computer models. It focuses particularly on tools that make it easy to construct and edit a customized graphical user interface ('GUI'). Although our simplified 'GUI' language depends heavily on the R interface to the 'Tcl/Tk' package, a user does not need to know 'Tcl/Tk'. Examples illustrate models built with other R packages, including 'PBSmapping', 'PBSddesolve', and 'BRugs'. A complete user's guide 'PBSmodelling-UG.pdf' shows how to use this package effectively.
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2024-01-16 |
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r-pbv
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Computes probabilities of the bivariate normal distribution in a vectorized R function (Drezner & Wesolowsky, 1990, <doi:10.1080/00949659008811236>).
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2024-01-16 |
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r-pbsmapping
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This software has evolved from fisheries research conducted at the Pacific Biological Station (PBS) in 'Nanaimo', British Columbia, Canada. It extends the R language to include two-dimensional plotting features similar to those commonly available in a Geographic Information System (GIS). Embedded C code speeds algorithms from computational geometry, such as finding polygons that contain specified point events or converting between longitude-latitude and Universal Transverse Mercator (UTM) coordinates. Additionally, we include 'C++' code developed by Angus Johnson for the 'Clipper' library, data for a global shoreline, and other data sets in the public domain. Under the user's R library directory '.libPaths()', specifically in './PBSmapping/doc', a complete user's guide is offered and should be consulted to use package functions effectively.
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2024-01-16 |
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r-pbsddesolve
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Functions for solving systems of delay differential equations by interfacing with numerical routines written by Simon N. Wood, including contributions from Benjamin J. Cairns. These numerical routines first appeared in Simon Wood's 'solv95' program. This package includes a vignette and a complete user's guide. 'PBSddesolve' originally appeared on CRAN under the name 'ddesolve'. That version is no longer supported. The current name emphasizes a close association with other 'PBS' packages, particularly 'PBSmodelling'.
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2024-01-16 |
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r-partykit
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public |
A toolkit with infrastructure for representing, summarizing, and visualizing tree-structured regression and classification models. This unified infrastructure can be used for reading/coercing tree models from different sources ('rpart', 'RWeka', 'PMML') yielding objects that share functionality for print()/plot()/predict() methods. Furthermore, new and improved reimplementations of conditional inference trees (ctree()) and model-based recursive partitioning (mob()) from the 'party' package are provided based on the new infrastructure. A description of this package was published by Hothorn and Zeileis (2015) <https://jmlr.org/papers/v16/hothorn15a.html>.
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2024-01-16 |
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r-pbrf
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Provides three ways to estimate the probability of being in response function (PBRF) The estimates are presented in Tsai, Luo and Crowley (2017) <doi: 10.1007/978-981-10-0126-0_10>.
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2024-01-16 |
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r-pbmcapply
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A light-weight package helps you track and visualize the progress of parallel version of vectorized R functions (mc*apply). Parallelization (mc.core > 1) works only on *nix (Linux, Unix such as macOS) system due to the lack of fork() functionality, which is essential for mc*apply, on Windows.
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2024-01-16 |
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r-pbivnorm
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Provides a vectorized R function for calculating probabilities from a standard bivariate normal CDF.
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2024-01-16 |
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r-pbdzmq
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public |
'ZeroMQ' is a well-known library for high-performance asynchronous messaging in scalable, distributed applications. This package provides high level R wrapper functions to easily utilize 'ZeroMQ'. We mainly focus on interactive client/server programming frameworks. For convenience, a minimal 'ZeroMQ' library (4.2.2) is shipped with 'pbdZMQ', which can be used if no system installation of 'ZeroMQ' is available. A few wrapper functions compatible with 'rzmq' are also provided.
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2024-01-16 |
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r-paws.common
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public |
Functions for making low-level API requests to Amazon Web Services <https://aws.amazon.com>. The functions handle building, signing, and sending requests, and receiving responses. They are designed to help build higher-level interfaces to individual services, such as Simple Storage Service (S3).
