IRT-M is a semi-supervised approach based on Bayesian Item Response Theory that produces theoretically identified underlying dimensions from input data and a constraints matrix. The methodology is fully described in Morucci et al. (2024), "Measurement That Matches Theory: Theory-Driven Identification in Item Response Theory Models"'. Details are available at <https://www.cambridge.org/core/journals/american-political-science-review/article/measurement-that-matches-theory-theorydriven-identification-in-item-response-theory-models/395DA1DFE3DCD7B866DC053D7554A30B>.

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

This package provides function to read data from the Igor Pro data analysis program by Wavemetrics'. The data formats supported are Igor packed experiment format ('pxp') and Igor binary wave ('ibw'). See: <https://www.wavemetrics.com/> for details. Also includes functions to load special pxp files produced by the Igor Pro Neuromatic and Nclamp packages for recording and analysing neuronal data. See <https://github.com/SilverLabUCL/NeuroMatic> for details.

The digits of the old version (before 2000 year) of Chinese ID Card Number is 15, this package aims to update to the current version of 18 digits. Besides, this package can help check whether the given ID is right or not.

This package provides a voxel is a representation of a value on a regular, three-dimensional grid; it is the 3D equivalent of a 2D pixel. Voxel data can be visualised with this package using fixed viewpoint isometric cubes for each data point. This package also provides sample voxel data and tools for transforming the data.

This package provides tools to extract information from the Intergovernmental Organizations ('IGO') Database (v3), provided by the Correlates of War Project <https://correlatesofwar.org/>. See also Pevehouse, J. C. et al. (2020) <doi:10.1177/0022343319881175>.

This package provides tools for passing messages between R processes. Shiny examples are provided showing how to perform useful tasks such as: updating reactive values from within a future, progress bars for long running async tasks, and interrupting async tasks based on user input.

This package implements Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. ICE plots refine Friedman's partial dependence plot by graphing the functional relationship between the predicted response and a covariate of interest for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate of interest, suggesting where and to what extent they may exist.

Addresses the log of zero by developing a new family of estimators called iterated Ordinary Least Squares. This family nests standard approaches such as log-linear and Poisson regressions, offers several computational advantages, and corresponds to the correct way to perform the popular log(Y + 1) transformation. For more details about how to use it, see the notebook at: <https://www.davidbenatia.com/>.

The initial basic feasible solution (IBFS) is a significant step to achieve the minimal total cost (optimal solution) of the transportation problem. However, the existing methods of IBFS do not always provide a good feasible solution which can reduce the number of iterations to find the optimal solution. This initial basic feasible solution can be obtained by using any of the following methods. a) North West Corner Method. b) Least Cost Method. c) Row Minimum Method. d) Column Minimum Method. e) Vogel's Approximation Method. etc. For more technical details about the algorithms please refer below URLs. <https://theintactone.com/2018/05/24/ds-u2-topic-8-transportation-problems-initial-basic-feasible-solution/>. <https://www.brainkart.com/article/Methods-of-finding-initial-Basic-Feasible-Solutions_39037/>. <https://myhomeworkhelp.com/row-minima-method/>. <https://myhomeworkhelp.com/column-minima-method/>.

Some interpolation methods taken from Boost': barycentric rational interpolation, modified Akima interpolation, PCHIP (piecewise cubic Hermite interpolating polynomial) interpolation, and Catmull-Rom splines.

This package provides a set of functions for performing null hypothesis testing on samples of persistence diagrams using the theory of permutations. Currently, only two-sample testing is implemented. Inputs can be either samples of persistence diagrams themselves or vectorizations. In the former case, they are embedded in a metric space using either the Bottleneck or Wasserstein distance. In the former case, persistence data becomes functional data and inference is performed using tools available in the fdatest package. Main reference for the interval-wise testing method: Pini A., Vantini S. (2017) "Interval-wise testing for functional data" <doi:10.1080/10485252.2017.1306627>. Main reference for inference on populations of networks: Lovato, I., Pini, A., Stamm, A., & Vantini, S. (2020) "Model-free two-sample test for network-valued data" <doi:10.1016/j.csda.2019.106896>.

Starting from user-supplied institutional data, these scripts transform, aggregate, and reshape the information to produce key-value pair data files that are able to be uploaded to IPEDS (Integrated Postsecondary Education Data System) through their submission portal <https://surveys.nces.ed.gov/ipeds/>. Starting data specifications can be found in the vignettes. Final files are saved locally to a location of the user's choice. User-friendly readable files can also be produced for purposes of data review and validation.

This package provides a framework for analysing inbreeding and heterozygosity-fitness correlations (HFCs) based on microsatellite and SNP markers.

An implementation of the Otsu's Image Segmentation Method described in the paper: "A C++ Implementation of Otsu's Image Segmentation Method". The algorithm is explained at <doi:10.5201/ipol.2016.158>.

This package provides user tokens for ICES web services that require authentication and authorization. Web services covered by this package are ICES VMS database, the ICES DATSU web services, and the ICES SharePoint site <https://www.ices.dk/data/tools/Pages/WebServices.aspx>.

Estimation of joint models for multivariate longitudinal markers (with various distributions available) and survival outcomes (possibly accounting for competing risks) with Integrated Nested Laplace Approximations (INLA). The flexible and user friendly function joint() facilitates the use of the fast and reliable inference technique implemented in the INLA package for joint modeling. More details are given in the help page of the joint() function (accessible via ?joint in the R console) and the vignette associated to the joint() function (accessible via vignette("INLAjoint") in the R console).

Use R to make requests to the US Census Bureau's International Data Base API. Results are returned as R data frames. For more information about the IDB API, visit <https://www.census.gov/data/developers/data-sets/international-database.html>.

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 coefficients of interrater reliability that are generalized to cope with randomly incomplete (i.e. unbalanced) datasets without any imputation of missing values or any (row-wise or column-wise) omissions of actually available data. Applied to complete (balanced) datasets, these generalizations yield the same results as the common procedures, namely the Intraclass Correlation according to McGraw & Wong (1996) \doi10.1037/1082-989X.1.1.30 and the Coefficient of Concordance according to Kendall & Babington Smith (1939) \doi10.1214/aoms/1177732186.

This package performs diagnostic tests of multiplicative interaction models and plots non-linear marginal effects of a treatment on an outcome across different values of a moderator.

Genome-wide gene insertion and deletion rates can be modelled in a maximum likelihood framework with the additional flexibility of modelling potential missing data using the models included within. These models simultaneously estimate insertion and deletion (indel) rates of gene families and proportions of "missing" data for (multiple) taxa of interest. The likelihood framework is utilized for parameter estimation. A phylogenetic tree of the taxa and gene presence/absence patterns (with data ordered by the tips of the tree) are required. See Dang et al. (2016) <doi:10.1534/genetics.116.191973> for more details.

Download and manage data sets of statistical projects and geographic data created by Instituto Nacional de Estadistica y Geografia (INEGI). See <https://www.inegi.org.mx/>.

Let us consider a sample of patients who can suffer from several diseases simultaneously, in a given set of diseases. The goal of the implemented algorithm is to estimate the individual average cost of each disease, starting from the global health costs available for each patient.