This package performs hypothesis testing using the interval estimates (e.g., confidence intervals). The non-overlapping interval estimates indicates the statistical significance. References to these procedures can be found at Noguchi and Marmolejo-Ramos (2016) <doi:10.1080/00031305.2016.1200487>, Bonett and Seier (2003) <doi:10.1198/0003130032323>, and Lemm (2006) <doi:10.1300/J082v51n02_05>.

Given two unbiased samples of patient level data on cost and effectiveness for a pair of treatments, make head-to-head treatment comparisons by (i) generating the bivariate bootstrap resampling distribution of ICE uncertainty for a specified value of the shadow price of health, lambda, (ii) form the wedge-shaped ICE confidence region with specified confidence fraction within [0.50, 0.99] that is equivariant with respect to changes in lambda, (iii) color the bootstrap outcomes within the above confidence wedge with economic preferences from an ICE map with specified values of lambda, beta and gamma parameters, (iv) display VAGR and ALICE acceptability curves, and (v) illustrate variation in ICE preferences by displaying potentially non-linear indifference(iso-preference) curves from an ICE map with specified values of lambda, beta and either gamma or eta parameters.

This package provides a model that provides researchers with a powerful tool for the classification and study of native corn by aiding in the identification of racial complexes which are fundamental to Mexico's agriculture and culture. This package has been developed based on data collected by "Proyecto Global de Maà ces Nativos México", which has conducted exhaustive surveys across the country to document the qualitative and quantitative characteristics of different types of native maize. The trained model uses a robust and diverse dataset, enabling it to achieve an 80% accuracy in classifying maize racial complexes. The characteristics included in the analysis comprise geographic location, grain and cob colors, as well as various physical measurements, such as lengths and widths.

The methods in this package adds to the functionality of the intamap package, such as bias correction and network optimization. Pebesma et al (2010) gives an overview of the methods behind and possible usage <doi:10.1016/j.cageo.2010.03.019>.

Implementation of some of the formulations for the thermodynamic and transport properties released by the International Association for the Properties of Water and Steam (IAPWS). More specifically, the releases R1-76(2014), R5-85(1994), R6-95(2018), R7-97(2012), R8-97, R9-97, R10-06(2009), R11-24, R12-08, R15-11, R16-17(2018), R17-20 and R18-21 at <https://iapws.org>.

This package provides a collection of functions that facilitate computational steps related to advice for fisheries management, according to ICES guidelines. These include methods for calculating reference points and model diagnostics.

Generates a Graphviz graph of the most significant 3-way interaction gains (i.e. conditional information gains) based on a provided discrete data frame. Various output formats are supported ('Graphviz', SVG, PNG, PDF, PS). For references, see the webpage of Aleks Jakulin <http://stat.columbia.edu/~jakulin/Int/>.

Calculation of key bacterial growth curve parameters using fourth degree polynomial functions. Six growth curve parameters are provided including peak growth rate, doubling time, lag time, maximum growth, and etc. ipolygrowth takes time series data from individual biological samples (with technical replicates) or multiple samples.

Calculates irrigation water quality ratios and has functions that could be used to plot several popular diagrams for irrigation water quality classification.

This package provides functions to Interact with the ICES Data Submission Utility (DATSU) <https://datsu.ices.dk/web/index.aspx>.

This package provides a set of tools for writing documents according to Geneva Graduate Institute conventions and regulations. The most common use is for writing and compiling theses or thesis chapters, as drafts or for examination with correct preamble formatting. However, the package also offers users to create HTML presentation slides with xaringan', complete problem sets, format posters, and, for course instructors, prepare a syllabus. The package includes additional functions for institutional color palettes, an institutional ggplot theme, a function for counting manuscript words, and a bibliographical analysis toolkit.

Computes bootstrapped Monte Carlo estimate of p value of Kolmogorov-Smirnov (KS) test and likelihood ratio test for zero-inflated count data, based on the work of Aldirawi et al. (2019) <doi:10.1109/BHI.2019.8834661>. With the package, user can also find tools to simulate random deviates from zero inflated or hurdle models and obtain maximum likelihood estimate of unknown parameters in these models.

These are data and functions to support quantitative peace science research. The data are important state-year information on democracy and wealth, which require periodic updates and regular maintenance. The functions permit some exploratory and diagnostic assessment of the kinds of data in demand by the community, but do not impose many dependencies on the user.

