This package provides functions to perform simulations of ANOVA designs of up to three factors. Calculates the observed power and average observed effect size for all main effects and interactions in the ANOVA, and all simple comparisons between conditions. Includes functions for analytic power calculations and additional helper functions that compute effect sizes for ANOVA designs, observed error rates in the simulations, and functions to plot power curves. Please see Lakens, D., & Caldwell, A. R. (2021). "Simulation-Based Power Analysis for Factorial Analysis of Variance Designs". <doi:10.1177/2515245920951503>.

This package provides tools for splitting large Excel worksheets into multiple smaller sheets based on a specified number of rows per chunk. The package reads Excel files, partitions the data efficiently using the data.table package, and writes the resulting subsets into a single workbook with multiple sheets using the openxlsx package. This is useful for handling large datasets, preparing data for reporting, and exporting manageable Excel files for downstream analysis.

An R data package containing setlists from all Bruce Springsteen concerts over 1973-2021. Also includes all his song details such as lyrics and albums. Data extracted from: <http://brucebase.wikidot.com/>.

Generalized framework for data generation, Maximum Likelihood Estimation, and Bayesian estimation of stress-strength reliability R = P(Y < X) for arbitrary continuous distributions under censoring schemes based on Chapter 9 of Balakrishnan', Cramer', and Kundu (2023) <ISBN:978-0-12-398387-9>. Users provide probability density functions, cumulative distribution functions, survival functions, support bounds, parameter ranges, and sample sizes. Implements data generation under Type-I, Type-II, progressive Type-II, Type-I hybrid, Type-II hybrid, generalized hybrid, progressive hybrid, joint, block random, middle, and truncation censoring schemes, accompanied by diagnostic histograms, dot plots, and autocorrelation plots. Maximum Likelihood Estimation supports optimization routines including Newton-Raphson', Broyden'-'Fletcher'-'Goldfarb'-'Shanno ('BFGS'), BFGS in R ('BFGSR'), Berndt'-'Hall'-'Hall'-'Hausman ('BHHH'), Simulated Annealing ('SANN'), Conjugate Gradients ('CG'), and Nelder'-'Mead ('NM'), returning summaries ('AIC', coef', logLik', nIter', stdEr', summary, vcov'). Bayesian estimation of stress-strength reliability R = P(Y < X) is performed via Gibbs sampling, Metropolis-Hastings algorithm, Importance Sampling, and Lindley approximation (1980). Methods and censoring schemes are described in Balakrishnan', Cramer', and Kundu (2023, ISBN:978-0-12-398387-9), Lindley (1980) <doi:10.1111/j.2517-6161.1980.tb01102.x>, Geweke (1989) <doi:10.2307/2290062>, Metropolis (1953) <doi:10.1063/1.1699114>, Hastings (1970) <doi:10.1093/biomet/57.1.97>, Geman and Geman (1984) <doi:10.1109/TPAMI.1984.4767596>, Kundu and Gupta (2005) <doi:10.1016/j.jspi.2004.09.006>, Kundu and Gupta (2006) <doi:10.1016/j.csda.2005.02.007>, Berndt', Hall', Hall', and Hausman (1974) <doi:10.3386/t0003>, Fletcher (1987, ISBN:978-0-471-91547-8), and Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308>.

This package provides a collection of self-labeled techniques for semi-supervised classification. In semi-supervised classification, both labeled and unlabeled data are used to train a classifier. This learning paradigm has obtained promising results, specifically in the presence of a reduced set of labeled examples. This package implements a collection of self-labeled techniques to construct a classification model. This family of techniques enlarges the original labeled set using the most confident predictions to classify unlabeled data. The techniques implemented can be applied to classification problems in several domains by the specification of a supervised base classifier. At low ratios of labeled data, it can be shown to perform better than classical supervised classifiers.

This package performs the Partial Least-Squares ('PLS') algorithm for functional data through the concept of active area integration. This approach builds upon the basis expansion methods for functional PLS regression described in Aguilera et al. (2010) <doi:10.1016/j.chemolab.2010.09.007>. The package seamlessly handles both Scalar Functional Data ('SFD') and Categorical Functional Data ('CFD'), providing interpretable regression curves even for discrete state changes. It was developed during a PhD thesis between DECATHLON and French research institute INRIA 2022-2026. The SmoothPLS method does not directly decompose the data into a basis; rather, it assumes the data is known as precisely as desired, and for every PLS component, the weight functions are decomposed into the basis. For both single-state and multi-state CFD as well as SFD', the algorithm is implemented for a scalar response. To provide a baseline, a naive PLS method on time-value functions and standard Functional PLS are also implemented.

This package provides functions that compute the spatial covariance matrix for the matern and power classes of spatial models, for data that arise on rectangular units. This code can also be used for the change of support problem and for spatial data that arise on irregularly shaped regions like counties or zipcodes by laying a fine grid of rectangles and aggregating the integrals in a form of Riemann integration.

Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions by using the alternating direction method of multipliers algorithm (Wang and Huang, 2017, <DOI:10.1080/10618600.2016.1157483>). The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D.

This package provides a set of spatial accessibility measures from a set of locations (demand) to another set of locations (supply). It aims, among others, to support research on spatial accessibility to health care facilities. Includes the locations and some characteristics of major public hospitals in Greece.

