This package provides fast application of image filters to data matrices, using R and C++ algorithms.

Generates Rd files from R source code with comments. The main features of the default syntax are that (1) docs are defined in comments near the relevant code, (2) function argument names are not repeated in comments, and (3) examples are defined in R code, not comments. It is also easy to define a new syntax.

Non-parametric resampling-based inference tests for ExPosition.

Paquete creado con el fin de facilitar el cálculo y distribución del à ndice Socio Material Territorial (ISMT), elaborado por el Observatorio de Ciudades UC. La metodologà a completa está disponible en "ISMT" (<https://ideocuc-ocuc.hub.arcgis.com/datasets/6ed956450cfc4293b7d90df3ce3474e4/about>) [Observatorio de Ciudades UC (2019)]. || Package created to facilitate the calculation and distribution of the Socio-Material Territorial Index by Observatorio de Ciudades UC. The full methodology is available at "ISMT" (<https://ideocuc-ocuc.hub.arcgis.com/datasets/6ed956450cfc4293b7d90df3ce3474e4/about>) [Observatorio de Ciudades UC (2019)].

Reconstruct birth-year specific probabilities of immune imprinting to influenza A, using the methods of Gostic et al. (2016) <doi:10.1126/science.aag1322>. Plot, save, or export the calculated probabilities for use in your own research. By default, the package calculates subtype-specific imprinting probabilities, but with user-provided frequency data, it is possible to calculate probabilities for arbitrary kinds of primary exposure to influenza A, including primary vaccination and exposure to specific clades, strains, etc.

Four datasets are provided here from the Intendo game Super Jetroid'. It is data from the 2015 year of operation and it comprises a revenue table ('all_revenue'), a daily users table ('users_daily'), a user summary table ('user_summary'), and a table with data on all user sessions ('all_sessions'). These core datasets come in different sizes, and, each of them has a variant that was intentionally made faulty (totally riddled with errors and inconsistencies). This suite of tables is useful for testing with packages that focus on data validation and data documentation.

Simple plotting function(s) for exploratory data analysis with flexible options allowing for easy plot customisation. The goal is to make it easy for beginners to start exploring a dataset through simple R function calls, as well as provide a similar interface to summary statistics and inference information. Includes functionality to generate interactive HTML-driven graphs. Used by iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions.

Generates three inter-related genomic datasets: methylation, gene expression and protein expression having user specified cluster patterns. The simulation utilizes the realistic inter- and intra- relationships from real DNA methylation, mRNA expression and protein expression data from the TCGA ovarian cancer study, Chalise (2016) <doi:10.1016/j.cmpb.2016.02.011>.

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.

In classification problems a monotone relation between some predictors and the classes may be assumed. In this package isoboost we propose new boosting algorithms, based on LogitBoost, that incorporate this isotonicity information, yielding more accurate and easily interpretable rules.

Using embedded sdmx queries, get the data of more than 150 000 insee series from bdm macroeconomic database.

This package provides a collection of Irucka Embry's miscellaneous USGS functions (processing .exp and .psf files, statistical error functions, "+" dyadic operator for use with NA, creating ADAPS and QW spreadsheet files, calculating saturated enthalpy). Irucka created these functions while a Cherokee Nation Technology Solutions (CNTS) United States Geological Survey (USGS) Contractor and/or USGS employee.

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.

Applying the family of the Bayesian Expectation-Maximization-Maximization (BEMM) algorithm to estimate: (1) Three parameter logistic (3PL) model proposed by Birnbaum (1968, ISBN:9780201043105); (2) four parameter logistic (4PL) model proposed by Barton & Lord (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x>; (3) one parameter logistic guessing (1PLG) and (4) one parameter logistic ability-based guessing (1PLAG) models proposed by San Martà n et al (2006) <doi:10.1177/0146621605282773>. The BEMM family includes (1) the BEMM algorithm for 3PL model proposed by Guo & Zheng (2019) <doi:10.3389/fpsyg.2019.01175>; (2) the BEMM algorithm for 1PLG model and (3) the BEMM algorithm for 1PLAG model proposed by Guo, Wu, Zheng, & Chen (2021) <doi:10.1177/0146621621990761>; (4) the BEMM algorithm for 4PL model proposed by Zheng, Guo, & Kern (2021) <doi:10.1177/21582440211052556>; and (5) their maximum likelihood estimation versions proposed by Zheng, Meng, Guo, & Liu (2018) <doi:10.3389/fpsyg.2017.02302>. Thus, both Bayesian modal estimates and maximum likelihood estimates are available.

