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r-qgam
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Smooth additive quantile regression models, fitted using the methods of Fasiolo et al. (2017) <arXiv:1707.03307>. 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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2025-04-22 |
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r-qcapro
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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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2025-04-22 |
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r-qap
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Implements heuristics for the Quadratic Assignment Problem (QAP). Currently only a simulated annealing heuristic is available.
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2025-04-22 |
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r-pyinit
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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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2025-04-22 |
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r-pwrgsd
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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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2025-04-22 |
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r-pwrfdr
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This is a package for calculating power and sample size in multiple testing situations using the Benjamini-Hochberg (BH) false discovery rate (FDR) procedure. The package computes power and sample size in one of either two ways, using the average power or the lambda-power. See Izmirlian, G. (2018) <arXiv:1801.03989>.
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2025-04-22 |
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r-pweall
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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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2025-04-22 |
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r-pwd
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Contains functions which allow the user to perform time series regression quickly using the Power Weighted Densities (PWD) approach. alphahat_LR_one_Rcpp() is the main workhorse function within this package.
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2025-04-22 |
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r-pvar
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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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2025-04-22 |
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r-squash
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Functions for color-based visualization of multivariate data, i.e. colorgrams or heatmaps. Lower-level functions map numeric values to colors, display a matrix as an array of colors, and draw color keys. Higher-level plotting functions generate a bivariate histogram, a dendrogram aligned with a color-coded matrix, a triangular distance matrix, and more.
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2025-04-22 |
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r-sqrl
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Facilitates interaction with ODBC data sources by managing communications on behalf of the user. Supports multi-statement SQL scripts, with or without embedded R.
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2025-04-22 |
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r-sqlutils
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This package provides utilities for working with a library of SQL files.
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2025-04-22 |
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r-sqlrender
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A rendering tool for parameterized SQL that also translates into different SQL dialects. These dialects include 'Microsoft Sql Server', 'Oracle', 'PostgreSql', 'Amazon RedShift', 'Apache Impala', 'IBM Netezza', 'Google BigQuery', 'Microsoft PDW', and 'SQLite'.
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2025-04-22 |
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r-spyvsspy
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Data on the Spy vs. Spy comic strip of Mad magazine, created and written by Antonio Prohias.
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2025-04-22 |
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r-spurs
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Provides functions and datasets from Jones, O.D., R. Maillardet, and A.P. Robinson. 2014. An Introduction to Scientific Programming and Simulation, Using R. 2nd Ed. Chapman And Hall/CRC.
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2025-04-22 |
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r-spsl
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Provides basic functionality for labeling iso- & anisotropic percolation clusters on 2D & 3D square lattices with various lattice sizes, occupation probabilities, von Neumann & Moore (1,d)-neighborhoods, and random variables weighting the percolation lattice sites.
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2025-04-22 |
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r-sprt
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Perform Wald's Sequential Probability Ratio Test on variables with a Normal, Bernoulli, Exponential and Poisson distribution. Plot acceptance and continuation regions, or create your own with the help of closures.
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2025-04-22 |
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r-sprsmdl
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R functions to mine sparse models from data.
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2025-04-22 |
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r-spreda
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The Statistical Package for REliability Data Analysis (SPREDA) implements recently-developed statistical methods for the analysis of reliability data. Modern technological developments, such as sensors and smart chips, allow us to dynamically track product/system usage as well as other environmental variables, such as temperature and humidity. We refer to these variables as dynamic covariates. The package contains functions for the analysis of time-to-event data with dynamic covariates and degradation data with dynamic covariates. The package also contains functions that can be used for analyzing time-to-event data with right censoring, and with left truncation and right censoring. Financial support from NSF and DuPont are acknowledged.
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2025-04-22 |
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r-sportsanalytics
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The aim of this package is to provide infrastructure for sports analysis. Anyway, currently it is a selection of data sets, functions to fetch sports data, examples, and demos -- with the ambition to develop bit by bit a set of classes to represent general concepts of sports analysis.
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2025-04-22 |
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r-sporm
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R implementation of the methods described in "A rank-based empirical likelihood approach to two-sample proportional odds model and its goodness-of-fit" by Zhong Guan and Cheng Peng, Journal of Nonparametric Statistics, to appear.
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2025-04-22 |
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r-spongebob
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Convert text (and text in R objects) to Mocking SpongeBob case <https://knowyourmeme.com/memes/mocking-spongebob> and show them off in fun ways. CoNVErT TexT (AnD TeXt In r ObJeCtS) To MOCkINg SpoNgebOb CAsE <https://knowyourmeme.com/memes/mocking-spongebob> aND shOw tHem OFf IN Fun WayS.
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2025-04-22 |
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r-spls
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Provides functions for fitting a sparse partial least squares (SPLS) regression and classification (Chun and Keles (2010) <doi:10.1111/j.1467-9868.2009.00723.x>).
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2025-04-22 |
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r-splot
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Automates common plotting tasks to ease data exploration. Makes density plots (potentially overlaid on histograms), scatter plots with prediction lines, or bar or line plots with error bars. For each type, y, or x and y variables can be plotted at levels of other variables, all with minimal specification.
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2025-04-22 |
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r-splitstackshape
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Online data collection tools like Google Forms often export multiple-response questions with data concatenated in cells. The concat.split (cSplit) family of functions splits such data into separate cells. The package also includes functions to stack groups of columns and to reshape wide data, even when the data are "unbalanced"---something which reshape (from base R) does not handle, and which melt and dcast from reshape2 do not easily handle.
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2025-04-22 |
