This package provides functions for estimating the potential dispersal of tree species using regeneration densities and dispersal distances to nearest seed trees. A quantile regression is implemented to determine the dispersal potential. Spatial prediction can be used to identify natural regeneration potential for forest restoration as described in Axer et al (2021) <doi:10.1016/j.foreco.2020.118802>.

Calculates the right-tail probability of quadratic forms of Gaussian variables using the skewness-kurtosis ratio matching method, modified Liu-Tang-Zhang method and Satterthwaite-Welch method. The technical details can be found in Hong Zhang, Judong Shen and Zheyang Wu (2020) <arXiv:2005.00905>.

Simulating and estimating peer effect models including the quantile-based specification (Houndetoungan, 2025 <doi:10.48550/arXiv.2506.12920>), and the models with Constant Elasticity of Substitution (CES)-based social norm (Boucher et al., 2024 <doi:10.3982/ECTA21048>).

Computes noncompartmental pharmacokinetic parameters for drug concentration profiles. For each profile, data imputations and adjustments are made as necessary and basic parameters are estimated. Supports single dose, multi-dose, and multi-subject data. Supports steady-state calculations and various routes of drug administration. See ?qpNCA and vignettes. Methodology follows Rowland and Tozer (2011, ISBN:978-0-683-07404-8), Gabrielsson and Weiner (1997, ISBN:978-91-9765-100-4), and Gibaldi and Perrier (1982, ISBN:978-0824710422).

This package 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.

This package provides a high-level wrapper that simplifies text classification into three streamlined steps: preprocessing, model training, and prediction. It unifies the interface for multiple algorithms (including glmnet', ranger', and xgboost') and vectorization methods (Bag-of-Words, Term Frequency-Inverse Document Frequency (TF-IDF)), allowing users to go from raw text to a trained sentiment model in two function calls. The resulting model artifact automatically handles preprocessing for new datasets in the third step, ensuring consistent prediction pipelines.

This package provides functions for quickly writing (and reading back) a data.frame to file in SQLite format. The name stands for *Store Tables using SQLite'*, or alternatively for *Quick Store Tables* (either way, it could be pronounced as *Quest*). For data.frames containing the supported data types it is intended to work as a drop-in replacement for the write_*() and read_*() functions provided by similar packages.

This function aims to calculate risk of developing cardiovascular disease of individual patients in next 10 years. This unofficial package was based on published open-sourced free risk prediction algorithm QRISK3-2017 <https://qrisk.org/src.php>.

It provides versatile tools for analysis of birth and death based Markovian Queueing Models and Single and Multiclass Product-Form Queueing Networks. It implements M/M/1, M/M/c, M/M/Infinite, M/M/1/K, M/M/c/K, M/M/c/c, M/M/1/K/K, M/M/c/K/K, M/M/c/K/m, M/M/Infinite/K/K, Multiple Channel Open Jackson Networks, Multiple Channel Closed Jackson Networks, Single Channel Multiple Class Open Networks, Single Channel Multiple Class Closed Networks and Single Channel Multiple Class Mixed Networks. Also it provides a B-Erlang, C-Erlang and Engset calculators. This work is dedicated to the memory of D. Sixto Rios Insua.

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>.

For QTL mapping, this package comprises several functions designed to execute diverse tasks, such as simulating or analyzing data, calculating significance thresholds, and visualizing QTL mapping results. The single-QTL or multiple-QTL method, which enables the fitting and comparison of various statistical models, is employed to analyze the data for estimating QTL parameters. The models encompass linear regression, permutation tests, normal mixture models, and truncated normal mixture models. The Gaussian stochastic process is utilized to compute significance thresholds for QTL detection on a genetic linkage map within experimental populations. Two types of data, complete genotyping, and selective genotyping data from various experimental populations, including backcross, F2, recombinant inbred (RI) populations, and advanced intercrossed (AI) populations, are considered in the QTL mapping analysis. For QTL hotspot detection, statistical methods can be developed based on either utilizing individual-level data or summarized data. We have proposed a statistical framework capable of handling both individual-level data and summarized QTL data for QTL hotspot detection. Our statistical framework can overcome the underestimation of thresholds resulting from ignoring the correlation structure among traits. Additionally, it can identify different types of hotspots with minimal computational cost during the detection process. Here, we endeavor to furnish the R codes for our QTL mapping and hotspot detection methods, intended for general use in genes, genomics, and genetics studies. The QTL mapping methods for the complete and selective genotyping designs are based on the multiple interval mapping (MIM) model proposed by Kao, C.-H. , Z.-B. Zeng and R. D. Teasdale (1999) <doi: 10.1534/genetics.103.021642> and H.-I Lee, H.-A. Ho and C.-H. Kao (2014) <doi: 10.1534/genetics.114.168385>, respectively. The QTL hotspot detection analysis is based on the method by Wu, P.-Y., M.-.H. Yang, and C.-H. Kao (2021) <doi: 10.1093/g3journal/jkab056>.

Implementation of a computationally efficient method for simulating queues with arbitrary arrival and service times. Please see Ebert, Wu, Mengersen & Ruggeri (2020, <doi:10.18637/jss.v095.i05>) for further details.

The approach is based on the closed testing procedure to control familywise error rate in a strong sense. The local tests implemented are Wald-type and rank-score. The method is described in De Santis, et al., (2026), <doi:10.48550/arXiv.2511.07999>.

The computation of quadratic form (QF) distributions is often not trivial and it requires numerical routines. The package contains functions aimed at evaluating the exact distribution of quadratic forms (QFs) and ratios of QFs. In particular, we propose to evaluate density, quantile and distribution functions of positive definite QFs and ratio of independent positive QFs by means of an algorithm based on the numerical inversion of Mellin transforms.

Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts.

Qiita is a technical knowledge sharing and collaboration platform for programmers. See <https://qiita.com/api/v2/docs> for more information.

Datasets for the book, A Guide to QTL Mapping with R/qtl. Broman and Sen (2009) <doi:10.1007/978-0-387-92125-9>.

This package provides functions for simulation, estimation, and model selection of finite mixtures of Tukey g-and-h distributions.

Scaling models and classifiers for sparse matrix objects representing textual data in the form of a document-feature matrix. Includes original implementations of Laver', Benoit', and Garry's (2003) <doi:10.1017/S0003055403000698>, Wordscores model, the Perry and Benoit (2017) <doi:10.48550/arXiv.1710.08963> class affinity scaling model, and the Slapin and Proksch (2008) <doi:10.1111/j.1540-5907.2008.00338.x> wordfish model, as well as methods for correspondence analysis, latent semantic analysis, and fast Naive Bayes and linear SVMs specially designed for sparse textual data.

Set of functions to make the processing and analysis of surveys easier : interactive shiny apps and addins for data recoding, contingency tables, dataset metadata handling, and several convenience functions.

Compute various quantitative genetics parameters from a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the observed phenotypic mean, phenotypic variance and additive genetic variance.

Estimate quadratic vector autoregression models with the strong hierarchy using the Regularization Algorithm under Marginality Principle (RAMP) by Hao et al. (2018) <doi:10.1080/01621459.2016.1264956>, compare the performance with linear models, and construct networks with partial derivatives.

Full text, in data frames containing one row per verse, of the Qur'an in Arabic (with and without vowels) and in English (the Yusuf Ali and Saheeh International translations), formatted to be convenient for text analysis.

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