Bundles the datasets and functions used in the textbook by Philip Pollock and Barry Edwards, an R Companion to Essentials of Political Analysis, Second Edition.

Create a parallel coordinates plot, using `htmlwidgets` package and `d3.js`.

Speeds up the process of loading raw data from MBA (Multiplex Bead Assay) examinations, performs quality control checks, and automatically normalises the data, preparing it for more advanced, downstream tasks. The main objective of the package is to create a simple environment for a user, who does not necessarily have experience with R language. The package is developed within the project of the same name - PvSTATEM', which is an international project aiming for malaria elimination.

Sample size calculations for practical equivalence trial design with a time to event endpoint.

This package implements projection pursuit forest algorithm for supervised classification.

This package provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Maternal Mortality 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>).

This package provides functions to perform paternity exclusion via allele matching, in autopolyploid species having ploidy 4, 6, or 8. The marker data used can be genotype data (copy numbers known) or allelic phenotype data (copy numbers not known).

Handles and formats author information in scientific writing in R Markdown and Quarto'. plume provides easy-to-use and flexible tools for inserting author data in YAML as well as generating author and contribution lists (among others) as strings from tabular data.

This package provides publicationâ quality and interactive plots for exploring the posterior output of Latent Space Item Response Models, including Posterior Interaction Profiles, radar charts, 2â D latent maps, and itemâ similarity heat maps. The methods implemented in this package are based on work by Jeon, M., Jin, I. H., Schweinberger, M., Baugh, S. (2021) <doi:10.1007/s11336-021-09762-5>.

This package implements propensity score weighting methods for estimating counterfactual survival functions, marginal hazard ratios, and weighted Kaplan-Meier and cumulative risk curves in observational studies with time-to-event outcomes. Supports binary and multiple treatment groups with inverse probability of treatment weighting (IPW), overlap weighting (OW), and average treatment effect on the treated (ATT). Includes symmetric trimming (Crump extension) for extreme propensity scores. Variance estimation via analytical M-estimation or bootstrap. Methods based on Li et al. (2018) <doi:10.1080/01621459.2016.1260466>, Li & Li (2019) <doi:10.1214/19-AOAS1282>, and Cheng et al. (2022) <doi:10.1093/aje/kwac043>.

Early generation breeding trials are to be conducted in multiple environments where it may not be possible to replicate all the lines in each environment due to scarcity of resources. For such situations, partially replicated (p-Rep) designs have wide application potential as only a proportion of the test lines are replicated at each environment. A collection of several utility functions related to p-Rep designs have been developed. Here, the package contains six functions for a complete stepwise analytical study of these designs. Five functions pRep1(), pRep2(), pRep3(), pRep4() and pRep5(), are used to generate five new series of p-Rep designs and also compute average variance factors and canonical efficiency factors of generated designs. A fourth function NCEV() is used to generate incidence matrix (N), information matrix (C), canonical efficiency factor (E) and average variance factor (V). This function is general in nature and can be used for studying the characterization properties of any block design. A construction procedure for p-Rep designs was given by Williams et al.(2011) <doi:10.1002/bimj.201000102> which was tedious and time consuming. Here, in this package, five different methods have been given to generate p-Rep designs easily.

This package performs partial verification bias (PVB) correction for binary diagnostic tests, where PVB arises from selective patient verification in diagnostic accuracy studies. Supports correction of important accuracy measures -- sensitivity, specificity, positive predictive values and negative predictive value -- under missing-at-random and missing-not-at-random missing data mechanisms. Available methods and references are "Begg and Greenes methods" in Alonzo & Pepe (2005) <doi:10.1111/j.1467-9876.2005.00477.x> and deGroot et al. (2011) <doi:10.1016/j.annepidem.2010.10.004>; "Multiple imputation" in Harel & Zhou (2006) <doi:10.1002/sim.2494>, "EM-based logistic regression" in Kosinski & Barnhart (2003) <doi:10.1111/1541-0420.00019>; "Inverse probability weighting" in Alonzo & Pepe (2005) <doi:10.1111/j.1467-9876.2005.00477.x>; "Inverse probability bootstrap sampling" in Nahorniak et al. (2015) <doi:10.1371/journal.pone.0131765> and Arifin & Yusof (2022) <doi:10.3390/diagnostics12112839>; "Scaled inverse probability resampling methods" in Arifin & Yusof (2025) <doi:10.1371/journal.pone.0321440>.

