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r-qpcr
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public |
Model fitting, optimal model selection and calculation of various features that are essential in the analysis of quantitative real-time polymerase chain reaction (qPCR).
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2024-01-16 |
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r-qqconf
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public |
Provides functionality for creating Quantile-Quantile (QQ) and Probability-Probability (PP) plots with simultaneous testing bands to asses significance of sample deviation from a reference distribution <doi:10.18637/jss.v106.i10>.
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2024-01-16 |
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r-qrm
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public |
Provides functions/methods to accompany the book Quantitative Risk Management: Concepts, Techniques and Tools by Alexander J. McNeil, Ruediger Frey, and Paul Embrechts.
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2024-01-16 |
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r-qgraph
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public |
Fork of qgraph - Weighted network visualization and analysis, as well as Gaussian graphical model computation. See Epskamp et al. (2012) <doi:10.18637/jss.v048.i04>.
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2024-01-16 |
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r-qrjoint
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public |
Joint estimation of quantile specific intercept and slope parameters in a linear regression setting.
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2024-01-16 |
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r-qrencoder
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public |
Quick Response codes (QR codes) are a type of matrix bar code and can be used to authenticate transactions, provide access to multi-factor authentication services and enable general data transfer in an image. QR codes use four standardized encoding modes (numeric, alphanumeric, byte/binary, and kanji) to efficiently store data. Matrix barcode generation is performed efficiently in C via the included 'libqrencoder' library created by Kentaro Fukuchi.
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2024-01-16 |
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r-qca
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public |
An extensive set of functions to perform Qualitative Comparative Analysis: crisp sets ('csQCA'), temporal ('tQCA'), multi-value ('mvQCA') and fuzzy sets ('fsQCA'), using a GUI - graphical user interface. 'QCA' is a methodology that bridges the qualitative and quantitative divide in social science research. It uses a Boolean minimization algorithm, resulting in a minimal causal configuration associated with a given phenomenon.
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2024-01-16 |
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r-qpdf
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public |
Content-preserving transformations transformations of PDF files such as split, combine, and compress. This package interfaces directly to the 'qpdf' C++ API and does not require any command line utilities. Note that 'qpdf' does not read actual content from PDF files: to extract text and data you need the 'pdftools' package.
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2024-01-16 |
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r-qgam
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public |
Smooth additive quantile regression models, fitted using the methods of Fasiolo et al. (2020) <doi:10.1080/01621459.2020.1725521>. See Fasiolo at al. (2021) <doi:10.18637/jss.v100.i09> for an introduction to the package. Differently from 'quantreg', the smoothing parameters are estimated automatically by marginal loss minimization, while the regression coefficients are estimated using either PIRLS or Newton algorithm. The learning rate is determined so that the Bayesian credible intervals of the estimated effects have approximately the correct coverage. The main function is qgam() which is similar to gam() in 'mgcv', but fits non-parametric quantile regression models.
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2024-01-16 |
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r-qif
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public |
Developed to perform the estimation and inference for regression coefficient parameters in longitudinal marginal models using the method of quadratic inference functions. Like generalized estimating equations, this method is also a quasi-likelihood inference method. It has been showed that the method gives consistent estimators of the regression coefficients even if the correlation structure is misspecified, and it is more efficient than GEE when the correlation structure is misspecified. Based on Qu, A., Lindsay, B.G. and Li, B. (2000) <doi:10.1093/biomet/87.4.823>.
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2024-01-16 |
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r-psychonetrics
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public |
Multi-group (dynamical) structural equation models in combination with confirmatory network models from cross-sectional, time-series and panel data <doi:10.31234/osf.io/8ha93>. Allows for confirmatory testing and fit as well as exploratory model search.
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2024-01-16 |
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r-purrrlyr
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public |
Some functions at the intersection of 'dplyr' and 'purrr' that formerly lived in 'purrr'.
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2024-01-16 |
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r-qcapro
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public |
Provides advanced functionality for performing configurational comparative research with Qualitative Comparative Analysis (QCA), including crisp-set, multi-value, and fuzzy-set QCA. It also offers advanced tools for sensitivity diagnostics and methodological evaluations of QCA.
