Estimation of different quantile models, at the moment only Conditional autoregressive value at risk (CAViaR) proposed by Engle & Manganelli (2004) <doi:10.1198/073500104000000370> with also the specification proposed in Huang et al. (2009) <doi:10.1016/j.eneco.2008.12.006> and it's multivariate extension, Multi-variate multi-quantile CAViaR (MVMQ-CAViaR) proposed by White et al. (2015) <doi:10.1016/j.jeconom.2015.02.004> are available, however, in further updates, other models and extensions will be included.

This package provides a set of functions of increasing complexity allows users to (1) convert QuadKey-identified datasets, based on Microsoft's Bing Maps Tile System', into Simple Features data frames, (2) transform Simple Features data frames into rasters, and (3) process multiple Meta ('Facebook') QuadKey-identified human mobility files directly into raster files. For more details, see Dâ Andrea et al. (2024) <doi:10.21105/joss.06500>.

This package provides functions are provided that implement the use of the Fieller's formula methodology, for calculating a confidence interval for a ratio of (commonly, correlated) means. See Fieller (1954) <doi:10.1111/j.2517-6161.1954.tb00159.x>. Here, the application of primary interest is to studies of insect mortality response to increasing doses of a fumigant, or, e.g., to time in coolstorage. The formula is used to calculate a confidence interval for the dose or time required to achieve a specified mortality proportion, commonly 0.5 or 0.99. Vignettes demonstrate link functions that may be considered, checks on fitted models, and alternative choices of error family. Note in particular the betabinomial error family. See also Maindonald, Waddell, and Petry (2001) <doi:10.1016/S0925-5214(01)00082-5>.

Create quantile binned and conditional plots for Exploratory Data Analysis. The package provides several plotting functions that are all based on quantile binning. The plots are created with ggplot2 and patchwork and can be further adjusted.

Molecular descriptors and outcomes for several public domain data sets.

Estimation and inference methods for the cross-quantilogram. The cross-quantilogram is a measure of nonlinear dependence between two variables, based on either unconditional or conditional quantile functions. It can be considered an extension of the correlogram, which is a correlation function over multiple lag periods that mainly focuses on linear dependency. One can use the cross-quantilogram to detect the presence of directional predictability from one time series to another. This package provides a statistical inference method based on the stationary bootstrap. For detailed theoretical and empirical explanations, see Linton and Whang (2007) for univariate time series analysis and Han, Linton, Oka and Whang (2016) for multivariate time series analysis. The full references for these key publications are as follows: (1) Linton, O., and Whang, Y. J. (2007). The quantilogram: with an application to evaluating directional predictability. Journal of Econometrics, 141(1), 250-282 <doi:10.1016/j.jeconom.2007.01.004>; (2) Han, H., Linton, O., Oka, T., and Whang, Y. J. (2016). The cross-quantilogram: measuring quantile dependence and testing directional predictability between time series. Journal of Econometrics, 193(1), 251-270 <doi:10.1016/j.jeconom.2016.03.001>.

Provide a variety of Q-matrix validation methods for the generalized cognitive diagnosis models, including the method based on the generalized deterministic input, noisy, and gate model (G-DINA) by de la Torre (2011) <DOI:10.1007/s11336-011-9207-7> discrimination index (the GDI method) by de la Torre and Chiu (2016) <DOI:10.1007/s11336-015-9467-8>, the Hull method by Najera et al. (2021) <DOI:10.1111/bmsp.12228>, the stepwise Wald test method (the Wald method) by Ma and de la Torre (2020) <DOI:10.1111/bmsp.12156>, the multiple logistic regressionâ based Qâ matrix validation method (the MLR-B method) by Tu et al. (2022) <DOI:10.3758/s13428-022-01880-x>, the beta method based on signal detection theory by Li and Chen (2024) <DOI:10.1111/bmsp.12371> and Q-matrix validation based on relative fit index by Chen et al. (2013) <DOI:10.1111/j.1745-3984.2012.00185.x>. Different research methods and iterative procedures during Q-matrix validating are available <DOI:10.3758/s13428-024-02547-5>.

The letters qe in the package title stand for "quick and easy," alluding to the convenience goal of the package. We bring together a variety of machine learning (ML) tools from standard R packages, providing wrappers with a simple, convenient, and uniform interface.

Estimates QAPE using bootstrap procedures. The residual, parametric and double bootstrap is used. The test of normality using Cholesky decomposition is added. Y pop is defined.

Test whether equality and order constraints hold for all individuals simultaneously by comparing Bayesian mixed models through Bayes factors. A tutorial style vignette and a quickstart guide are available, via vignette("manual", "quid"), and vignette("quickstart", "quid") respectively. See Haaf and Rouder (2017) <doi:10.1037/met0000156>; Haaf, Klaassen and Rouder (2019) <doi:10.31234/osf.io/a4xu9>; and Rouder & Haaf (2021) <doi:10.5334/joc.131>.

