Calculates marginal effects based on logistic model objects such as glm or speedglm at the average (default) or at given values using finite differences. It also returns confidence intervals for said marginal effects and the p-values, which can easily be used as input in stargazer. The function only returns the essentials and is therefore much faster but not as detailed as other functions available to calculate marginal effects. As a result, it is highly suitable for large datasets for which other packages may require too much time or calculating power.

The main goal of this package is drawing the membership function of the fuzzy p-value which is defined as a fuzzy set on the unit interval for three following problems: (1) testing crisp hypotheses based on fuzzy data, see Filzmoser and Viertl (2004) <doi:10.1007/s001840300269>, (2) testing fuzzy hypotheses based on crisp data, see Parchami et al. (2010) <doi:10.1007/s00362-008-0133-4>, and (3) testing fuzzy hypotheses based on fuzzy data, see Parchami et al. (2012) <doi:10.1007/s00362-010-0353-2>. In all cases, the fuzziness of data or / and the fuzziness of the boundary of null fuzzy hypothesis transported via the p-value function and causes to produce the fuzzy p-value. If the p-value is fuzzy, it is more appropriate to consider a fuzzy significance level for the problem. Therefore, the comparison of the fuzzy p-value and the fuzzy significance level is evaluated by a fuzzy ranking method in this package.

Random simulations of fuzzy numbers are still a challenging problem. The aim of this package is to provide the respective procedures to simulate fuzzy random variables, especially in the case of the piecewise linear fuzzy numbers (PLFNs, see Coroianua et al. (2013) <doi:10.1016/j.fss.2013.02.005> for the further details). Additionally, the special resampling algorithms known as the epistemic bootstrap are provided (see Grzegorzewski and Romaniuk (2022) <doi:10.34768/amcs-2022-0021>, Grzegorzewski and Romaniuk (2022) <doi:10.1007/978-3-031-08974-9_39>, Romaniuk et al. (2024) <doi:10.32614/RJ-2024-016>) together with the functions to apply statistical tests and estimate various characteristics based on the epistemic bootstrap. The package also includes real-life datasets of epistemic fuzzy triangular and trapezoidal numbers. The fuzzy numbers used in this package are consistent with the FuzzyNumbers package.

This package provides methods to solve Fuzzy Linear Programming Problems with fuzzy constraints (following different approaches proposed by Verdegay, Zimmermann, Werners and Tanaka), fuzzy costs, and fuzzy technological matrix.

This package implements various methods for estimating fractal dimension of time series and 2-dimensional data <doi:10.1214/11-STS370>.

Process raw force-plate data (txt-files) by segmenting them into trials and, if needed, calculating (user-defined) descriptive statistics of variables for user-defined time bins (relative to trigger onsets) for each trial. When segmenting the data a baseline correction, a filter, and a data imputation can be applied if needed. Experimental data can also be processed and combined with the segmented force-plate data. This procedure is suggested by Johannsen et al. (2023) <doi:10.6084/m9.figshare.22190155> and some of the options (e.g., choice of low-pass filter) are also suggested by Winter (2009) <doi:10.1002/9780470549148>.

Estimating the number of factors in Exploratory Factor Analysis (EFA) with out-of-sample prediction errors using a cross-validation scheme. Haslbeck & van Bork (Preprint) <https://psyarxiv.com/qktsd>.

This package provides fast moving-window ("focal") and buffer-based extraction for raster data using the terra package. Automatically selects between a C++ backend (via terra') and a Fast Fourier Transform (FFT) backend depending on problem size. The FFT backend supports sum and mean, while other statistics (e.g., median, min, max, standard deviation) are handled by the terra backend. Supports multiple kernel types (e.g., circle, rectangle, gaussian), with NA handling consistent with terra via na.rm and na.policy'. Operates on SpatRaster objects and returns results with the same geometry.

Use Rmarkdown First method to build your package. Start your package with documentation, functions, examples and tests in the same unique file. Everything can be set from the Rmarkdown template file provided in your project, then inflated as a package. Inflating the template copies the relevant chunks and sections in the appropriate files required for package development.

Special procedures for the imputation of missing fuzzy numbers are still underdeveloped. The goal of the package is to provide the new d-imputation method (DIMP for short, Romaniuk, M. and Grzegorzewski, P. (2023) "Fuzzy Data Imputation with DIMP and FGAIN" RB/23/2023) and covert some classical ones applied in R packages ('missForest','miceRanger','knn') for use with fuzzy datasets. Additionally, specially tailored benchmarking tests are provided to check and compare these imputation procedures with fuzzy datasets.

Interval estimation of the population allele frequency from qPCR analysis based on the restriction enzyme digestion (RED)-DeltaDeltaCq method (Osakabe et al. 2017, <doi:10.1016/j.pestbp.2017.04.003>), as well as general DeltaDeltaCq analysis. Compatible with the Cq measurement of DNA extracted from multiple individuals at once, so called "group-testing", this model assumes that the quantity of DNA extracted from an individual organism follows a gamma distribution. Therefore, the point estimate is robust regarding the uncertainty of the DNA yield.