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2024-01-16 |
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r-qap
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public |
Implements heuristics for the Quadratic Assignment Problem (QAP). Although, the QAP was introduced as a combinatorial optimization problem for the facility location problem in operations research, it also has many applications in data analysis. The problem is NP-hard and the package implements a simulated annealing heuristic.
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2024-01-16 |
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r-pyinit
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public |
Deterministic Pena-Yohai initial estimator for robust S estimators of regression. The procedure is described in detail in Pena, D., & Yohai, V. (1999) <doi:10.2307/2670164>.
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2024-01-16 |
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r-pwrgsd
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public |
Tools for the evaluation of interim analysis plans for sequentially monitored trials on a survival endpoint; tools to construct efficacy and futility boundaries, for deriving power of a sequential design at a specified alternative, template for evaluating the performance of candidate plans at a set of time varying alternatives. See Izmirlian, G. (2014) <doi:10.4310/SII.2014.v7.n1.a4>.
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2024-01-16 |
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r-pwrfdr
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public |
This is a package for calculating two differing notions of power, or deriving sample sizes for specified requisite power in multiple testing experiments under a variety of methods for control of the distribution of the False Discovery Proportion (FDP). More specifically, one can choose to control the FDP distribution according to control of its (i) mean, e.g. the usual BH-FDR procedure, or via the probability that it exceeds a given value, delta, via (ii) the Romano procedure, or via (iii) my procedure based upon asymptotic approximation. Likewise, we can think of the power in multiple testing experiments in terms of a summary of the distribution of the True Positive Proportion (TPP). The package will compute power, sample size or any other missing parameter required for power based upon (i) the mean of the TPP which is the average power (ii) the probability that the TPP exceeds a given value, lambda, via my asymptotic approximation procedure. The theoretical results are described in Izmirlian, G. (2020) Stat. and Prob. letters, <doi:10.1016/j.spl.2020.108713>, and an applied paper describing the methodology with a simulation study is in preparation.
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2024-01-16 |
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r-purrr
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public |
A complete and consistent functional programming toolkit for R.
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2024-01-16 |
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r-pweall
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public |
Calculates various functions needed for design and monitoring survival trials accounting for complex situations such as delayed treatment effect, treatment crossover, non-uniform accrual, and different censoring distributions between groups. The event time distribution is assumed to be piecewise exponential (PWE) distribution and the entry time is assumed to be piecewise uniform distribution. As compared with Version 1.2.1, two more types of hybrid crossover are added. A bug is corrected in the function "pwecx" that calculates the crossover-adjusted survival, distribution, density, hazard and cumulative hazard functions. Also, to generate the crossover-adjusted event time random variable, a more efficient algorithm is used and the output includes crossover indicators.
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2024-01-16 |
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r-pvar
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public |
The calculation of p-variation of the finite sample data. This package is a realisation of the procedure described in Butkus, V. & Norvaisa, R. Lith Math J (2018). <doi: 10.1007/s10986-018-9414-3> The formal definitions and reference into literature are given in vignette.
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2024-01-16 |
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r-ptw
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public |
Parametric Time Warping aligns patterns, i.e. it aims to put corresponding features at the same locations. The algorithm searches for an optimal polynomial describing the warping. It is possible to align one sample to a reference, several samples to the same reference, or several samples to several references. One can choose between calculating individual warpings, or one global warping for a set of samples and one reference. Two optimization criteria are implemented: RMS (Root Mean Square error) and WCC (Weighted Cross Correlation). Both warping of peak profiles and of peak lists are supported. A vignette for the latter is contained in the inst/doc directory of the source package - the vignette source can be found on the package github site.
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2024-01-16 |
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r-proxyc
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public |
Computes proximity between rows or columns of large matrices efficiently in C++. Functions are optimised for large sparse matrices using the Armadillo and Intel TBB libraries. Among several built-in similarity/distance measures, computation of correlation, cosine similarity and Euclidean distance is particularly fast.