This package provides functions for the joint analysis of Q sets of p-values obtained for the same list of items. This joint analysis is performed by querying a composite hypothesis, i.e. an arbitrary complex combination of simple hypotheses, as described in Mary-Huard et al. (2021) <doi:10.1093/bioinformatics/btab592> and De Walsche et al.(2025) <doi: 10.1093/nargab/lqaf118>. In this approach, the Q-uplet of p-values associated with each item is distributed as a multivariate mixture, where each of the 2^Q components corresponds to a specific combination of simple hypotheses. The dependence between the p-value series is considered using a Gaussian copula function. A p-value for the composite hypothesis test is derived from the posterior probabilities.

Fit quantile regression neural network models with optional left censoring, partial monotonicity constraints, generalized additive model constraints, and the ability to fit multiple non-crossing quantile functions following Cannon (2011) <doi:10.1016/j.cageo.2010.07.005> and Cannon (2018) <doi:10.1007/s00477-018-1573-6>.

This package provides an infrastructure for efficient processing of large-scale genetic and phenotypic data including core functions for: 1) fitting linear mixed models, 2) constructing marker-based genomic relationship matrices, 3) estimating genetic parameters (heritability and correlation), 4) performing genomic prediction and genetic risk profiling, and 5) single or multi-marker association analyses. Rohde et al. (2019) <doi:10.1101/503631>.

Functionality to read, recode, and transcode data as used in quantitative language comparison, specifically to deal with multilingual orthographic variation (Moran & Cysouw (2018) <doi:10.5281/zenodo.1296780>) and with the recoding of nominal data.

Extends the quadprog package to solve quadratic programs with absolute value constraints and absolute values in the objective function.

To construct a model in 2-D space from 2-D nonlinear dimension reduction data and then lift it to the high-dimensional space. Additionally, provides tools to visualise the model overlay the data in 2-D and high-dimensional space. Furthermore, provides summaries and diagnostics to evaluate the nonlinear dimension reduction layout.

Analysis of Q methodology, used to identify distinct perspectives existing within a group. This methodology is used across social, health and environmental sciences to understand diversity of attitudes, discourses, or decision-making styles (for more information, see <https://qmethod.org/>). A single function runs the full analysis. Each step can be run separately using the corresponding functions: for automatic flagging of Q-sorts (manual flagging is optional), for statement scores, for distinguishing and consensus statements, and for general characteristics of the factors. The package allows to choose either principal components or centroid factor extraction, manual or automatic flagging, a number of mathematical methods for rotation (or none), and a number of correlation coefficients for the initial correlation matrix, among many other options. Additional functions are available to import and export data (from raw *.CSV, HTMLQ and FlashQ *.CSV, PQMethod *.DAT and easy-htmlq *.JSON files), to print and plot, to import raw data from individual *.CSV files, and to make printable cards. The package also offers functions to print Q cards and to generate Q distributions for study administration. See further details in the package documentation, and in the web pages below, which include a cookbook, guidelines for more advanced analysis (how to perform manual flagging or change the sign of factors), data management, and a graphical user interface (GUI) for online and offline use.

This package provides routines to create some quaternions splines: Barry-Goldman algorithm, De Casteljau algorithm, and Kochanek-Bartels algorithm. The implementations are based on the Python library splines'. Quaternions splines allow to construct spherical curves. References: Barry and Goldman <doi:10.1145/54852.378511>, Kochanek and Bartels <doi:10.1145/800031.808575>.

This package implements quantile-based discriminant analysis (QuanDA) for imbalanced classification in high-dimensional, low-sample-size settings. The method fits penalized quantile regression directly on discrete class labels and tunes the quantile level to reflect class imbalance.

This package provides a no-frills open-source solution for designing plot labels affixed with QR codes. It features EasyQrlabelr', a BrAPI-compliant shiny app that simplifies the process of plot label design for non-R users. It builds on the methods described by Wu et al. (2020) <doi:10.1111/2041-210X.13405>.

Quasi-Cauchy quantile regression, proposed by de Oliveira, Ospina, Leiva, Figueroa-Zuniga and Castro (2023) <doi:10.3390/fractalfract7090667>. This regression model is useful for the case where you want to model data of a nature limited to the intervals [0,1], (0,1], [0,1) or (0,1) and you want to use a quantile approach.

This package implements Quantile-on-Quantile Kernel-Based Regularized Least Squares (QQKRLS) as in Adebayo, Ozkan and Eweade (2024) <doi:10.1016/j.jclepro.2024.140832>. Combines Kernel-Based Regularized Least Squares (KRLS) of Hainmueller and Hazlett (2014) <doi:10.1093/pan/mpt019> with the Quantile-on-Quantile regression of Sim and Zhou (2015) <doi:10.1016/j.jbankfin.2015.01.013>: for each quantile theta of the independent variable the response is fit by KRLS on the corresponding sub-sample and the tau-quantile of the resulting pointwise marginal effects yields beta(theta, tau). Standard errors come from a paired bootstrap. Visualisations use the MATLAB Parula colour map by default.

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>).

Automates many of the tasks associated with quantitative discourse analysis of transcripts containing discourse including frequency counts of sentence types, words, sentences, turns of talk, syllables and other assorted analysis tasks. The package provides parsing tools for preparing transcript data. Many functions enable the user to aggregate data by any number of grouping variables, providing analysis and seamless integration with other R packages that undertake higher level analysis and visualization of text. This affords the user a more efficient and targeted analysis. qdap is designed for transcript analysis, however, many functions are applicable to other areas of Text Mining/ Natural Language Processing.