Merges and downloads SPSS data from different International Large-Scale Assessments (ILSA), including: Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), and other studies from <https://www.iea.nl/>.

Comprehensive computational routines for random data generation, Maximum Likelihood Estimation (MLE), Maximum Product of Spacings Estimation (MPSE), and MCMC Bayesian estimation under the Improved Adaptive Type-II Progressive Censoring Scheme (IAT-II PCS). Users can supply custom probability density functions (PDF), cumulative distribution functions (CDF), survival functions, parameter ranges, and progressive censoring plans for any continuous univariate lifetime distribution, or rely on built-in parametric models (e.g., Generalized Exponential). Point estimation methods include MLE via optimization algorithms (Broyden-Fletcher-Goldfarb-Shanno (BFGS), Newton-Raphson (NR), Nelder-Mead (NM), Conjugate Gradients (CG), L-BFGS-B, Simulated Annealing (SANN), and Berndt-Hall-Hall-Hausman (BHHH)) and MPSE. Bayesian inference utilizes Metropolis-Hastings within Gibbs sampling under Squared Error Loss (SEL) and LINEX Loss (LL) functions to compute point estimates and Highest Posterior Density (HPD) credible intervals. Asymptotic confidence intervals for parameters, reliability, and hazard rate functions are constructed using asymptotic normality and delta method. Methods are based on Dev and Chacko (2026, Journal of the Iranian Statistical Society, 25, 1-29), Yan, Zhang, and Dong (2021, Journal of Computational and Applied Mathematics, 381, 113022, <doi:10.1016/j.cam.2020.113022>), Ng, Kundu, and Chan (2004, Naval Research Logistics, 51, 1145-1168, <doi:10.1002/nav.20045>), Cheng and Amin (1983, Journal of the Royal Statistical Society Series B, 45, 394-403, <doi:10.1111/j.2517-6161.1983.tb01268.x>), Kundu and Gupta (1999, Australian & New Zealand Journal of Statistics, 41, 173-188, <doi:10.1111/1467-842X.00072>), and Berndt, Hall, Hall, and Hausman (1974, Annals of Economic and Social Measurement, 3, 653-665).

It performs interlaboratory studies (ILS) to detect those laboratories that provide non-consistent results when comparing to others. It permits to work simultaneously with various testing materials, from standard univariate, and functional data analysis (FDA) perspectives. The univariate approach based on ASTM E691-08 consist of estimating the Mandel's h and k statistics to identify those laboratories that provide more significant different results, testing also the presence of outliers by Cochran and Grubbs tests, Analysis of variance (ANOVA) techniques are provided (F and Tuckey tests) to test differences in means corresponding to different laboratories per each material. Taking into account the functional nature of data retrieved in analytical chemistry, applied physics and engineering (spectra, thermograms, etc.). ILS package provides a FDA approach for finding the Mandel's k and h statistics distribution by smoothing bootstrap resampling.

Graphical User Interface allowing to determine the concentration in the sample in CFU per mL or in number of copies per mL provided to qPCR results after with or without PMA treatment. This package is simply to use because no knowledge in R commands is necessary. A graphic represents the standard curve, and a table containing the result for each sample is created.

The Iterative Cumulative Sum of Squares (ICSS) algorithm by Inclan/Tiao (1994) <https://www.jstor.org/stable/2290916> detects multiple change points, i.e. structural break points, in the variance of a sequence of independent observations. For series of moderate size (i.e. 200 observations and beyond), the ICSS algorithm offers results comparable to those obtained by a Bayesian approach or by likelihood ration tests, without the heavy computational burden required by these approaches.

Includes a collection of shiny applications to demonstrate or to explore fundamental item response theory (IRT) concepts such as estimation, scoring, and multidimensional IRT models.

Send emails using the mailgun api. To use this package you will need an account from <https://www.mailgun.com> .

API wrapper that contains functions to retrieve data from the IsoMemo partnership databases. Web services for API: <https://isomemodb.com/api/v1/iso-data>.

An R6-based client to facilitate interaction with the Statistics Portugal (Instituto Nacional de Estatistica - INE) API (<https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_api&INST=322751522&xlang=en>).

Fit a predictive model using iteratively reweighted boosting (IRBoost) to minimize robust loss functions within the CC-family (concave-convex). This constitutes an application of iteratively reweighted convex optimization (IRCO), where convex optimization is performed using the functional descent boosting algorithm. IRBoost assigns weights to facilitate outlier identification. Applications include robust generalized linear models and robust accelerated failure time models. Wang (2025) <doi:10.6339/24-JDS1138>.