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2024-01-16 |
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r-pullword
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R Interface to Pullword Service for natural language processing in Chinese. It enables users to extract valuable words from text by deep learning models. For more details please visit the official site (in Chinese) <http://www.pullword.com/>.
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2024-01-16 |
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r-psychotools
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public |
Infrastructure for psychometric modeling such as data classes (for item response data and paired comparisons), basic model fitting functions (for Bradley-Terry, Rasch, parametric logistic IRT, generalized partial credit, rating scale, multinomial processing tree models), extractor functions for different types of parameters (item, person, threshold, discrimination, guessing, upper asymptotes), unified inference and visualizations, and various datasets for illustration. Intended as a common lightweight and efficient toolbox for psychometric modeling and a common building block for fitting psychometric mixture models in package "psychomix" and trees based on psychometric models in package "psychotree".
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2024-01-16 |
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r-ptest
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public |
Implements p-value computations using an approximation to the cumulative distribution function for a variety of tests for periodicity. These tests include harmonic regression tests with normal and double exponential errors as well as modifications of Fisher's g test. An accompanying vignette illustrates the application of these tests.
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2024-01-16 |
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r-prophet
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public |
Implements a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
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2024-01-16 |
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r-pspline
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None |
Smoothing splines with penalties on order m derivatives.
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2024-01-16 |
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r-pscl
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public |
Bayesian analysis of item-response theory (IRT) models, roll call analysis; computing highest density regions; maximum likelihood estimation of zero-inflated and hurdle models for count data; goodness-of-fit measures for GLMs; data sets used in writing and teaching at the Political Science Computational Laboratory; seats-votes curves.
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2024-01-16 |
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r-pspearman
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public |
Spearman's rank correlation test with precomputed exact null distribution for n <= 22.
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2024-01-16 |
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r-ps
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public |
List, query and manipulate all system processes, on 'Windows', 'Linux' and 'macOS'.
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2024-01-16 |
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r-projpred
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public |
Performs projection predictive feature selection for generalized linear models (Piironen, Paasiniemi, and Vehtari, 2020, <doi:10.1214/20-EJS1711>) with or without multilevel or additive terms (Catalina, Bürkner, and Vehtari, 2022, <https://proceedings.mlr.press/v151/catalina22a.html>), for some ordinal and nominal regression models (Weber, Glass, and Vehtari, 2023, <arXiv:2301.01660>), and for many other regression models (using the latent projection by Catalina, Bürkner, and Vehtari, 2021, <arXiv:2109.04702>, which can also be applied to most of the former models). The package is compatible with the 'rstanarm' and 'brms' packages, but other reference models can also be used. See the vignettes and the documentation for more information and examples.
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2024-01-16 |
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r-pryr
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public |
Useful tools to pry back the covers of R and understand the language at a deeper level.
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2024-01-16 |
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r-prsr
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public |
Tests periodicity in short time series using response surface regression.
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2024-01-16 |
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r-proxy
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public |
Provides an extensible framework for the efficient calculation of auto- and cross-proximities, along with implementations of the most popular ones.
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2024-01-16 |
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r-protviz
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public |
Helps with quality checks, visualizations and analysis of mass spectrometry data, coming from proteomics experiments. The package is developed, tested and used at the Functional Genomics Center Zurich <https://fgcz.ch>. We use this package mainly for prototyping, teaching, and having fun with proteomics data. But it can also be used to do data analysis for small scale data sets.
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2024-01-16 |
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r-prospectr
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public |
Functions to preprocess spectroscopic data and conduct (representative) sample selection/calibration sampling.
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2024-01-16 |
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r-protoclust
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public |
Performs minimax linkage hierarchical clustering. Every cluster has an associated prototype element that represents that cluster as described in Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering with Prototypes via Minimax Linkage," The Journal of the American Statistical Association, 106(495), 1075-1084.
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2024-01-16 |
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r-procmaps
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public |
Portable '/proc/self/maps' as a data frame. Determine which library or other region is mapped to a specific address of a process. -- R packages can contain native code, compiled to shared libraries at build or installation time. When loaded, each shared library occupies a portion of the address space of the main process. When only a machine instruction pointer is available (e.g. from a backtrace during error inspection or profiling), the address space map determines which library this instruction pointer corresponds to.
