Create surface forms from matrix or raster data for flexible plotting and conversion to other mesh types. The functions quadmesh or triangmesh produce a continuous surface as a mesh3d object as used by the rgl package. This is used for plotting raster data in 3D (optionally with texture), and allows the application of a map projection without data loss and many processing applications that are restricted by inflexible regular grid rasters. There are discrete forms of these continuous surfaces available with dquadmesh and dtriangmesh functions.

This package provides a system to implement the Q-Q boxplot. It is implemented as an extension to ggplot2'. The Q-Q boxplot is an amalgam of the boxplot and the Q-Q plot and allows the user to rapidly examine summary statistics and tail behavior for multiple distributions in the same pane. As an extension of the ggplot2 implementation of the boxplot, possible modifications to the boxplot extend to the Q-Q boxplot.

This package provides a collection of (wrapper) functions the creator found useful for quickly placing data summaries and formatted regression results into .Rnw or .Rmd files. Functions for generating commonly used graphics, such as receiver operating curves or Bland-Altman plots, are also provided by qwraps2'. qwraps2 is a updated version of a package qwraps'. The original version qwraps was never submitted to CRAN but can be found at <https://github.com/dewittpe/qwraps/>. The implementation and limited scope of the functions within qwraps2 <https://github.com/dewittpe/qwraps2/> is fundamentally different from qwraps'.

This package implements moving-blocks bootstrap and extended tapered-blocks bootstrap, as well as smooth versions of each, for quantile regression in time series. This package accompanies the paper: Gregory, K. B., Lahiri, S. N., & Nordman, D. J. (2018). A smooth block bootstrap for quantile regression with time series. The Annals of Statistics, 46(3), 1138-1166.

Finding hidden clusters in structured data can be hindered by the presence of masking variables. If not detected, masking variables are used to calculate the overall similarities between units, and therefore the cluster attribution is more imprecise. The algorithm q-vars implements an optimization method to find the variables that most separate units between clusters. In this way, masking variables can be discarded from the data frame and the clustering is more accurate. Tests can be found in Benati et al.(2017) <doi:10.1080/01605682.2017.1398206>.

This package provides functionality for working with raster-like quadtrees (also called â region quadtreesâ ), which allow for variable-sized cells. The package allows for flexibility in the quadtree creation process. Several functions defining how to split and aggregate cells are provided, and custom functions can be written for both of these processes. In addition, quadtrees can be created using other quadtrees as â templatesâ , so that the new quadtree's structure is identical to the template quadtree. The package also includes functionality for modifying quadtrees, querying values, saving quadtrees to a file, and calculating least-cost paths using the quadtree as a resistance surface.

This package provides several methods for computing the Quantile Treatment Effect (QTE) and Quantile Treatment Effect on the Treated (QTT). The main cases covered are (i) Treatment is randomly assigned, (ii) Treatment is as good as randomly assigned after conditioning on some covariates (also called conditional independence or selection on observables) using the methods developed in Firpo (2007) <doi:10.1111/j.1468-0262.2007.00738.x>, (iii) Identification is based on a Difference in Differences assumption (several varieties are available in the package e.g. Athey and Imbens (2006) <doi:10.1111/j.1468-0262.2006.00668.x> Callaway and Li (2019) <doi:10.3982/QE935>, Callaway, Li, and Oka (2018) <doi:10.1016/j.jeconom.2018.06.008>).

Various quantile-based clustering algorithms: algorithm CU (Common theta and Unscaled variables), algorithm CS (Common theta and Scaled variables through lambda_j), algorithm VU (Variable-wise theta_j and Unscaled variables) and algorithm VW (Variable-wise theta_j and Scaled variables through lambda_j). Hennig, C., Viroli, C., Anderlucci, L. (2019) "Quantile-based clustering." Electronic Journal of Statistics. 13 (2) 4849 - 4883 <doi:10.1214/19-EJS1640>.

Basic functions for building parsers, with an application to PC-AXIS format files.

Non-parametric methods as local normal regression, polynomial local regression and penalized cubic B-splines regression are used to estimate quantiles curves. See Fan and Gijbels (1996) <doi:10.1201/9780203748725> and Perperoglou et al.(2019) <doi:10.1186/s12874-019-0666-3>.

The new QOI file format offers a very simple but efficient image compression algorithm. This package provides an easy and simple way to read, write and display bitmap images stored in the QOI (Quite Ok Image) format. It can read and write both files and in-memory raw vectors.

Select optimal functional regression or dichotomized quantile predictors for survival/logistic/numeric outcome and perform optimistic bias correction for any optimally dichotomized numeric predictor(s), as in Yi, et. al. (2023) <doi:10.1016/j.labinv.2023.100158>.

