Computes the double bootstrap as discussed in McKnight, McKean, and Huitema (2000) <doi:10.1037/1082-989X.5.1.87>. The double bootstrap method provides a better fit for a linear model with autoregressive errors than ARIMA when the sample size is small.

An R interface to the codediff JavaScript library (a copy of which is included in the package, see <https://github.com/danvk/codediff.js> for information). Allows for visualization of the difference between 2 files, usually text files or R scripts, in a browser.

Includes various functions for playing drum sounds. beat() plays a drum sound from one of the six included drum kits. tempo() sets spacing between calls to beat() in bpm. Together the two functions can be used to create many different drum patterns.

This package provides tools for working with iEEG matrix data, including downloading curated iEEG data from OSF (The Open Science Framework <https://osf.io/>) (EpochDownloader()), making new objects (Epoch()), processing (crop() and resample()), and visualizing the data (plot()).

This package provides a model-independent factor importance ranking and selection procedure based on total Sobol indices. Please see Huang and Joseph (2025) <doi:10.1080/00401706.2025.2483531>. This research is supported by U.S. National Science Foundation grants DMS-2310637 and DMREF-1921873.

Includes several statistical methods for the estimation of parameters and high quantiles of river flow distributions. The focus is on regional estimation based on homogeneity assumptions and computed from multivariate observations (multiple measurement stations). For details see Kinsvater et al. (2017) <arXiv:1701.06455>.

FDR functions for permutation-based estimators, including pi0 as well as FDR confidence intervals. The confidence intervals account for dependencies between tests by the incorporation of an overdispersion parameter, which is estimated from the permuted data. Also included are options for an analog parametric approach.

Network meta-analyses (mixed treatment comparisons) in the Bayesian framework using JAGS. Includes methods to assess heterogeneity and inconsistency, and a number of standard visualizations. van Valkenhoef et al. (2012) <doi:10.1002/jrsm.1054>; van Valkenhoef et al. (2015) <doi:10.1002/jrsm.1167>.

Performing the different steps of gene set enrichment meta-analysis. It provides different functions that allow the application of meta-analysis based on the combination of effect sizes from different pathways in different studies to obtain significant pathways that are common to all of them.

This package provides functions for obtaining generalized normal/exponential power distribution probabilities, quantiles, densities and random deviates. The generalized normal/exponential power distribution was introduced by Subbotin (1923) and rediscovered by Nadarajah (2005). The parametrization given by Nadarajah (2005) <doi:10.1080/02664760500079464> is used.

An open-source R package to deploys reproducible and flexible labels using layers. The huito package is part of the inkaverse project for developing different procedures and tools used in plant science and experimental designs. Learn more about the inkaverse project at <https://inkaverse.com/>.

Mediation analysis is used to identify and quantify intermediate effects from factors that intervene the observed relationship between an exposure/predicting variable and an outcome. We use a Bayesian adaptive lasso method to take care of the hierarchical structures and high dimensional exposures or mediators.

Estimates marginal likelihood from a posterior sample using the method described in Wang et al. (2023) <doi:10.1093/sysbio/syad007>, which does not require evaluation of any additional points and requires only the log of the unnormalized posterior density for each sampled parameter vector.

Designed to query Longitudinal Employer-Household Dynamics (LEHD) workplace/residential association and origin-destination flat files and optionally aggregate Census block-level data to block group, tract, county, or state. Data comes from the LODES FTP server <https://lehd.ces.census.gov/data/lodes/LODES8/>.

It offers random-forest-based functions to impute clustered incomplete data. The package is tailored for but not limited to imputing multitissue expression data, in which a gene's expression is measured on the collected tissues of an individual but missing on the uncollected tissues.

Conducts moderated nonlinear factor analysis (e.g., Curran et al., 2014, <doi:10.1080/00273171.2014.889594>). Regularization methods are implemented for assessing non-invariant items. Currently, the package includes dichotomous items and unidimensional item response models. Extensions will be included in future package versions.

Fast moment-based hierarchical model fitting. Implements methods from the papers "Fast Moment-Based Estimation for Hierarchical Models," by Perry (2017) and "Fitting a Deeply Nested Hierarchical Model to a Large Book Review Dataset Using a Moment-Based Estimator," by Zhang, Schmaus, and Perry (2018).

Routines for enumerating all existing nonnegative integer solutions of a linear Diophantine equation. The package provides routines for solving 0-1, bounded and unbounded knapsack problems; 0-1, bounded and unbounded subset sum problems; additive partitioning of natural numbers; and one-dimensional bin-packing problem.

Comprehensive toolkit for generating various numerical features of protein sequences described in Xiao et al. (2015) <DOI:10.1093/bioinformatics/btv042>. For full functionality, the software ncbi-blast+ is needed, see <https://blast.ncbi.nlm.nih.gov/doc/blast-help/downloadblastdata.html> for more information.

Construct parser combinator functions, higher order functions that parse input. Construction of such parsers is transparent and easy. Their main application is the parsing of structured text files like those generated by laboratory instruments. Based on a paper by Hutton (1992) <doi:10.1017/S0956796800000411>.

This package provides functions for fitting Cliff-Ord-type spatial autoregressive models with and without heteroskedastic innovations using Generalized Method of Moments estimation are provided. Some support is available for fitting spatial HAC models, and for fitting with non-spatial endogeneous variables using instrumental variables.

Evaluating the consistency assumption of Network Meta-Analysis both globally and locally in the Bayesian framework. Inconsistencies are located by applying Bayesian variable selection to the inconsistency factors. The implementation of the method is described by Seitidis et al. (2023) <doi:10.1002/sim.9891>.

Get the most appropriate autoregressive integrated moving average, generalized auto-regressive conditional heteroscedasticity and Markov switching GARCH model. For method details see Haas M, Mittnik S, Paolella MS (2004). <doi:10.1093/jjfinec/nbh020>, Bollerslev T (1986). <doi:10.1016/0304-4076(86)90063-1>.

Implementation and forecasting univariate time series data using the Support Vector Machine model. Support Vector Machine is one of the prominent machine learning approach for non-linear time series forecasting. For method details see Kim, K. (2003) <doi:10.1016/S0925-2312(03)00372-2>.