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2024-01-16 |
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r-promises
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public |
Provides fundamental abstractions for doing asynchronous programming in R using promises. Asynchronous programming is useful for allowing a single R process to orchestrate multiple tasks in the background while also attending to something else. Semantics are similar to 'JavaScript' promises, but with a syntax that is idiomatic R.
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2024-01-16 |
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r-processmapr
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public |
Visualize event logs using directed graphs, i.e. process maps. Part of the 'bupaR' framework.
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2024-01-16 |
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r-proj4
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public |
A simple interface to lat/long projection and datum transformation of the PROJ.4 cartographic projections library. It allows transformation of geographic coordinates from one projection and/or datum to another.
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2024-01-16 |
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r-proj
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public |
Currently non-operational, a harmless wrapper to allow package 'reproj' to install and function while relying on the 'proj4' package.
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2024-01-16 |
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r-profvis
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public |
Interactive visualizations for profiling R code.
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2024-01-16 |
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r-prodlim
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public |
Fast and user friendly implementation of nonparametric estimators for censored event history (survival) analysis. Kaplan-Meier and Aalen-Johansen method.
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2024-01-16 |
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r-prioritizr
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public |
Systematic conservation prioritization using mixed integer linear programming (MILP). It provides a flexible interface for building and solving conservation planning problems. Once built, conservation planning problems can be solved using a variety of commercial and open-source exact algorithm solvers. By using exact algorithm solvers, solutions can be generated that are guaranteed to be optimal (or within a pre-specified optimality gap). Furthermore, conservation problems can be constructed to optimize the spatial allocation of different management actions or zones, meaning that conservation practitioners can identify solutions that benefit multiple stakeholders. To solve large-scale or complex conservation planning problems, users should install the Gurobi optimization software (available from <https://www.gurobi.com/>) and the 'gurobi' R package (see Gurobi Installation Guide vignette for details). Users can also install the IBM CPLEX software (<https://www.ibm.com/products/ilog-cplex-optimization-studio/cplex-optimizer>) and the 'cplexAPI' R package (available at <https://github.com/cran/cplexAPI>). Additionally, the 'rcbc' R package (available at <https://github.com/dirkschumacher/rcbc>) can be used to generate solutions using the CBC optimization software (<https://github.com/coin-or/Cbc>).
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2024-01-16 |
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r-premium
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Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership. The package allows Bernoulli, Binomial, Poisson, Normal, survival and categorical response, as well as Normal and discrete covariates. It also allows for fixed effects in the response model, where a spatial CAR (conditional autoregressive) term can be also included. Additionally, predictions may be made for the response, and missing values for the covariates are handled. Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence. A number of R functions for post-processing of the output are also provided. In addition to fitting mixtures, it may additionally be of interest to determine which covariates actively drive the mixture components. This is implemented in the package as variable selection. The main reference for the package is Liverani, Hastie, Azizi, Papathomas and Richardson (2015) <doi:10.18637/jss.v064.i07>.
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2024-01-16 |
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r-processx
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public |
Tools to run system processes in the background. It can check if a background process is running; wait on a background process to finish; get the exit status of finished processes; kill background processes. It can read the standard output and error of the processes, using non-blocking connections. 'processx' can poll a process for standard output or error, with a timeout. It can also poll several processes at once.
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2024-01-16 |
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r-prefmod
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public |
Generates design matrix for analysing real paired comparisons and derived paired comparison data (Likert type items/ratings or rankings) using a loglinear approach. Fits loglinear Bradley-Terry model (LLBT) exploiting an eliminate feature. Computes pattern models for paired comparisons, rankings, and ratings. Some treatment of missing values (MCAR and MNAR). Fits latent class (mixture) models for paired comparison, rating and ranking patterns using a non-parametric ML approach.
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2024-01-16 |
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r-proc
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None |
Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.
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2024-01-16 |
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r-princurve
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public |
Fitting a principal curve to a data matrix in arbitrary dimensions. Hastie and Stuetzle (1989) <doi:10.2307/2289936>.
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2024-01-16 |