An opinionated interface to Amazon Web Services <https://aws.amazon.com>, with functions for interacting with IAM (Identity and Access Management), S3 (Simple Storage Service), RDS (Relational Data Service), Redshift, and Billing. Lower level functions ('aws_ prefix) are for do it yourself workflows, while higher level functions ('six_ prefix) automate common tasks.

Formulates a sparse distance weighted discrimination (SDWD) for high-dimensional classification and implements a very fast algorithm for computing its solution path with the L1, the elastic-net, and the adaptive elastic-net penalties. More details about the methodology SDWD is seen on Wang and Zou (2016) (<doi:10.1080/10618600.2015.1049700>).

This package provides several methods to integrate functions over the unit sphere and ball in n-dimensional Euclidean space. Routines for converting to/from multivariate polar/spherical coordinates are also provided.

Perform a probabilistic linkage of two data files using a scaling procedure using the methods described in Goldstein, H., Harron, K. and Cortina-Borja, M. (2017) <doi:10.1002/sim.7287>.

This package provides a programmatic interface to many species occurrence data sources, including Global Biodiversity Information Facility ('GBIF'), iNaturalist', eBird', Integrated Digitized Biocollections ('iDigBio'), VertNet', Ocean Biogeographic Information System ('OBIS'), and Atlas of Living Australia ('ALA'). Includes functionality for retrieving species occurrence data, and combining those data.

Computationally efficient tools for high dimensional predictive modeling (regression and classification). SAM is short for sparse additive modeling, and adopts the computationally efficient basis spline technique. We solve the optimization problems by various computational algorithms including the block coordinate descent algorithm, fast iterative soft-thresholding algorithm, and newton method. The computation is further accelerated by warm-start and active-set tricks.

This package implements the structural forest methodology for the heterogeneous newsvendor model. The package provides tools to prepare data, fit honest newsvendor trees and forests, and obtain point and distributional predictions for demand decisions under uncertainty.

This package provides functions for fitting multi-state semi-Markov models to longitudinal data. A parametric maximum likelihood estimation method adapted to deal with Exponential, Weibull and Exponentiated Weibull distributions is considered. Right-censoring can be taken into account and both constant and time-varying covariates can be included using a Cox proportional model. Reference: A. Krol and P. Saint-Pierre (2015) <doi:10.18637/jss.v066.i06>.

This package provides functions for computing geographically weighted regressions are provided, based on work by Chris Brunsdon, Martin Charlton and Stewart Fotheringham.

Nonparametric estimation of Spearman's rank correlation with bivariate survival (right-censored) data as described in Eden, S.K., Li, C., Shepherd B.E. (2021), Nonparametric Estimation of Spearman's Rank Correlation with Bivariate Survival Data, Biometrics (under revision). The package also provides functions that visualize bivariate survival data and bivariate probability mass function.

Function library for processing collective movement data (e.g. fish schools, ungulate herds, baboon troops) collected from GPS trackers or computer vision tracking software.

Markov chain Monte Carlo samplers for posterior simulations of conjugate Bayesian nonparametric mixture models. Functionality is provided for Gibbs sampling as in Algorithm 3 of Neal (2000) <DOI:10.1080/10618600.2000.10474879>, restricted Gibbs merge-split sampling as described in Jain & Neal (2004) <DOI:10.1198/1061860043001>, and sequentially-allocated merge-split sampling <DOI:10.1080/00949655.2021.1998502>, as well as summary and utility functions.

This package provides methods for regression with high-dimensional predictors and univariate or maltivariate response variables. It considers the decomposition of the coefficient matrix that leads to the best approximation to the signal part in the response given any rank, and estimates the decomposition by solving a penalized generalized eigenvalue problem followed by a least squares procedure. Ruiyan Luo and Xin Qi (2017) <doi:10.1016/j.jmva.2016.09.005>.

Simulate survival times from standard parametric survival distributions (exponential, Weibull, Gompertz), 2-component mixture distributions, or a user-defined hazard, log hazard, cumulative hazard, or log cumulative hazard function. Baseline covariates can be included under a proportional hazards assumption. Time dependent effects (i.e. non-proportional hazards) can be included by interacting covariates with linear time or a user-defined function of time. Clustered event times are also accommodated. The 2-component mixture distributions can allow for a variety of flexible baseline hazard functions reflecting those seen in practice. If the user wishes to provide a user-defined hazard or log hazard function then this is possible, and the resulting cumulative hazard function does not need to have a closed-form solution. For details see the supporting paper <doi:10.18637/jss.v097.i03>. Note that this package is modelled on the survsim package available in the Stata software (see Crowther and Lambert (2012) <https://www.stata-journal.com/sjpdf.html?articlenum=st0275> or Crowther and Lambert (2013) <doi:10.1002/sim.5823>).

This package implements the algorithm described in Barron, M., and Li, J. (Not yet published). This algorithm clusters samples from multiple ordered populations, links the clusters across the conditions and identifies marker genes for these changes. The package was designed for scRNA-Seq data but is also applicable to many other data types, just replace cells with samples and genes with variables. The package also contains functions for estimating the parameters for SparseMDC as outlined in the paper. We recommend that users further select their marker genes using the magnitude of the cluster centers.