This is the first package allowing for the estimation, visualization and prediction of the most well-known football models: double Poisson, bivariate Poisson, Skellam, student_t, diagonal-inflated bivariate Poisson, and zero-inflated Skellam. It supports both maximum likelihood estimation (MLE, for static models only) and Bayesian inference. For Bayesian methods, it incorporates several techniques: MCMC sampling with Hamiltonian Monte Carlo, variational inference using either the Pathfinder algorithm or Automatic Differentiation Variational Inference (ADVI), and the Laplace approximation. The package compiles all the CmdStan models once during installation using the instantiate package. The model construction relies on the most well-known football references, such as Dixon and Coles (1997) <doi:10.1111/1467-9876.00065>, Karlis and Ntzoufras (2003) <doi:10.1111/1467-9884.00366> and Egidi, Pauli and Torelli (2018) <doi:10.1177/1471082X18798414>.

This package provides a computationally efficient and statistically rigorous fast Kernel Machine method for multi-kernel analysis. The approach is based on a low-rank approximation to the nuisance effect kernel matrices. The algorithm is applicable to continuous, binary, and survival traits and is implemented using the existing single-kernel analysis software SKAT and coxKM'. coxKM can be obtained from <https://github.com/lin-lab/coxKM>.

Rcpp (free of Java'/'Weka') implementation of FSelector entropy-based feature selection algorithms based on an MDL discretization (Fayyad U. M., Irani K. B.: Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In 13'th International Joint Conference on Uncertainly in Artificial Intelligence (IJCAI93), pages 1022-1029, Chambery, France, 1993.) <https://www.ijcai.org/Proceedings/93-2/Papers/022.pdf> with a sparse matrix support.

Implementation of the Future API <doi:10.32614/RJ-2021-048> on top of the batchtools package. This allows you to process futures, as defined by the future package, in parallel out of the box, not only on your local machine or ad-hoc cluster of machines, but also via high-performance compute ('HPC') job schedulers such as LSF', OpenLava', Slurm', SGE', and TORQUE / PBS', e.g. y <- future.apply::future_lapply(files, FUN = process)'.

Estimation and regularization for covariance matrix of asset returns. For covariance matrix estimation, three major types of factor models are included: macroeconomic factor model, fundamental factor model and statistical factor model. For covariance matrix regularization, four regularized estimators are included: banding, tapering, hard-thresholding and soft- thresholding. The tuning parameters of these regularized estimators are selected via cross-validation.

Collect your data on digital marketing campaigns from Facebook Organic using the Windsor.ai API <https://windsor.ai/api-fields/>.

Does family-based gene by environment interaction tests, joint gene, gene-environment interaction test, and a test of a set of genes conditional on another set of genes.

Package for time value of money calculation, time series analysis and computational finance.

This package provides a simplified interface to the Central Data Repository REST API service made available by the United States Federal Financial Institutions Examination Council ('FFIEC'). Contains functions to retrieve reports of Condition and Income (Call Reports) and Uniform Bank Performance Reports ('UBPR') in list or tidy data frame format for most FDIC insured institutions. See <https://cdr.ffiec.gov/public/Files/SIS611_-_Retrieve_Public_Data_via_Web_Service.pdf> for the official REST API documentation published by the FFIEC'.

Fitting (hierarchical) hidden Markov models to financial data via maximum likelihood estimation. See Oelschläger, L. and Adam, T. "Detecting Bearish and Bullish Markets in Financial Time Series Using Hierarchical Hidden Markov Models" (2021, Statistical Modelling) <doi:10.1177/1471082X211034048> for a reference on the method. A user guide is provided by the accompanying software paper "fHMM: Hidden Markov Models for Financial Time Series in R", Oelschläger, L., Adam, T., and Michels, R. (2024, Journal of Statistical Software) <doi:10.18637/jss.v109.i09>.

This package provides the Big Merge Tracker and COSCI algorithms for convex clustering and feature screening using L1 fusion penalty. See Radchenko, P. and Mukherjee, G. (2017) <doi:10.1111/rssb.12226> and T.Banerjee et al. (2017) <doi:10.1016/j.jmva.2017.08.001> for more details.

The FMT method computes posterior residual variances to be used in the denominator of a moderated t-statistic from a linear model analysis of gene expression data. It is an extension of the moderated t-statistic originally proposed by Smyth (2004) <doi:10.2202/1544-6115.1027>. LOESS local regression and empirical Bayesian method are used to estimate gene specific prior degrees of freedom and prior variance based on average gene intensity levels. The posterior residual variance in the denominator is a weighted average of prior and residual variance and the weights are prior degrees of freedom and residual variance degrees of freedom. The degrees of freedom of the moderated t-statistic is simply the sum of prior and residual variance degrees of freedom.

The function estimates a multivariate regression model for outcomes with network dependence.