Carries out model-based clustering or classification using parsimonious Gaussian mixture models. McNicholas and Murphy (2008) <doi:10.1007/s11222-008-9056-0>, McNicholas (2010) <doi:10.1016/j.jspi.2009.11.006>, McNicholas and Murphy (2010) <doi:10.1093/bioinformatics/btq498>, McNicholas et al. (2010) <doi:10.1016/j.csda.2009.02.011>.

Optogenetics is a new tool to study neuronal circuits that have been genetically modified to allow stimulation by flashes of light. This package implements the methodological framework, Point-process Response model for Optogenetics (PRO), for analyzing data from these experiments. This method provides explicit nonlinear transformations to link the flash point-process with the spiking point-process. Such response functions can be used to provide important and interpretable scientific insights into the properties of the biophysical process that governs neural spiking in response to optogenetic stimulation.

An implementation of reliability estimation methods described in the paper (Bosnic, Z., & Kononenko, I. (2008) <doi:10.1007/s10489-007-0084-9>), which allows you to test the reliability of a single predicted instance made by your model and prediction function. It also allows you to make a correlation test to estimate which reliability estimate is the most accurate for your model.

This package provides a thin wrapper over PLINK 2's core libraries which provides an R interface for reading .pgen files. A minimal .pvar loader is also included. Chang et al. (2015) <doi:10.1186/s13742-015-0047-8>.

Detecting markers of politeness in English natural language. This package allows researchers to easily visualize and quantify politeness between groups of documents. This package combines prior research on the linguistic markers of politeness. We thank the Spencer Foundation, the Hewlett Foundation, and Harvard's Institute for Quantitative Social Science for support.

Extends the S3 generic function knit_print() in knitr to automatically print some objects using an appropriate format such as Markdown or LaTeX. For example, data frames are automatically printed as tables, and the help() pages can also be rendered in knitr documents.

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>).

Perform permutation-based hypothesis testing for randomized experiments as suggested in Ludbrook & Dudley (1998) <doi:10.2307/2685470> and Ernst (2004) <doi:10.1214/088342304000000396>, introduced in Pham et al. (2022) <doi:10.1016/j.chemosphere.2022.136736>.

This package contains various tools for conveniently downloading and editing taxon-specific datasets from the Paleobiology Database <https://paleobiodb.org>, extracting information on abundance, temporal distribution of subtaxa and taxonomic diversity through deep time, and visualizing these data in relation to phylogeny and stratigraphy.

Weighted Deming regression, also known as errors-in-variable regression, is applied with suitable weights. Weights are modeled via a precision profile; thus the methods implemented here are referred to as precision profile weighted Deming (PWD) regression. The package covers two settings â one where the precision profiles are known either from external studies or from adequate replication of the X and Y readings, and one in which there is a plausible functional form for the precision profiles but the exact (unknown) function must be estimated from the (generally singlicate) readings. The function set includes tools for: estimated standard errors (via jackknifing); standardized-residual analysis function with regression diagnostic tools for normality, linearity and constant variance; and an outlier analysis identifying significant outliers for closer investigation. The following reference provides further information on mathematical derivations and applications. Hawkins, D.M., and J.J. Kraker (2026). Precision Profile Weighted Deming Regression for Methods Comparison'. The Journal of Applied Laboratory Medicine 11, 379-392 <doi:10.1093/jalm/jfaf183>.

Several tests of quantitative palaeoenvironmental reconstructions from microfossil assemblages, including the null model tests of the statistically significant of reconstructions developed by Telford and Birks (2011) <doi:10.1016/j.quascirev.2011.03.002>, and tests of the effect of spatial autocorrelation on transfer function model performance using methods from Telford and Birks (2009) <doi:10.1016/j.quascirev.2008.12.020> and Trachsel and Telford (2016) <doi:10.5194/cp-12-1215-2016>. Age-depth models with generalized mixed-effect regression from Heegaard et al (2005) <doi:10.1191/0959683605hl836rr> are also included.

This package provides functions to get prediction intervals and prediction points of future observations from mixture distributions like gamma, beta, Weibull and normal.