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2024-01-16 |
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r-pathmapping
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public |
Functions to compute and display the area-based deviation between spatial paths and to compute a mapping based on minimizing area and distance-based cost. For details, see: Mueller, S. T., Perelman, B. S., & Veinott, E. S. (2016) <DOI:10.3758/s13428-015-0562-7>.
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2024-01-16 |
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r-patchdvi
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public |
Functions to patch specials in '.dvi' files, or entries in '.synctex' files. Works with concordance=TRUE in Sweave, knitr or R Markdown to link sources to previews.
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2024-01-16 |
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r-partitions
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Additive partitions of integers. Enumerates the partitions, unequal partitions, and restricted partitions of an integer; the three corresponding partition functions are also given. Set partitions and now compositions and riffle shuffles are included.
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2024-01-16 |
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r-parsedate
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public |
Parse dates automatically, without the need of specifying a format. Currently it includes the git date parser. It can also recognize and parse all ISO 8601 formats.
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2024-01-16 |
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r-parsermd
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An implementation of a formal grammar and parser for R Markdown documents using the Boost Spirit X3 library. It also includes a collection of high level functions for working with the resulting abstract syntax tree.
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2024-01-16 |
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r-party
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A computational toolbox for recursive partitioning. The core of the package is ctree(), an implementation of conditional inference trees which embed tree-structured regression models into a well defined theory of conditional inference procedures. This non-parametric class of regression trees is applicable to all kinds of regression problems, including nominal, ordinal, numeric, censored as well as multivariate response variables and arbitrary measurement scales of the covariates. Based on conditional inference trees, cforest() provides an implementation of Breiman's random forests. The function mob() implements an algorithm for recursive partitioning based on parametric models (e.g. linear models, GLMs or survival regression) employing parameter instability tests for split selection. Extensible functionality for visualizing tree-structured regression models is available. The methods are described in Hothorn et al. (2006) <doi:10.1198/106186006X133933>, Zeileis et al. (2008) <doi:10.1198/106186008X319331> and Strobl et al. (2007) <doi:10.1186/1471-2105-8-25>.
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2024-01-16 |
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r-paramhelpers
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public |
Functions for parameter descriptions and operations in black-box optimization, tuning and machine learning. Parameters can be described (type, constraints, defaults, etc.), combined to parameter sets and can in general be programmed on. A useful OptPath object (archive) to log function evaluations is also provided.
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2024-01-16 |
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r-paralleldist
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A fast parallelized alternative to R's native 'dist' function to calculate distance matrices for continuous, binary, and multi-dimensional input matrices, which supports a broad variety of 41 predefined distance functions from the 'stats', 'proxy' and 'dtw' R packages, as well as user- defined functions written in C++. For ease of use, the 'parDist' function extends the signature of the 'dist' function and uses the same parameter naming conventions as distance methods of existing R packages. The package is mainly implemented in C++ and leverages the 'RcppParallel' package to parallelize the distance computations with the help of the 'TinyThread' library. Furthermore, the 'Armadillo' linear algebra library is used for optimized matrix operations during distance calculations. The curiously recurring template pattern (CRTP) technique is applied to avoid virtual functions, which improves the Dynamic Time Warping calculations while the implementation stays flexible enough to support different DTW step patterns and normalization methods.
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2024-01-16 |
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r-parmigene
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Parallel estimation of the mutual information based on entropy estimates from k-nearest neighbors distances and algorithms for the reconstruction of gene regulatory networks (Sales et al, 2011 <doi:10.1093/bioinformatics/btr274>).
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2024-01-16 |
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r-parglm
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Provides a parallel estimation method for generalized linear models without compiling with a multithreaded LAPACK or BLAS.
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2024-01-16 |
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r-pagoda2
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Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos, <https://github.com/kharchenkolab/conos>. This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/pagoda2>. The size of the 'p2data' package is approximately 6 MB.
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2024-01-16 |
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r-padr
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public |
Transforms datetime data into a format ready for analysis. It offers two core functionalities; aggregating data to a higher level interval (thicken) and imputing records where observations were absent (pad).
