This package provides a quantitative and automated tool to extract (palaeo)biological information (i.e., measurements, velocities, similarity metrics, etc.) from the analysis of tetrapod trackways. Methods implemented in the package draw from several sources, including Alexander (1976) <doi:10.1038/261129a0>, Batschelet (1981, ISBN:9780120810505), Benhamou (2004) <doi:10.1016/j.jtbi.2004.03.016>, Bovet and Benhamou (1988) <doi:10.1016/S0022-5193(88)80038-9>, Cheung et al. (2007) <doi:10.1007/s00422-007-0158-0>, Cheung et al. (2008) <doi:10.1007/s00422-008-0251-z>, Cleasby et al. (2019) <doi:10.1007/s00265-019-2761-1>, Farlow et al. (1981) <doi:10.1038/294747a0>, Ostrom (1972) <doi:10.1016/0031-0182(72)90049-1>, Rohlf (2008) <https://sbmorphometrics.org/>, Rohlf (2009) <https://sbmorphometrics.org/>, Ruiz and Torices (2013) <doi:10.1080/10420940.2012.759115>, Scrucca et al. (2016) <doi:10.32614/RJ-2016-021>, Thulborn and Wade (1984) <https://www.museum.qld.gov.au/collections-and-research/memoirs/nature-21/mqm-n21-2-11-thulborn-wade>.

Upload raster data and easily create interactive quantitative risk analysis QRA visualizations. Select from numerous color palettes, base-maps, and different configurations.

Enables the user to calculate Value at Risk (VaR) and Expected Shortfall (ES) by means of various types of historical simulation. Currently plain-, age-, volatility-weighted- and filtered historical simulation are implemented in this package. Volatility weighting can be carried out via an exponentially weighted moving average model (EWMA) or other GARCH-type models. The performance can be assessed via Traffic Light Test, Coverage Tests and Loss Functions. The methods of the package are described in Gurrola-Perez, P. and Murphy, D. (2015) <https://EconPapers.repec.org/RePEc:boe:boeewp:0525> as well as McNeil, J., Frey, R., and Embrechts, P. (2015) <https://ideas.repec.org/b/pup/pbooks/10496.html>.

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.

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.

Helper functions for Qualitative Comparative Analysis: evaluate and plot Boolean formulae on fuzzy set score data, apply Boolean operations, compute consistency and coverage measures.

Researchers working with Qualitative Comparative Analysis (QCA) can use the package to estimate power of a sufficient term using permutation tests. A term can be anything: A condition, conjunction or disjunction of any combination of these. The package further allows users to plot the estimation results and to estimate the number of cases required to achieve a certain level of power, given a prespecified null and alternative hypothesis. Reference for the article introducing power estimation for QCA is: Rohlfing, Ingo (2018) <doi:10.1017/pan.2017.30> (ungated version: <doi:10.17605/OSF.IO/PC4DF>).

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>.

This package provides a collection of functions is provided by this package to fit quantiles regression models for censored residual lifetimes. It provides various options for regression parameters estimation: the induced smoothing approach (smooth), and L1-minimization (non-smooth). It also implements the estimation methods for standard errors of the regression parameters estimates based on an efficient partial multiplier bootstrap method and robust sandwich estimator. Furthermore, a simultaneous procedure of estimating regression parameters and their standard errors via an iterative updating procedure is implemented (iterative). For more details, see Kim, K. H., Caplan, D. J., & Kang, S. (2022), "Smoothed quantile regression for censored residual life", Computational Statistics, 1-22 <doi:10.1007/s00180-022-01262-z>.

Manages, builds and computes statistics and datasets for the construction of quarterly (sub-annual) life tables by exploiting micro-data from either a general or an insured population. References: Pavà a and Lledó (2022) <doi:10.1111/rssa.12769>. Pavà a and Lledó (2023) <doi:10.1017/asb.2023.16>. Pavà a and Lledó (2025) <doi:10.1371/journal.pone.0315937>. Acknowledgements: The authors wish to thank Conselleria de Educación, Universidades y Empleo, Generalitat Valenciana (grants AICO/2021/257; CIAICO/2024/031), Ministerio de Ciencia e Innovación (grant PID2021-128228NB-I00) and Fundación Mapfre (grant Modelización espacial e intra-anual de la mortalidad en España. Una herramienta automática para el calculo de productos de vida') for supporting this research.

This package provides the function qqtest which incorporates uncertainty in its qqplot display(s) so that the user might have a better sense of the evidence against the specified distributional hypothesis. qqtest draws a quantile quantile plot for visually assessing whether the data come from a test distribution that has been defined in one of many ways. The vertical axis plots the data quantiles, the horizontal those of a test distribution. The default behaviour generates 1000 samples from the test distribution and overlays the plot with shaded pointwise interval estimates for the ordered quantiles from the test distribution. A small number of independently generated exemplar quantile plots can also be overlaid. Both the interval estimates and the exemplars provide different comparative information to assess the evidence provided by the qqplot for or against the hypothesis that the data come from the test distribution (default is normal or gaussian). Finally, a visual test of significance (a lineup plot) can also be displayed to test the null hypothesis that the data come from the test distribution.

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>.

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.

This package implements the Bayesian quantile regression model for binary longitudinal data (QBLD) developed in Rahman and Vossmeyer (2019) <DOI:10.1108/S0731-90532019000040B009>. The model handles both fixed and random effects and implements both a blocked and an unblocked Gibbs sampler for posterior inference.

We implement an adaptation of Jiang & Zeng's (1995) <https://www.genetics.org/content/140/3/1111> likelihood ratio test for testing the null hypothesis of pleiotropy against the alternative hypothesis, two separate quantitative trait loci. The test differs from that in Jiang & Zeng (1995) <https://www.genetics.org/content/140/3/1111> and that in Tian et al. (2016) <doi:10.1534/genetics.115.183624> in that our test accommodates multiparental populations.

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.

This package provides a range of quadratic forms are evaluated, using efficient methods. Unnecessary transposes are not performed. Complex values are handled consistently.

Densitometric evaluation of the photo-archived quantitative thin-layer chromatography (TLC) plates.

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>).

Property based testing, inspired by the original QuickCheck'. This package builds on the property based testing framework provided by hedgehog and is designed to seamlessly integrate with testthat'.

Offers a suite of functions to prepare questionnaire data for analysis (perhaps other types of data as well). By data preparation, I mean data analytic tasks to get your raw data ready for statistical modeling (e.g., regression). There are functions to investigate missing data, reshape data, validate responses, recode variables, score questionnaires, center variables, aggregate by groups, shift scores (i.e., leads or lags), etc. It provides functions for both single level and multilevel (i.e., grouped) data. With a few exceptions (e.g., ncases()), functions without an "s" at the end of their primary word (e.g., center_by()) act on atomic vectors, while functions with an "s" at the end of their primary word (e.g., centers_by()) act on multiple columns of a data.frame.

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.

The queueing model of visual search models the accuracy and response time data in a visual search experiment using queueing models with finite customer population and stopping criteria of completing the service for finite number of customers. It implements the conceptualization of a hybrid model proposed by Moore and Wolfe (2001), in which visual stimuli enter the processing one after the other and then are identified in parallel. This package provides functions that simulate the specified queueing process and calculate the Wasserstein distance between the empirical response times and the model prediction.

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