Contain code to work with a C struct, in short cgeneric, to define a Gaussian Markov random (GMRF) model. The cgeneric contain code to specify GMRF elements such as the graph and the precision matrix, and also the initial and prior for its parameters, useful for model inference. It can be accessed from a C program and is the recommended way to implement new GMRF models in the INLA package (<https://www.r-inla.org>). The INLAtools implement functions to evaluate each one of the model specifications from R. The implemented functionalities leverage the use of cgeneric models and provide a way to debug the code as well to work with the prior for the model parameters and to sample from it. A very useful functionality is the Kronecker product method that creates a new model from multiple cgeneric models. It also works with the rgeneric, the R version of the cgeneric intended to easy try implementation of new GMRF models. The Kronecker between two cgeneric models was used in Sterrantino et. al. (2024) <doi:10.1007/s10260-025-00788-y>, and can be used to build the spatio-temporal intrinsic interaction models for what the needed constraints are automatically set.

Estimation of the most-left informative set of gross returns (i.e., the informative set). The procedure to compute the informative set adjusts the method proposed by Mariani et al. (2022a) <doi:10.1007/s11205-020-02440-6> and Mariani et al. (2022b) <doi:10.1007/s10287-022-00422-2> to gross returns of financial assets. This is accomplished through an adaptive algorithm that identifies sub-groups of gross returns in each iteration by approximating their distribution with a sequence of two-component log-normal mixtures. These sub-groups emerge when a significant change in the distribution occurs below the median of the financial returns, with their boundary termed as the â change point" of the mixture. The process concludes when no further change points are detected. The outcome encompasses parameters of the leftmost mixture distributions and change points of the analyzed financial time series. The functionalities of the INFOSET package include: (i) modelling asset distribution detecting the parameters which describe left tail behaviour (infoset function), (ii) clustering, (iii) labeling of the financial series for predictive and classification purposes through a Left Risk measure based on the first change point (LR_cp function) (iv) portfolio construction (ptf_construction function). The package also provide a specific function to construct rolling windows of different length size and overlapping time.

This package provides a non-parametric effect size measure capturing changes in central tendency or shape of data distributions. The package provides the necessary functions to calculate and plot the Impact effect size measure between two groups.

This package provides a simplified version of the IDSL.UFA package to calculate isotopic profiles and adduct formulas from molecular formulas with no dependency on other R packages for online tools and educational mass spectrometry courses. The IDSL.SUFA package also provides an ancillary module to process user-defined adduct formulas.

This package provides functions and classes to compute, handle and visualise incidence from dated events for a defined time interval. Dates can be provided in various standard formats. The class incidence2 is used to store computed incidence and can be easily manipulated, subsetted, and plotted.

Utility functions that implement and automate common sets of validation tasks. These functions are particularly useful to validate inputs, intermediate objects and output values in user-defined functions, resulting in tidier and less verbose functions.

Authentication can be the most difficult part about working with a new API. ibmAcousticR facilitates making a connection to the IBM Acoustic email campaign management API and executing various queries. The IBM Acoustic API documentation is available at <https://developer.ibm.com/customer-engagement/docs/>. This package is not supported by IBM'.

An open source library for face detection in images. Provides a pretrained convolutional neural network based on <https://github.com/ShiqiYu/libfacedetection> which can be used to detect faces which have size greater than 10x10 pixels.

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.

Download ifo business survey data and more time series from ifo institute <https://www.ifo.de/en/ifo-time-series>.