This package provides functions for phenological data preprocessing, modelling and result handling. For more information, please refer to Lange et al. (2016) <doi:10.1007/s00484-016-1161-8>.

This package provides a collection of functions for modelling mutations in pedigrees with marker data, as used e.g. in likelihood computations with microsatellite data. Implemented models include equal, proportional and stepwise models, as well as random models for experimental work, and custom models allowing the user to apply any valid mutation matrix. Allele lumping is done following the lumpability criteria of Kemeny and Snell (1976), ISBN:0387901922.

Perform user-friendly power analyses for the random intercept cross-lagged panel model (RI-CLPM) and the bivariate stable trait autoregressive trait state (STARTS) model. The strategy as proposed by Mulder (2023) <doi:10.1080/10705511.2022.2122467> is implemented. Extensions include the use of parameter constraints over time, bounded estimation, generation of data with skewness and kurtosis, and the option to setup the power analysis for Mplus.

This R package allows the determination of some distributions of the voters power when passing laws in weighted voting situations.

This package provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).

Interfaces and methods for variable selection in Partial Least Squares. The methods include filter methods, wrapper methods and embedded methods. Both regression and classification is supported.

This package provides a set of concise and efficient tools for statistical production. Can also be used for data management. In statistical production, you deal with complex data and need to control your process at each step of your work. Concise functions are very helpful, because you do not hesitate to use them. The following functions are included in the package. dup checks duplicates. miss checks missing values. tac computes contingency table of all columns. toc compares two tables, spotting significant deviations. chi2_find compares columns within a data.frame, spotting related categories of (a more complex function).

Given k populations (can be in thousands), what is the probability that a given subset of size t contains the true top t populations? This package finds this probability and offers three tuning parameters (G, d, L) to relax the definition.

Propagation of uncertainty using higher-order Taylor expansion and Monte Carlo simulation. Calculations of propagated uncertainties are based on matrix calculus including covariance structure according to Arras 1998 <doi:10.3929/ethz-a-010113668> (first order), Wang & Iyer 2005 <doi:10.1088/0026-1394/42/5/011> (second order) and BIPM Supplement 1 (Monte Carlo) <doi:10.59161/JCGM101-2008>.

This software has evolved from fisheries research conducted at the Pacific Biological Station (PBS) in Nanaimo', British Columbia, Canada. It extends the R language to include two-dimensional plotting features similar to those commonly available in a Geographic Information System (GIS). Embedded C code speeds algorithms from computational geometry, such as finding polygons that contain specified point events or converting between longitude-latitude and Universal Transverse Mercator (UTM) coordinates. Additionally, we include C++ code developed by Angus Johnson for the Clipper library, data for a global shoreline, and other data sets in the public domain. Under the user's R library directory .libPaths()', specifically in ./PBSmapping/doc', a complete user's guide is offered and should be consulted to use package functions effectively.

This package provides functions and data sets for the text Probability and Statistics with R.

This package provides a shiny application for visualizing high-dimensional data using non-linear dimensionality reduction (NLDR) techniques such as t-SNE and UMAP. It provides an interactive platform to explore high-dimensional datasets, diagnose the quality of the embeddings using the quollr package, and compare different NLDR methods.

Numerical integration of continuously differentiable functions f(x,y) over simple closed polygonal domains. The following cubature methods are implemented: product Gauss cubature (Sommariva and Vianello, 2007, <doi:10.1007/s10543-007-0131-2>), the simple two-dimensional midpoint rule (wrapping spatstat.geom functions), and adaptive cubature for radially symmetric functions via line integrate() along the polygon boundary (Meyer and Held, 2014, <doi:10.1214/14-AOAS743>, Supplement B). For simple integration along the axes, the cubature package is more appropriate.

Estimates power, minimum detectable effect size (MDES) and sample size requirements. The context is multilevel randomized experiments with multiple outcomes. The estimation takes into account the use of multiple testing procedures. Development of this package was supported by a grant from the Institute of Education Sciences (R305D170030). For a full package description, including a detailed technical appendix, see <doi:10.18637/jss.v108.i06>.

This package implements our Bayesian phase I repeated measurement design that accounts for multidimensional toxicity endpoints from multiple treatment cycles. The package also provides a novel design to account for both multidimensional toxicity endpoints and early-stage efficacy endpoints in the phase I design. For both designs, functions are provided to recommend the next dosage selection based on the data collected in the available patient cohorts and to simulate trial characteristics given design parameters. Yin, Jun, et al. (2017) <doi:10.1002/sim.7134>.