This package provides a set of functions to extract results from regression models and plot the effect size using ggplot2 seamlessly. While broom is useful to convert statistical analysis objects into tidy data frames, coefplot is adept at showing multivariate regression results. With specific outcome, this package could build regression models automatically, extract results into a data frame and provide a quicker way to summarize models statistical findings using ggplot2'.

This package provides helper functions to work programmatically within a quarto document. It allows the user to create section headers, tabsets, divs, and spans, and formats these objects into quarto syntax when printed into a document.

Primarily, the qcv package computes key indices related to the Quantifying Construct Validity procedure (QCV; Westen & Rosenthal, 2003 <doi:10.1037/0022-3514.84.3.608>; see also Furr & Heuckeroth, in press). The qcv() function is the heart of the qcv package, but additional functions in the package provide useful ancillary information related to the QCV procedure.

Implementations of the quantile slice sampler of Heiner et al. (2024+, in preparation) as well as other popular slice samplers are provided. Helper functions for specifying pseudo-target distributions are included, both for diagnostics and for tuning the quantile slice sampler. Other implemented methods include the generalized elliptical slice sampler of Nishihara et al. (2014)<https://jmlr.org/papers/v15/nishihara14a.html

Evaluates moments of ratios (and products) of quadratic forms in normal variables, specifically using recursive algorithms developed by Bao and Kan (2013) <doi:10.1016/j.jmva.2013.03.002> and Hillier et al. (2014) <doi:10.1017/S0266466613000364>. Also provides distribution, quantile, and probability density functions of simple ratios of quadratic forms in normal variables with several algorithms. Originally developed as a supplement to Watanabe (2023) <doi:10.1007/s00285-023-01930-8> for evaluating average evolvability measures in evolutionary quantitative genetics, but can be used for a broader class of statistics. Generating functions for these moments are also closely related to the top-order zonal and invariant polynomials of matrix arguments.

Given a dataset, the user is invited to utilize the Empirical Cumulative Distribution Function (ECDF) to guess interactively the mean and the mean deviation. Thereafter, using the quadratic curve the user can guess the Root Mean Squared Deviation (RMSD) and visualize the standard deviation (SD). For details, see Sarkar and Rashid (2019)<doi:10.3126/njs.v3i0.25574>, Have You Seen the Standard Deviaton?, Nepalese Journal of Statistics, Vol. 3, 1-10.

An implementation of two interaction indices between extractive activity and groundwater resources based on hazard and vulnerability parameters used in the assessment of natural hazards. One index is based on a discrete choice model and the other is relying on an artificial neural network.

Implementation of the food safety restaurant grading system adopted by Public Health - Seattle & King County (see Ashwood, Z.C., Elias, B., and Ho. D.E. "Improving the Reliability of Food Safety Disclosure: A Quantile Adjusted Restaurant Grading System for Seattle-King County" (working paper)). As reported in the accompanying paper, this package allows jurisdictions to easily implement refinements that address common challenges with unadjusted grading systems. First, in contrast to unadjusted grading, where the most recent single routine inspection is the primary determinant of a grade, grading inputs are allowed to be flexible. For instance, it is straightforward to base the grade on average inspection scores across multiple inspection cycles. Second, the package can identify quantile cutoffs by inputting substantively meaningful regulatory thresholds (e.g., the proportion of establishments receiving sufficient violation points to warrant a return visit). Third, the quantile adjustment equalizes the proportion of establishments in a flexible number of grading categories (e.g., A/B/C) across areas (e.g., ZIP codes, inspector areas) to account for inspector differences. Fourth, the package implements a refined quantile adjustment that addresses two limitations with the stats::quantile() function when applied to inspection score datasets with large numbers of score ties. The quantile adjustment algorithm iterates over quantiles until, over all restaurants in all areas, grading proportions are within a tolerance of desired global proportions. In addition the package allows a modified definition of "quantile" from "Nearest Rank". Instead of requiring that at least p[1]% of restaurants receive the top grade and at least (p[1]+p[2])% of restaurants receive the top or second best grade for quantiles p, the algorithm searches for cutoffs so that as close as possible p[1]% of restaurants receive the top grade, and as close as possible to p[2]% of restaurants receive the second top grade.

Translate SQL SELECT statements into lists of R expressions.

This package provides functions to calculate Average Sample Numbers (ASN), Average Run Length (ARL1) and value of k, k1 and k2 for quality control charts under repetitive sampling as given in Aslam et al. (2014) (<DOI:10.7232/iems.2014.13.1.101>).

Execute multi-step SQL workflows by leveraging specially formatted comments to define and control execution. This enables users to mix queries, commands, and metadata within a single script. Results are returned as named objects for use in downstream workflows.

This package implements the robust algorithm for fitting finite mixture models based on quantile regression proposed by Emir et al., 2017 (unpublished).