Multivariate outlier detection is performed using invariant coordinates where the package offers different methods to choose the appropriate components. ICS is a general multivariate technique with many applications in multivariate analysis. ICSOutlier offers a selection of functions for automated detection of outliers in the data based on a fitted ICS object or by specifying the dataset and the scatters of interest. The current implementation targets data sets with only a small percentage of outliers.

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

Code to specify, run, and then visualize and analyze the results of Ixodidae (hard-bodied ticks) population and infection dynamics models. Such models exist in the literature, but the source code to run them is not always available. IxPopDyMod provides an easy way for these models to be written and shared.

Routines and tools for assessing the quality of content analysis on the basis of the Iota Reliability Concept. The concept is inspired by item response theory and can be applied to any kind of content analysis which uses a standardized coding scheme and discrete categories. It is also applicable for content analysis conducted by artificial intelligence. The package provides reliability measures for a complete scale as well as for every single category. Analysis of subgroup-invariance and error corrections are implemented. This information can support the development process of a coding scheme and allows a detailed inspection of the quality of the generated data. Equations and formulas working in this package are part of Berding et al. (2022)<doi:10.3389/feduc.2022.818365> and Berding and Pargmann (2022) <doi:10.30819/5581>.

This package provides a multivariate Gaussian mixture model framework to integrate multiple types of genomic data and allow modeling of inter-data-type correlations for association analysis. IMIX can be implemented to test whether a disease is associated with genes in multiple genomic data types, such as DNA methylation, copy number variation, gene expression, etc. It can also study the integration of multiple pathways. IMIX uses the summary statistics of association test outputs and conduct integration analysis for two or three types of genomics data. IMIX features statistically-principled model selection, global FDR control and computational efficiency. Details are described in Ziqiao Wang and Peng Wei (2020) <doi:10.1093/bioinformatics/btaa1001>.

This package provides functions to assess the strength and statistical significance of the relationship between species occurrence/abundance and groups of sites [De Caceres & Legendre (2009) <doi:10.1890/08-1823.1>]. Also includes functions to measure species niche breadth using resource categories [De Caceres et al. (2011) <doi:10.1111/J.1600-0706.2011.19679.x>].

Read and process isotopocule data from an Orbitrap Isotope Solutions mass spectrometer. Citation: Kantnerova et al. (Nature Protocols, 2024).

This package provides a GUI designed to support the analysis of financial-economic time series data.

This package provides a test bench for the comparison of missing data imputation methods in uni-variate time series. Imputation methods are compared using different error metrics. Proposed imputation methods and alternative error metrics can be used.

Currently used CI method has its limitation when the test statistics are asymmetrical (chi-square test, F-test) or the model functions are non-linear. It can be overcome by using the likelihood functions for the interval estimation. inteli package now supports interval estimation for the mean, variance, variance ratio, binomial distribution, Poisson distribution, odds ratio, risk difference, relative risk and their likelihood function plots. Testing functions are also provided.

Note that imageData has been superseded by growthPheno'. The package growthPheno incorporates all the functionality of imageData and has functionality not available in imageData', but some imageData functions have been renamed. The imageData package is no longer maintained, but is retained for legacy purposes.

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.

This package provides a variational Bayesian approach for fast integrative clustering and feature selection, facilitating the analysis of multi-view, mixed type, high-dimensional datasets with applications in fields like cancer research, genomics, and more.

This package provides a collection of Item Response Theory (IRT) and Computerized Adaptive Testing (CAT) functions that are used in psychometrics.

The Percentage of Importance Indice (Percentage_I.I.) bases in magnitudes, frequencies, and distributions of occurrence of an event (DEMOLIN-LEITE, 2021) <http://cjascience.com/index.php/CJAS/article/view/1009/1350>. This index can detect the key loss sources (L.S) and solution sources (S.S.), classifying them according to their importance in terms of loss or income gain, on the productive system. The Percentage_I.I. = [(ks1 x c1 x ds1)/SUM (ks1 x c1 x ds1) + (ks2 x c2 x ds2) + (ksn x cn x dsn)] x 100. key source (ks) is obtained using simple regression analysis and magnitude (abundance). Constancy (c) is SUM of occurrence of L.S. or S.S. on the samples (absence = 0 or presence = 1), and distribution source (ds) is obtained using chi-square test. This index has derivations: i.e., i) Loss estimates and solutions effectiveness and ii) Attention and non-attention levels (DEMOLIN-LEITE,2024) <DOI: 10.1590/1519-6984.253215>.

Interface to the OpenGWAS database API <https://api.opengwas.io/api/>. Includes a wrapper to make generic calls to the API, plus convenience functions for specific queries.

Calculates event rates and compares means and variances of groups of interval data corrected for missed arrival observations.

Calculates intraclass correlation coefficient (ICC) for assessing reproducibility of interval-censored data with two repeated measurements (Kovacic and Varnai (2014) <doi:10.1097/EDE.0000000000000139>). ICC is estimated by maximum likelihood from model with one fixed and one random effect (both intercepts). Help in model checking (normality of subjects means and residuals) is provided.

This package provides access to low-level operating system mechanisms for performing atomic operations on shared data structures. Mutexes provide shared and exclusive locks. Semaphores act as counters. Message queues move text strings from one process to another. All these interprocess communication (IPC) tools can optionally block with or without a timeout. Implemented using the cross-platform boost C++ library <https://www.boost.org/doc/libs/release/libs/interprocess/>.

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>.

This package provides a tool to calculate and plot estimates from models in which an interaction between the main predictor and a continuous covariate has been specified. Methods used in the package refer to Harrell Jr FE (2015, ISBN:9783319330396); Durrleman S, Simon R. (1989) <doi:10.1002/sim.4780080504>; Greenland S. (1995) <doi:10.1097/00001648-199507000-00005>.

This package provides a pipeline to annotate a number of peaks from the IDSL.IPA peaklists using an exhaustive chemical enumeration-based approach. This package can perform elemental composition calculations using the following 15 elements : C, B, Br, Cl, K, S, Si, N, H, As, F, I, Na, O, and P.

This package provides functions to facilitate inverse estimation (e.g., calibration) in linear, generalized linear, nonlinear, and (linear) mixed-effects models. A generic function is also provided for plotting fitted regression models with or without confidence/prediction bands that may be of use to the general user. For a general overview of these methods, see Greenwell and Schubert Kabban (2014) <doi:10.32614/RJ-2014-009>.

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>.