The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 <doi:10.1017/RDC.2020.68>) are provided here in a single data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98) and postbomb curves. Also provided are functions to copy the curves into memory, and to plot the curves and their underlying data, as well as functions to calibrate radiocarbon dates.

Support for implicit expansion of arrays in operations involving arrays of mismatching sizes. This pattern is known as "broadcasting" in Python and "implicit expansion" in Matlab and is explained for example in the article "Array programming with NumPy" by C. R. Harris et al. (2020) <doi:10.1038/s41586-020-2649-2>.

Intensity-duration-frequency (IDF) curves are a widely used analysis-tool in hydrology to assess extreme values of precipitation [e.g. Mailhot et al., 2007, <doi:10.1016/j.jhydrol.2007.09.019>]. The package IDF provides functions to estimate IDF parameters for given precipitation time series on the basis of a duration-dependent generalized extreme value distribution [Koutsoyiannis et al., 1998, <doi:10.1016/S0022-1694(98)00097-3>].

This package provides statistical workflows used in the evaluation of in vitro diagnostic reagents. Facilities include method comparison, commutability assessment, Bland-Altman and receiver operating characteristic analysis, qualitative agreement, C5 and C95 estimation, precision and variance-component analysis, linearity, interference, dilution and spiking studies, high-dose hook assessment, measurement uncertainty, reference-material bias, reference intervals, stability studies, quality-control charts, curve fitting, analytical sensitivity, outlier and normality assessment, and sample-size calculations. For methodological details, see Bland and Altman (1986) <doi:10.1016/S0140-6736(86)90837-8>, Passing and Bablok (1983) <doi:10.1515/cclm.1983.21.11.709>, Linnet (1993) <doi:10.1093/clinchem/39.3.424>, Hawkins and Kraker (2026) <doi:10.1093/jalm/jfaf183>, Hanley and McNeil (1982) <doi:10.1148/radiology.143.1.7063747>, Horn et al. (1998) <doi:10.1093/clinchem/44.3.622>, Westgard et al. (1981) <doi:10.1093/clinchem/27.3.493>, and Lu et al. (2016) <doi:10.1515/ijb-2015-0039>.

Tools, tutorials, and demos of Item Factor Analysis using OpenMx'. This software is described in Pritikin & Falk (2020) <doi:10.1177/0146621620929431>.

Allows an interactive assessment of the timing of interim analyses. The algorithm simulates both the recruitment and treatment/event phase of a clinical trial based on the package interim'.

It provides multiple functions that are useful for ecological research and teaching statistics to ecologists. It is based on data analysis courses offered at the Instituto de Ecologà a AC (INECOL). For references and published evidence see, Manrique-Ascencio, et al (2024) <doi:10.1111/gcb.17282>, Manrique-Ascencio et al (2024) <doi:10.1111/plb.13683>, Ruiz-Guerra et al(2017) <doi:10.17129/botsci.812>, Juarez-Fragoso et al (2024) <doi:10.1007/s10980-024-01809-z>, Papaqui-Bello et al (2024) <doi:10.13102/sociobiology.v71i2.10503>.

IsoSpec is a fine structure calculator used for obtaining the most probable masses of a chemical compound given the frequencies of the composing isotopes and their masses. It finds the smallest set of isotopologues with a given probability. The probability is assumed to be that of the product of multinomial distributions, each corresponding to one particular element and parametrized by the frequencies of finding these elements in nature. These numbers are supplied by IUPAC - the International Union of Pure and Applied Chemistry. See: Lacki, Valkenborg, Startek (2020) <DOI:10.1021/acs.analchem.0c00959> and Lacki, Startek, Valkenborg, Gambin (2017) <DOI:10.1021/acs.analchem.6b01459> for the description of the algorithms used.

Assist in the estimation of the Intraclass Correlation Coefficient (ICC) from variance components of a one-way analysis of variance and also estimate the number of individuals or groups necessary to obtain an ICC estimate with a desired confidence interval width.

The goal of image2data is to extract images and return them into a data set, especially for teaching data manipulation and data visualization. Basically, the eponymous function takes an image file ('png', tiff', jpeg', bmp') and turn it into a data set, pixels being rows (subjects) and columns (variables) being their coordinate positions (x- and y-axis) and their respective color (in hex codes). The function can return a complete image or a range of color (i.e., contour, silhouette). The data can then be manipulated as would any data set by either creating other related variables (to hide the image) or as a genuine toy data set.

Calculates the RMS intrinsic and parameter-effects curvatures of a nonlinear regression model. The curvatures are global measures of assessing whether a model/data set combination is close-to-linear or not. See Bates and Watts (1980) <doi:10.1002/9780470316757> and Ratkowsky and Reddy (2017) <doi:10.1093/aesa/saw098> for details.

Estimation of reliability coefficients for ability estimates and sum scores from item response theory models as defined in Cheng, Y., Yuan, K.-H. and Liu, C. (2012) <doi:10.1177/0013164411407315> and Kim, S. and Feldt, L. S. (2010) <doi:10.1007/s12564-009-9062-8>. The package supports the 3-PL and generalized partial credit models and includes estimates of the standard errors of the reliability coefficient estimators, derived in Andersson, B. and Xin, T. (2018) <doi:10.1177/0013164417713570>.

