Univariate stratification of survey populations with a generalization of the Lavallee-Hidiroglou method of stratum construction. The generalized method takes into account a discrepancy between the stratification variable and the survey variable. The determination of the optimal boundaries also incorporate, if desired, an anticipated non-response, a take-all stratum for large units, a take-none stratum for small units, and a certainty stratum to ensure that some specific units are in the sample. The well known cumulative root frequency rule of Dalenius and Hodges and the geometric rule of Gunning and Horgan are also implemented.

Tracing is a framework for instrumenting Rust programs with context-aware, structured, event-based diagnostic information. This crate provides compatibility layers for using tracing alongside the logging facade provided by the log crate.

This crate provides:

• AsTrace and AsLog traits for converting between tracing and log types.

• LogTracer, a log::Log implementation that consumes log::Records and outputs them as tracing::Events.

• An env_logger module, with helpers for using the env_logger crate with tracing (optional, enabled by the env-logger feature).

Tracing is a framework for instrumenting Rust programs with context-aware, structured, event-based diagnostic information. This crate provides compatibility layers for using tracing alongside the logging facade provided by the log crate.

This crate provides:

• AsTrace and AsLog traits for converting between tracing and log types.

• LogTracer, a log::Log implementation that consumes log::Records and outputs them as tracing::Events.

• An env_logger module, with helpers for using the env_logger crate with tracing (optional, enabled by the env-logger feature).

In semi-structured interviews that use the framework method, it is not always clear how refinements to interview questions affect the decision of when to stop interviews. The trend of novel and duplicate interview codes (novel codes are information that other interviewees have not previously mentioned) provides insight into the richness of qualitative information. This package provides tools to visualise when refinements occur and how that affects the trends of novel and duplicate codes. These visualisations, when used progressively as new interviews are finished, can help the researcher to decide on a stopping point for their interviews.

Implementations of various robust and flexible model-based clustering methods for data sets with missing values at random. Two main models are: Multivariate Contaminated Normal Mixture (MCNM, Tong and Tortora, 2022, <doi:10.1007/s11634-021-00476-1>) and Multivariate Generalized Hyperbolic Mixture (MGHM, Wei et al., 2019, <doi:10.1016/j.csda.2018.08.016>). Mixtures via some special or limiting cases of the multivariate generalized hyperbolic distribution are also included: Normal-Inverse Gaussian, Symmetric Normal-Inverse Gaussian, Skew-Cauchy, Cauchy, Skew-t, Student's t, Normal, Symmetric Generalized Hyperbolic, Hyperbolic Univariate Marginals, Hyperbolic, and Symmetric Hyperbolic.

This package provides a tool set for food information and dietary assessment. It uses food composition data from several reference databases, including: USDA (United States), CIQUAL (France), BEDCA (Spain), CNF (Canada) and STFCJ (Japan). NutrienTrackeR calculates the intake levels for both macronutrient and micronutrients, and compares them with the recommended dietary allowances (RDA). It includes a number of visualization tools, such as time series plots of nutrient intake, and pie-charts showing the main foods contributing to the intake level of a given nutrient. A shiny app exposing the main functionalities of the package is also provided.

Seed vigor is defined as the sum total of those properties of the seed which determine the level of activity and performance of the seed or seed lot during germination and seedling emergence. Testing for vigor becomes more important for carryover seeds, especially if seeds were stored under unknown conditions or under unfavorable storage conditions. Seed vigor testing is also used as indicator of the storage potential of a seed lot and in ranking various seed lots with different qualities. The vigour index is calculated using the equation given by (Ling et al. 2014) <doi:10.1038/srep05859>.

This package implements Multivariate ANalysis Of VAriance (MANOVA) parameters inference and test with regularization for semicontinuous high-dimensional data. The method can be applied also in presence of low-dimensional data. The p-value can be obtained through asymptotic distribution or using a permutation procedure. The package gives also the possibility to simulate this type of data. Method is described in Elena Sabbioni, Claudio Agostinelli and Alessio Farcomeni (2025) A regularized MANOVA test for semicontinuous high-dimensional data. Biometrical Journal, 67:e70054. DOI <doi:10.1002/bimj.70054>, arXiv DOI <doi:10.48550/arXiv.2401.04036>.

This package implements a likelihood ratio test and two pairwise standardized mean difference tests for testing equality of means against tree ordered alternatives in one-way ANOVA. The null hypothesis assumes all group means are equal, while the alternative assumes the control mean is less than or equal to each treatment mean with at least one strict inequality. Inputs are a list of numeric vectors (groups) and a significance level; outputs include the test statistic, critical value, and decision. Methods described in "Testing Against Tree Ordered Alternatives in One-way ANOVA" <doi:10.48550/arXiv.2507.17229>.