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2024-01-16 |
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r-pander
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public |
Contains some functions catching all messages, 'stdout' and other useful information while evaluating R code and other helpers to return user specified text elements (like: header, paragraph, table, image, lists etc.) in 'pandoc' markdown or several type of R objects similarly automatically transformed to markdown format. Also capable of exporting/converting (the resulting) complex 'pandoc' documents to e.g. HTML, 'PDF', 'docx' or 'odt'. This latter reporting feature is supported in brew syntax or with a custom reference class with a smarty caching 'backend'.
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2024-01-16 |
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r-pan
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It provides functions and examples for maximum likelihood estimation for generalized linear mixed models and Gibbs sampler for multivariate linear mixed models with incomplete data, as described in Schafer JL (1997) "Imputation of missing covariates under a multivariate linear mixed model". Technical report 97-04, Dept. of Statistics, The Pennsylvania State University.
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2024-01-16 |
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r-pamr
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Some functions for sample classification in microarrays.
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2024-01-16 |
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r-oskeyring
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Aims to support all features of the system credential store, including non-portable ones. Supports 'Keychain' on 'macOS', and 'Credential Manager' on 'Windows'. See the 'keyring' package if you need a portable 'API'.
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2024-01-16 |
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r-osmdata
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Download and import of 'OpenStreetMap' ('OSM') data as 'sf' or 'sp' objects. 'OSM' data are extracted from the 'Overpass' web server (<https://overpass-api.de/>) and processed with very fast 'C++' routines for return to 'R'.
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2024-01-16 |
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r-packcircles
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public |
Algorithms to find arrangements of non-overlapping circles.
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2024-01-16 |
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r-overlap
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public |
Provides functions to fit kernel density functions to data on temporal activity patterns of animals; estimate coefficients of overlapping of densities for two species; and calculate bootstrap estimates of confidence intervals.
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2024-01-16 |
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r-optmatch
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Distance based bipartite matching using minimum cost flow, oriented to matching of treatment and control groups in observational studies ('Hansen' and 'Klopfer' 2006 <doi:10.1198/106186006X137047>). Routines are provided to generate distances from generalised linear models (propensity score matching), formulas giving variables on which to limit matched distances, stratified or exact matching directives, or calipers, alone or in combination.
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2024-01-16 |
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r-osqp
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Provides bindings to the 'OSQP' solver. The 'OSQP' solver is a numerical optimization package or solving convex quadratic programs written in 'C' and based on the alternating direction method of multipliers. See <arXiv:1711.08013> for details.
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2024-01-16 |
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r-ordinal
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Implementation of cumulative link (mixed) models also known as ordered regression models, proportional odds models, proportional hazards models for grouped survival times and ordered logit/probit/... models. Estimation is via maximum likelihood and mixed models are fitted with the Laplace approximation and adaptive Gauss-Hermite quadrature. Multiple random effect terms are allowed and they may be nested, crossed or partially nested/crossed. Restrictions of symmetry and equidistance can be imposed on the thresholds (cut-points/intercepts). Standard model methods are available (summary, anova, drop-methods, step, confint, predict etc.) in addition to profile methods and slice methods for visualizing the likelihood function and checking convergence.
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2024-01-16 |
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r-osc
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Allows distance based spatial clustering of georeferenced data by implementing the City Clustering Algorithm - CCA. Multiple versions allow clustering for a matrix, raster and single coordinates on a plain (Euclidean distance) or on a sphere (great-circle or orthodromic distance).
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2024-01-16 |
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r-orsk
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Convert odds ratio to relative risk in cohort studies with partial data information (Wang (2013) <doi:10.18637/jss.v055.i05>).
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2024-01-16 |
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r-ore
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public |
Provides an alternative to R's built-in functionality for handling regular expressions, based on the Onigmo library. Offers first-class compiled regex objects, partial matching and function-based substitutions, amongst other features.
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2024-01-16 |
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r-openxlsx2
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public |
Simplifies the creation of 'xlsx' files by providing a high level interface to writing, styling and editing worksheets.
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2024-01-16 |
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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 (2021) <doi:10.18637/jss.v099.i06>.