This package provides a collection of several utility functions related to binary incomplete block designs. Contains function to generate A- and D-efficient binary incomplete block designs with given numbers of treatments, number of blocks and block size. Contains function to generate an incomplete block design with specified concurrence matrix. There are functions to generate balanced treatment incomplete block designs and incomplete block designs for test versus control treatments comparisons with specified concurrence matrix. Allows performing analysis of variance of data and computing estimated marginal means of factors from experiments using a connected incomplete block design. Tests of hypothesis of treatment contrasts in incomplete block design set up is supported.

Fits the (randomized drift) inverse Gaussian distribution to survival data. The model is described in Aalen OO, Borgan O, Gjessing HK. Survival and Event History Analysis. A Process Point of View. Springer, 2008. It is based on describing time to event as the barrier hitting time of a Wiener process, where drift towards the barrier has been randomized with a Gaussian distribution. The model allows covariates to influence starting values of the Wiener process and/or average drift towards a barrier, with a user-defined choice of link functions.

This package infers a topology of relationships between different datasets, such as multi-omics and phenotypic data recorded on the same samples. We based this methodology on the RV coefficient (Robert & Escoufier, 1976, <doi:10.2307/2347233>), a measure of matrix correlation, which we have extended for partial matrix correlations and binary data (Aben et al., 2018, <doi:10.1101/293993>).

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.

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>).

Fit unidimensional item response theory (IRT) models to test data, which includes both dichotomous and polytomous items, calibrate pretest item parameters, estimate examinees abilities, and examine the IRT model-data fit on item-level in different ways as well as provide useful functions related to IRT analyses such as differential item functioning analysis. In addition, the package provides a set of classical test theory functions for computing item- and test-level statistics (e.g., item difficulty, item-total correlation, and coefficient alpha) and for scoring and analyzing selected-response item data. The bring.flexmirt() and write.flexmirt() functions were written by modifying the read.flexmirt() function (Pritikin & Falk (2020) <doi:10.1177/0146621620929431>). The bring.bilog() and bring.parscale() functions were written by modifying the read.bilog() and read.parscale() functions, respectively (Weeks (2010) <doi:10.18637/jss.v035.i12>). The bisection() function was written by modifying the bisection() function (Howard (2017, ISBN:9780367657918)). The code of the inverse test characteristic curve scoring in the est_score() function was written by modifying the irt.eq.tse() function (Gonzalez (2014) <doi:10.18637/jss.v059.i07>). In est_score() function, the code of weighted likelihood estimation method was written by referring to the Pi(), Ji(), and Ii() functions of the catR package (Magis & Barrada (2017) <doi:10.18637/jss.v076.c01>).

This package implements the Interval Consensus Model (ICM) for analyzing continuous bounded interval-valued responses in psychometrics using Stan for Bayesian estimation. Provides functions for transforming interval data to simplex representations, fitting item response theory (IRT) models with isometric log-ratio (ILR) and sum log-ratio (SLR) link functions, and visualizing results. The package enables aggregation and analysis of interval-valued response data commonly found in psychological measurement and related disciplines. Based on Kloft et al. (2024) <doi:10.31234/osf.io/dzvw2>.

Coefficients of Interrater Reliability and Agreement for quantitative, ordinal and nominal data: ICC, Finn-Coefficient, Robinson's A, Kendall's W, Cohen's Kappa, ...

Calculates insulin secretion rates from C-peptide values based on the methods described in Van Cauter et al. (1992) <doi:10.2337/diab.41.3.368>. Includes functions to calculate estimated insulin secretion rates using linear or cubic spline interpolation of c-peptide values (see Eaton et al., 1980 <doi:10.1210/jcem-51-3-520> and Polonsky et al., 1986 <doi:10.1172/JCI112308>) and to calculate estimates of input coefficients (volume of distribution, short half life, long half life, and fraction attributed to short half life) as described by Van Cauter. Although the generated coefficients are specific to insulin secretion, the two-compartment secretion model used here is useful for certain applications beyond insulin.

This package provides functions to calculate indices used to score immunoglobulin A (IgA) binding of bacteria in IgA sequencing (IgA-Seq) experiments. This includes the original Kau and Palm indices and more recent methods as described in Jackson et al. (2020) <doi:10.1101/2020.08.19.257501>. Additionally the package contains a function to simulate IgA-Seq data and an example experimental data set for method testing.

Allows for the non-parametric estimation of transition intensities in interval-censored multi-state models using the approach of Gomon and Putter (2024) <doi:10.48550/arXiv.2409.07176> or Gu et al. (2023) <doi:10.1093/biomet/asad073>.

Computes and tests individual (species, phylogenetic and functional) diversity-area relationships, i.e., how species-, phylogenetic- and functional-diversity varies with spatial scale around the individuals of some species in a community. See applications of these methods in Wiegand et al. (2007) <doi:10.1073/pnas.0705621104> or Chacon-Labella et al. (2016) <doi:10.1007/s00442-016-3547-z>.