Computes the log likelihood for an inverse gamma stochastic volatility model using a closed form expression of the likelihood. The details of the computation of this closed form expression are given in Gonzalez and Majoni (2023) <http://rcea.org/RePEc/pdf/wp23-11.pdf> . The closed form expression is obtained for a stationary inverse gamma stochastic volatility model by marginalising out the volatility. This allows the user to obtain the maximum likelihood estimator for this non linear non Gaussian state space model. In addition, the user can obtain the estimates of the smoothed volatility using the exact smoothing distributions.

This package contains several tools for nonlinear regression analyses and general data analysis in biology and agriculture. Contains also datasets for practicing and teaching purposes. Supports the blog: Onofri (2024) "Fixing the bridge between biologists and statisticians" <https://www.statforbiology.com> and the book: Onofri (2024) "Experimental Methods in Agriculture" <https://www.statforbiology.com/_statbookeng/>. The blog is a collection of short articles aimed at improving the efficiency of communication between biologists and statisticians, as pointed out in Kozak (2016) <doi:10.1590/0103-9016-2015-0399>, spreading a better awareness of the potential usefulness, beauty and limitations of biostatistic.

This package provides a Shiny application to access the functionalities and datasets of the archeofrag package for spatial analysis in archaeology from refitting data. Quick and seamless exploration of archaeological refitting datasets, focusing on physical refits only. Features include: built-in documentation and convenient workflow, plot generation and exports, exploration of spatial units merging solutions, simulation of archaeological site formation processes, support for parallel computing, R code generation to re-execute simulations and ensure reproducibility, code generation for the openMOLE model exploration software. A demonstration of the app is available at <https://analytics.huma-num.fr/Sebastien.Plutniak/archeofrag/>.

Unified and user-friendly framework for using new distributional representations of biosensors data in different statistical modeling tasks: regression models, hypothesis testing, cluster analysis, visualization, and descriptive analysis. Distributional representations are a functional extension of compositional time-range metrics and we have used them successfully so far in modeling glucose profiles and accelerometer data. However, these functional representations can be used to represent any biosensor data such as ECG or medical imaging such as fMRI. Matabuena M, Petersen A, Vidal JC, Gude F. "Glucodensities: A new representation of glucose profiles using distributional data analysis" (2021) <doi:10.1177/0962280221998064>.

Predictive scores must be updated with care, because actions taken on the basis of existing risk scores causes bias in risk estimates from the updated score. A holdout set is a straightforward way to manage this problem: a proportion of the population is held-out from computation of the previous risk score. This package provides tools to estimate a size for this holdout set and associated errors. Comprehensive vignettes are included. Please see: Haidar-Wehbe S, Emerson SR, Aslett LJM, Liley J (2022) <doi:10.48550/arXiv.2202.06374> (to appear in Annals of Applied Statistics) for details of methods.

PACTA (Paris Agreement Capital Transition Assessment) for Banks is a tool that allows banks to calculate the climate alignment of their corporate lending portfolios. This package is designed to make it easy to install and load multiple PACTA for Banks packages in a single step. It also provides thorough documentation - the PACTA for Banks cookbook at <https://rmi-pacta.github.io/pacta.loanbook/articles/cookbook_overview.html> - on how to run a PACTA for Banks analysis. This covers prerequisites for the analysis, the separate steps of running the analysis, the interpretation of PACTA for Banks results, and advanced use cases.

Fits non-crossing regression quantiles as a function of linear covariates and multiple smooth terms, including varying coefficients, via B-splines with L1-norm difference penalties. Random intercepts and variable selection are allowed via the lasso penalties. The smoothing parameters are estimated as part of the model fitting, see Muggeo and others (2021) <doi:10.1177/1471082X20929802>. Monotonicity and concavity constraints on the fitted curves are allowed, see Muggeo and others (2013) <doi:10.1007/s10651-012-0232-1>, and also <doi:10.13140/RG.2.2.12924.85122> or <doi:10.13140/RG.2.2.29306.21445> some code examples.

