Compute several variations of the Implicit Association Test (IAT) scores, including the D scores (Greenwald, Nosek, Banaji, 2003, <doi:10.1037/0022-3514.85.2.197>) and the new scores that were developed using robust statistics (Richetin, Costantini, Perugini, and Schonbrodt, 2015, <doi:10.1371/journal.pone.0129601>).

This package provides a collection of several functions related to construction and analysis of incomplete split-plot designs. The package contains functions to obtain and analyze incomplete split-plot designs for three kinds of situations namely (i) when blocks are complete with respect to main plot treatments and main plots are incomplete with respect to subplot treatments, (ii) when blocks are incomplete with respect to main plot treatments and main plots are complete with respect to subplot treatments and (iii) when blocks are incomplete with respect to main plot treatments and main plots are incomplete with respect to subplot treatments.

Estimate the proportions of the null and the reproducibility and non-reproducibility of the signal group for the input data set. The Bayes factor calculation and EM (Expectation Maximization) algorithm procedures are also included.

The iterLap (iterated Laplace approximation) algorithm approximates a general (possibly non-normalized) probability density on R^p, by repeated Laplace approximations to the difference between current approximation and true density (on log scale). The final approximation is a mixture of multivariate normal distributions and might be used for example as a proposal distribution for importance sampling (eg in Bayesian applications). The algorithm can be seen as a computational generalization of the Laplace approximation suitable for skew or multimodal densities.

This package provides an interface for image recognition using the Google Vision API <https://cloud.google.com/vision/> . Converts API data for features such as object detection and optical character recognition to data frames. The package also includes functions for analyzing image annotations.

Kappa statistics is one of the most used methods to evaluate the effectiveness of inpsections based on attribute assessments in industry. However, its estimation by available methods does not provide its "real" or "intrinstic" value. This package provides functions for the computation of the intrinsic kappa value as it is described in: Rafael Sanchez-Marquez, Frank Gerhorst and David Schindler (2023) "Effectiveness of quality inspections of attributive characteristics â A novel and practical method for estimating the â intrinsicâ value of kappa based on alpha and beta statistics." <doi:10.1016/j.cie.2023.109006>.

Display a 2D-matrix data as a interactive zoomable gray-scale image viewer, providing tools for manual data inspection. The viewer window shows cursor guiding lines and a corresponding data slices for both axes at the current cursor position. A tool-bar allows adjusting image display brightness/contrast through WebGL filters and performing basic high-pass/low-pass filtering.

This package provides methods for quantifying temporal and spatial causality through information flow, and decomposing it into unique, redundant, and synergistic components, following the framework described in Martinez-Sanchez et al. (2024) <doi:10.1038/s41467-024-53373-4>.

This package provides a toolkit for idionomic science, a research philosophy that places the unit of the ensemble (individual/couple/group) at the center of analysis. Rather than assuming a common distribution, a similar enough process for each unit, and fitting a single model to the whole ensemble, idionomic methods model each unit separately, then aggregate upward if sensible. The group-level picture emerges from individual results, not the other way around, while explicitly evaluating whether aggregation is reasonable given the measured level of heterogeneity of effects. The package is built around intensive longitudinal data where each participant contributes a time series. It provides a pipeline from preprocessing through modeling to group-level summaries. Current functions: data quality screening (i_screener()), within-person standardization (pmstandardize()), linear detrending (i_detrender()), per-subject ARIMAX (AutoRegressive Integrated Moving Average with eXogenous inputs) modeling and meta-analysis (iarimax()), individual p-values (i_pval()), Sign Divergence and Equisyncratic Null tests (sden_test()), and directed loop detection (looping_machine()). Methods are described in Hernandez et al. (2024) <doi:10.1007/978-3-030-77644-2_136-1>, Ciarrochi et al. (2024) <doi:10.1007/s10608-024-10486-w>, and Sahdra et al. (2024) <doi:10.1016/j.jcbs.2024.100728>.

This package provides access to granular socioeconomic indicators from the Spanish Statistical Office (INE) Household Income Distribution Atlas. The package downloads and processes data from a companion GitHub repository (<https://github.com/pablogguz/ineAtlas.data/>) which contains processed versions of the official INE Atlas data. Functions are provided to fetch data at multiple geographic levels (municipalities, districts, and census tracts), including income indicators, demographic characteristics, and inequality metrics. The data repository is updated every year when new releases are published by INE.

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

The correction is achieved under the assumption that non-migrating cells of the essay approximately form a quadratic flow profile due to frictional effects, compare law of Hagen-Poiseuille for flow in a tube. The script fits a conical plane to give xyz-coordinates of the cells. It outputs the number of migrated cells and the new corrected coordinates.

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

This package contains implementations of the integrative Cox model with uncertain event times proposed by Wang, et al. (2020) <doi:10.1214/19-AOAS1287>, the regularized Cox cure rate model with uncertain event status proposed by Wang, et al. (2023) <doi:10.1007/s12561-023-09374-w>, and other survival analysis routines including the Cox cure rate model proposed by Kuk and Chen (1992) <doi:10.1093/biomet/79.3.531> via an EM algorithm proposed by Sy and Taylor (2000) <doi:10.1111/j.0006-341X.2000.00227.x>, the regularized Cox cure rate model with elastic net penalty following Masud et al. (2018) <doi:10.1177/0962280216677748>.

Download data from ISTAT (Italian Institute of Statistics) database, both old and new provider (respectively, <http://dati.istat.it/> and <https://esploradati.istat.it/databrowser/>). Additional functions for manipulating data are provided. Moreover, a shiny application called shinyIstat can be used to search, download and filter datasets more easily.

Estimates weights to make a continuous-valued exposure statistically independent of a vector of pre-treatment covariates using the method proposed in Huling, Greifer, and Chen (2021) <arxiv:2107.07086>.

This package performs Goodness of Fit for regression models using Integrated Regression method. Works for several different fitting techniques.

This package contains a number of infix binary operators that may be useful in day to day practices.

This package provides a fast (C) implementation of the iterative proportional fitting procedure.

This package provides tools for analysing inflation dynamics. Computes weighted contributions of price index components, core inflation measures (trimmed mean, weighted median, exclusion-based) following Bryan and Cecchetti (1994) <doi:10.1016/0304-3932(94)90030-2>, inflation persistence via sum-of-AR-coefficients, diffusion indices, Phillips curve estimation, breakeven inflation, and trend inflation using the Beveridge-Nelson decomposition and Hodrick-Prescott filter. All functions are pure computation and work with price data from any source.

The methods in this package adds to the functionality of the intamap package, such as bias correction and network optimization. Pebesma et al (2010) gives an overview of the methods behind and possible usage <doi:10.1016/j.cageo.2010.03.019>.

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.

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.

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.