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2024-01-16 |
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r-ordinalgmifs
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Provides a function for fitting cumulative link, adjacent category, forward and backward continuation ratio, and stereotype ordinal response models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method.
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2024-01-16 |
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r-ordinalclust
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Ordinal data classification, clustering and co-clustering using model-based approach with the BOS distribution for ordinal data (Christophe Biernacki and Julien Jacques (2016) <doi:10.1007/s11222-015-9585-2>).
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2024-01-16 |
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r-orca
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Implements orbit counting using a fast combinatorial approach. Counts orbits of nodes and edges from edge matrix or data frame, or a graph object from the graph package.
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2024-01-16 |
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r-opusminer
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Provides a simple R interface to the OPUS Miner algorithm (implemented in C++) for finding the top-k productive, non-redundant itemsets from transaction data. The OPUS Miner algorithm uses the OPUS search algorithm to efficiently discover the key associations in transaction data, in the form of self-sufficient itemsets, using either leverage or lift. See <http://i.giwebb.com/index.php/research/association-discovery/> for more information in relation to the OPUS Miner algorithm.
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2024-01-16 |
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r-openmx
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Create structural equation models that can be manipulated programmatically. Models may be specified with matrices or paths (LISREL or RAM) Example models include confirmatory factor, multiple group, mixture distribution, categorical threshold, modern test theory, differential Fit functions include full information maximum likelihood, maximum likelihood, and weighted least squares. equations, state space, and many others. Support and advanced package binaries available at <http://openmx.ssri.psu.edu>. The software is described in Neale, Hunter, Pritikin, Zahery, Brick, Kirkpatrick, Estabrook, Bates, Maes, & Boker (2016) <doi:10.1007/s11336-014-9435-8>.
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2024-01-16 |
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r-optimization
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Flexible optimizer with numerous input specifications for detailed parameterisation. Designed for complex loss functions with state and parameter space constraints. Visualization tools for validation and analysis of the convergence are included.
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2024-01-16 |
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r-opthedging
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Estimation of value and hedging strategy of call and put options, based on optimal hedging and Monte Carlo method, from Chapter 3 of 'Statistical Methods for Financial Engineering', by Bruno Remillard, CRC Press, (2013).
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2024-01-16 |
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r-opera
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Misc methods to form online predictions, for regression-oriented time-series, by combining a finite set of forecasts provided by the user. See Cesa-Bianchi and Lugosi (2006) <doi:10.1017/CBO9780511546921> for an overview.
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2024-01-16 |
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r-openxlsx
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Simplifies the creation of Excel .xlsx files by providing a high level interface to writing, styling and editing worksheets. Through the use of 'Rcpp', read/write times are comparable to the 'xlsx' and 'XLConnect' packages with the added benefit of removing the dependency on Java.
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2024-01-16 |
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r-openimager
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Incorporates functions for image preprocessing, filtering and image recognition. The package takes advantage of 'RcppArmadillo' to speed up computationally intensive functions. The histogram of oriented gradients descriptor is a modification of the 'findHOGFeatures' function of the 'SimpleCV' computer vision platform, the average_hash(), dhash() and phash() functions are based on the 'ImageHash' python library. The Gabor Feature Extraction functions are based on 'Matlab' code of the paper, "CloudID: Trustworthy cloud-based and cross-enterprise biometric identification" by M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, Expert Systems with Applications, vol. 42, no. 21, pp. 7905-7916, 2015, <doi:10.1016/j.eswa.2015.06.025>. The 'SLIC' and 'SLICO' superpixel algorithms were explained in detail in (i) "SLIC Superpixels Compared to State-of-the-art Superpixel Methods", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274-2282, May 2012, <doi:10.1109/TPAMI.2012.120> and (ii) "SLIC Superpixels", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, EPFL Technical Report no. 149300, June 2010.
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2024-01-16 |
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r-openair
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Tools to analyse, interpret and understand air pollution data. Data are typically regular time series and air quality measurement, meteorological data and dispersion model output can be analysed. The package is described in Carslaw and Ropkins (2012, <doi:10.1016/j.envsoft.2011.09.008>) and subsequent papers.
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2024-01-16 |