This package implements the American Heart Association Predicting Risk of cardiovascular disease EVENTs (PREVENT) equations from Khan SS, Matsushita K, Sang Y, and colleagues (2023) <doi:10.1161/CIRCULATIONAHA.123.067626>, with optional comparison with their de facto predecessor, the Pooled Cohort Equations from the American Heart Association and American College of Cardiology (2013) <doi:10.1161/01.cir.0000437741.48606.98> and the revision to the Pooled Cohort Equations from Yadlowsky and colleagues (2018) <doi:10.7326/M17-3011>.

In this record linkage package, data preprocessing has been meticulously executed to cover a wide range of datasets, ensuring that variable names are standardized using synonyms. This approach facilitates seamless data integration and analysis across various datasets. While users have the flexibility to modify variable names, the system intelligently ensures that changes are only permitted when they do not compromise data consistency or essential variable essence.

Quasi likelihood-based methods for estimating linear and log-linear Poisson Network Autoregression models with p lags and covariates. Tools for testing the linearity versus several non-linear alternatives. Tools for simulation of multivariate count distributions, from linear and non-linear PNAR models, by using a specific copula construction. References include: Armillotta, M. and K. Fokianos (2023). "Nonlinear network autoregression". Annals of Statistics, 51(6): 2526--2552. <doi:10.1214/23-AOS2345>. Armillotta, M. and K. Fokianos (2024). "Count network autoregression". Journal of Time Series Analysis, 45(4): 584--612. <doi:10.1111/jtsa.12728>. Armillotta, M., Tsagris, M. and Fokianos, K. (2024). "Inference for Network Count Time Series with the R Package PNAR". The R Journal, 15/4: 255--269. <doi:10.32614/RJ-2023-094>.

An assortment of functions that could be useful in analyzing data from psychophysical experiments. It includes functions for calculating d from several different experimental designs, links for m-alternative forced-choice (mafc) data to be used with the binomial family in glm (and possibly other contexts) and self-Start functions for estimating gamma values for CRT screen calibrations.

This package provides a suite of multivariate methods and data visualization tools to implement profile analysis and cross-validation techniques described in Davison & Davenport (2002) <DOI: 10.1037/1082-989X.7.4.468>, Bulut (2013), and other published and unpublished resources. The package includes routines to perform criterion-related profile analysis, profile analysis via multidimensional scaling, moderated profile analysis, profile analysis by group, and a within-person factor model to derive score profiles.

This package provides a unified and user-friendly framework for applying the principal sufficient dimension reduction methods for both linear and nonlinear cases. The package has an extendable power by varying loss functions for the support vector machine, even for an user-defined arbitrary function, unless those are convex and differentiable everywhere over the support (Li et al. (2011) <doi:10.1214/11-AOS932>). Also, it provides a real-time sufficient dimension reduction update procedure using the principal least squares support vector machine (Artemiou et al. (2021) <doi:10.1016/j.patcog.2020.107768>).

This package provides methods to calculate and present PHENTHAUproc', an early warning and decision support system for hazard assessment and control of oak processionary moth (OPM) using local and spatial temperature data. It was created by Halbig et al. 2024 (<doi:10.1016/j.foreco.2023.121525>) at FVA (<https://www.fva-bw.de/en/homepage/>) Forest Research Institute Baden-Wuerttemberg, Germany and at BOKU - University of Natural Ressources and Life Sciences, Vienna, Austria.

Includes functions to calculate several physicochemical properties and indices for amino-acid sequences as well as to read and plot XVG output files from the GROMACS molecular dynamics package.

Perform inference in the secondary analysis setting with linked data potentially containing mismatch errors. Only the linked data file may be accessible and information about the record linkage process may be limited or unavailable. Implements the General Framework for Regression with Mismatched Data developed by Slawski et al. (2023) <doi:10.48550/arXiv.2306.00909>. The framework uses a mixture model for pairs of linked records whose two components reflect distributions conditional on match status, i.e., correct match or mismatch. Inference is based on composite likelihood and the Expectation-Maximization (EM) algorithm. The package currently supports Cox Proportional Hazards Regression (right-censored data only) and Generalized Linear Regression Models (Gaussian, Gamma, Poisson, and Logistic (binary models only)). Information about the underlying record linkage process can be incorporated into the method if available (e.g., assumed overall mismatch rate, safe matches, predictors of match status, or predicted probabilities of correct matches).