This package provides a function extrapolate that extrapolates a given function f(x) to f(x0), evaluating f only at a geometric sequence of points > x0 (or optionally < x0). The key algorithm is Richardson extrapolation using a Neville–Aitken tableau, which adaptively increases the degree of an extrapolation polynomial until convergence is achieved to a desired tolerance (or convergence stalls due to e.g. floating-point errors). This allows one to obtain f(x0) to high-order accuracy, assuming that f(x0+h) has a Taylor series or some other power series in h.

Estimate and plot confounder-adjusted survival curves using either Direct Adjustment', Direct Adjustment with Pseudo-Values', various forms of Inverse Probability of Treatment Weighting', two forms of Augmented Inverse Probability of Treatment Weighting', Empirical Likelihood Estimation or Targeted Maximum Likelihood Estimation'. Also includes a significance test for the difference between two adjusted survival curves and the calculation of adjusted restricted mean survival times. Additionally enables the user to estimate and plot cause-specific confounder-adjusted cumulative incidence functions in the competing risks setting using the same methods (with some exceptions). For details, see Denz et. al (2023) <doi:10.1002/sim.9681>.

This package provides tools for Bayesian parameter estimation of adsorption isotherm models using Markov Chain Monte Carlo (MCMC) methods. This package enables users to fit non-linear and linear adsorption isotherm modelsâ Freundlich, Langmuir, and Temkinâ within a probabilistic framework, capturing uncertainty and parameter correlations. It provides posterior summaries, 95% credible intervals, convergence diagnostics (Gelman-Rubin), and visualizations through trace and density plots. With this R package, researchers can rigorously analyze adsorption behavior in environmental and chemical systems using robust Bayesian inference. For more details, see Gilks et al. (1995) <doi:10.1201/b14835>, and Gamerman & Lopes (2006) <doi:10.1201/9781482296426>.

Enhances the functionality of the mvbutils::foodweb() program. The matrix-format output of the original program contains identical row names and column names, each name representing a retrieved function. This format is enhanced by using the find_funs() program [see Sebastian (2017) <https://sebastiansauer.github.io/finds_funs/>] to concatenate the package name to the function name. Each package is assigned a unique color, that is used to color code the text naming the packages and the functions. This color coding is extended to the entries of value "1" within the matrix, indicating the pattern of ancestor and descendent functions.

Plots traced ultrasound tongue imaging data according to a polar coordinate system. There is currently support for plotting means and standard deviations of each category's trace; Smoothing Splines Analysis of Variance (SSANOVA) could be implemented as well. The origin of the polar coordinates may be defined manually or automatically determined based on different algorithms. Currently ultrapolaRplot supports ultrasound tongue imaging trace data from UltraTrace (<https://github.com/SwatPhonLab/UltraTrace>). UltraTrace is capable of importing data from Articulate Instruments AAA. read_textgrid.R is required for opening TextGrids to determine category and alignment information of ultrasound traces.

This package provides a variety of Network Scale-up Models for researchers to analyze Aggregated Relational Data, mostly through the use of Stan. In this version, the package implements models from Laga, I., Bao, L., and Niu, X (2021) <arXiv:2109.10204>, Zheng, T., Salganik, M. J., and Gelman, A. (2006) <doi:10.1198/016214505000001168>, Killworth, P. D., Johnsen, E. C., McCarty, C., Shelley, G. A., and Bernard, H. R. (1998) <doi:10.1016/S0378-8733(96)00305-X>, and Killworth, P. D., McCarty, C., Bernard, H. R., Shelley, G. A., and Johnsen, E. C. (1998) <doi:10.1177/0193841X9802200205>.

This package implements an adaptively weighted group Lasso procedure for simultaneous variable selection and structure identification in varying coefficient quantile regression models and additive quantile regression models with ultra-high dimensional covariates. The methodology, grounded in a strong sparsity condition, establishes selection consistency under certain weight conditions. To address the challenge of tuning parameter selection in practice, a BIC-type criterion named high-dimensional information criterion (HDIC) is proposed. The Lasso procedure, guided by HDIC-determined tuning parameters, maintains selection consistency. Theoretical findings are strongly supported by simulation studies. (Toshio Honda, Ching-Kang Ing, Wei-Ying Wu, 2019, <DOI:10.3150/18-BEJ1091>).

Rust has lots of builtin traits that are implemented for its basic types, such as Add, Not, From or Display. However, when wrapping these types inside your own structs or enums you lose the implementations of these traits and are required to recreate them. This is especially annoying when your own structures are very simple, such as when using the commonly advised newtype pattern (e.g. MyInt(i32)).

This library tries to remove these annoyances and the corresponding boilerplate code. It does this by allowing you to derive lots of commonly used traits for both structs and enums.