This package provides a user-friendly interface, using Shiny, to analyse glucose-stimulated insulin secretion (GSIS) assays in pancreatic beta cells or islets. The package allows the user to import several sets of experiments from different spreadsheets and to perform subsequent steps: summarise in a tidy format, visualise data quality and compare experimental conditions without omitting to account for technical confounders such as the date of the experiment or the technician. Together, insane is a comprehensive method that optimises pre-processing and analyses of GSIS experiments in a friendly-user interface. The Shiny App was initially designed for EndoC-betaH1 cell line following method described in Ndiaye et al., 2017 (<doi:10.1016/j.molmet.2017.03.011>).

An easy way to work with census, survey, and geographic data provided by IPUMS in R. Generate and download data through the IPUMS API and load IPUMS files into R with their associated metadata to make analysis easier. IPUMS data describing 1.4 billion individuals drawn from over 750 censuses and surveys is available free of charge from the IPUMS website <https://www.ipums.org>.

This package performs valid statistical inference on predicted data (IPD) using recent methods, where for a subset of the data, the outcomes have been predicted by an algorithm. Provides a wrapper function with specified defaults for the type of model and method to be used for estimation and inference. Further provides methods for tidying and summarizing results. Salerno et al., (2025) <doi:10.1093/bioinformatics/btaf055>.

This package provides a method that estimates an IV-optimal individualized treatment rule. An individualized treatment rule is said to be IV-optimal if it minimizes the maximum risk with respect to the putative IV and the set of IV identification assumptions. Please refer to <arXiv:2002.02579> for more details on the methodology and some theory underpinning the method. Function IV-PILE() uses functions in the package locClass'. Package locClass can be accessed and installed from the R-Forge repository via the following link: <https://r-forge.r-project.org/projects/locclass/>. Alternatively, one can install the package by entering the following in R: install.packages("locClass", repos="<http://R-Forge.R-project.org>")'.

This package provides tools for estimating incidence from biomarker data in cross- sectional surveys, and for calibrating tests for recent infection. Implements and extends the method of Kassanjee et al. (2012) <doi:10.1097/EDE.0b013e3182576c07>.

Allows the simulation of the recruitment and both the event and treatment phase of a clinical trial. Based on these simulations, the timing of interim analyses can be assessed.

This package provides classes and methods for seismic data analysis. The base classes and methods are inspired by the python code found in the ObsPy python toolbox <https://github.com/obspy/obspy>. Additional classes and methods support data returned by web services provided by the IRIS DMC <http://service.iris.edu/>.

Interpretation methods for analyzing the behavior and individual predictions of modern neural networks in a three-step procedure: Converting the model, running the interpretation method, and visualizing the results. Implemented methods are, e.g., Connection Weights described by Olden et al. (2004) <doi:10.1016/j.ecolmodel.2004.03.013>, layer-wise relevance propagation ('LRP') described by Bach et al. (2015) <doi:10.1371/journal.pone.0130140>, deep learning important features ('DeepLIFT') described by Shrikumar et al. (2017) <doi:10.48550/arXiv.1704.02685> and gradient-based methods like SmoothGrad described by Smilkov et al. (2017) <doi:10.48550/arXiv.1706.03825>, Gradient x Input or Vanilla Gradient'. Details can be found in the accompanying scientific paper: Koenen & Wright (2024, Journal of Statistical Software, <doi:10.18637/jss.v111.i08>).

An implementation of corrected sandwich variance (CSV) estimation method for making inference of marginal hazard ratios (HR) in inverse probability weighted (IPW) Cox model without and with clustered data, proposed by Shu, Young, Toh, and Wang (2019) in their paper under revision for Biometrics. Both conventional inverse probability weights and stabilized weights are implemented. Logistic regression model is assumed for propensity score model.

This package provides implementation of various correlation coefficients of common use in Information Retrieval. In particular, it includes Kendall (1970, isbn:0852641990) tau coefficient as well as tau_a and tau_b for the treatment of ties. It also includes Yilmaz et al. (2008) <doi:10.1145/1390334.1390435> tauAP correlation coefficient, and versions tauAP_a and tauAP_b developed by Urbano and Marrero (2017) <doi:10.1145/3121050.3121106> to cope with ties.

Calculate incidence and prevalence using data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Incidence and prevalence can be estimated for the total population in a database or for a stratification cohort.