Leveraging information-theoretic measures like mutual information and v-measure to quantify spatial associations between patterns (Nowosad and Stepinski (2018) <doi:10.1080/13658816.2018.1511794>; Bai, H. et al. (2023) <doi:10.1080/24694452.2023.2223700>).

This package provides functions to fetch market data, search historical prices, execute trades, and get account details from the IG Trading REST API <https://labs.ig.com>. Returns tidy tibbles for easy analysis. Trading contracts for difference (CFDs), options and spread bets carries a high risk of losing money. This package is not financial or trading advice.

Plot idiograms of karyotypes, plasmids, circular chr. having a set of data.frames for chromosome data and optionally mark data. Two styles of chromosomes can be used: without or with visible chromatids. Supports micrometers, cM and Mb or any unit. Three styles of centromeres are available: triangle, rounded and inProtein; and six styles of marks are available: square (squareLeft), dots, cM (cMLeft), cenStyle, upArrow (downArrow), exProtein (inProtein); its legend (label) can be drawn inline or to the right of karyotypes. Idiograms can also be plotted in concentric circles. It is possible to calculate chromosome indices by Levan et al. (1964) <doi:10.1111/j.1601-5223.1964.tb01953.x>, karyotype indices of Watanabe et al. (1999) <doi:10.1007/PL00013869> and Romero-Zarco (1986) <doi:10.2307/1221906> and classify chromosomes by morphology Guerra (1986) and Levan et al. (1964).

R dependency injection framework. Dependency injection allows a program design to follow the dependency inversion principle. The user delegates to external code (the injector) the responsibility of providing its dependencies. This separates the responsibilities of use and construction.

This package provides a collection of functions for creating color schemes. Used to support packages and scripts written by researchers at the United States Geological Survey (USGS) Idaho National Laboratory Project Office.

For different linear dimension reduction methods like principal components analysis (PCA), independent components analysis (ICA) and supervised linear dimension reduction tests and estimates for the number of interesting components (ICs) are provided.

Estimating the mean and variance of a covariate for the complier, never-taker and always-taker subpopulation in the context of instrumental variable estimation. This package implements the method described in Marbach and Hangartner (2020) <doi:10.1017/pan.2019.48> and Hangartner, Marbach, Henckel, Maathuis, Kelz and Keele (2021) <doi:10.48550/arXiv.2103.06328>.

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/>.

This package provides methods for detecting influential subjects in longitudinal data, particularly when observations are collected at irregular time points. The package identifies subjects whose response trajectories deviate substantially from population-level patterns, helping to diagnose anomalies and undue influence on model estimates.

This package provides functions to parse strings with ISO8601 dates, times, and date-times into R-objects. Additionally, there are functions to determine the type of ISO8601 string and to standardise ISO8601 strings.

This package implements Bayesian models to analyze data from tracer addition experiments. The implemented method was originally described in the article "A New Method to Reconstruct Quantitative Food Webs and Nutrient Flows from Isotope Tracer Addition Experiments" by López-Sepulcre et al. (2020) <doi:10.1086/708546>.

This package provides utilities to retrieve and manage ICD-10 code descriptions from Excel files. Supports vectors, data frame columns, and association rule outputs (e.g., arules objects) that require mapping to ICD-10 descriptions. Designed to simplify processing of healthcare datasets.

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>.

The Dynamic Time Warping (DTW) distance measure for time series allows non-linear alignments of time series to match similar patterns in time series of different lengths and or different speeds. IncDTW is characterized by (1) the incremental calculation of DTW (reduces runtime complexity to a linear level for updating the DTW distance) - especially for life data streams or subsequence matching, (2) the vector based implementation of DTW which is faster because no matrices are allocated (reduces the space complexity from a quadratic to a linear level in the number of observations) - for all runtime intensive DTW computations, (3) the subsequence matching algorithm runDTW, that efficiently finds the k-NN to a query pattern in a long time series, and (4) C++ in the heart. For details about DTW see the original paper "Dynamic programming algorithm optimization for spoken word recognition" by Sakoe and Chiba (1978) <DOI:10.1109/TASSP.1978.1163055>. For details about this package, Dynamic Time Warping and Incremental Dynamic Time Warping please see "IncDTW: An R Package for Incremental Calculation of Dynamic Time Warping" by Leodolter et al. (2021) <doi:10.18637/jss.v099.i09>.