This package provides interpretable high-dimensional mean comparison methods (HMC). For example, users can apply these methods to assess the difference in gene expression between two treatment groups. It is not a gene-by-gene comparison. Instead, the methods focus on the interplay between features and identify those that are predictive of the group label. The tests are valid frequentist procedures and yield sparse estimates indicating which features contribute to the group differences.

An interface to the algorithms of Interpretable AI <https://www.interpretable.ai> from the R programming language. Interpretable AI provides various modules, including Optimal Trees for classification, regression, prescription and survival analysis, Optimal Imputation for missing data imputation and outlier detection, and Optimal Feature Selection for exact sparse regression. The iai package is an open-source project. The Interpretable AI software modules are proprietary products, but free academic and evaluation licenses are available.

An adaptation of Kernelized Stein Discrepancy, this package provides a goodness-of-fit test of whether a given i.i.d. sample is drawn from a given distribution. It works for any distribution once its score function (the derivative of log-density) can be provided. This method is based on "A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation" by Liu, Lee, and Jordan, available at <arXiv:1602.03253>.

Pattern Sequence Based Forecasting (PSF) takes univariate time series data as input and assist to forecast its future values. This algorithm forecasts the behavior of time series based on similarity of pattern sequences. Initially, clustering is done with the labeling of samples from database. The labels associated with samples are then used for forecasting the future behaviour of time series data. The further technical details and references regarding PSF are discussed in Vignette.

Derived from the work of Kruschke (2015, <ISBN:9780124058880>), the present package aims to provide a framework for conducting Bayesian analysis using Markov chain Monte Carlo (MCMC) sampling utilizing the Just Another Gibbs Sampler ('JAGS', Plummer, 2003, <https://mcmc-jags.sourceforge.io>). The initial version includes several modules for conducting Bayesian equivalents of chi-squared tests, analysis of variance (ANOVA), multiple (hierarchical) regression, softmax regression, and for fitting data (e.g., structural equation modeling).

This package provides a new methodology for linear regression with both curve response and curve regressors, which is described in Cho, Goude, Brossat and Yao (2013) <doi:10.1080/01621459.2012.722900> and (2015) <doi:10.1007/978-3-319-18732-7_3>. The key idea behind this methodology is dimension reduction based on a singular value decomposition in a Hilbert space, which reduces the curve regression problem to several scalar linear regression problems.

This package implements various decision support tools related to the Econometrics & Technometrics. Subroutines include correlation reliability test, Mahalanobis distance measure for outlier detection, combinatorial search (all possible subset regression), non-parametric efficiency analysis measures: DDF (directional distance function), DEA (data envelopment analysis), HDF (hyperbolic distance function), SBM (slack-based measure), and SF (shortage function), benchmarking, Malmquist productivity analysis, risk analysis, technology adoption model, new product target setting, network DEA, dynamic DEA, intertemporal budgeting, etc.

Efficiently estimate shape parameters of periodic time series imagery with which a statistical seasonal trend analysis (STA) is subsequently performed. STA output can be exported in conventional raster formats. Methods to visualize STA output are also implemented as well as the calculation of additional basic statistics. STA is based on (R. Eastman, F. Sangermano, B. Ghimire, H. Zhu, H. Chen, N. Neeti, Y. Cai, E. Machado and S. Crema, 2009) <doi:10.1080/01431160902755338>.

Implementation of target-controlled infusion algorithms for compartmental pharmacokinetic and pharmacokinetic-pharmacodynamic models. Jacobs (1990) <doi:10.1109/10.43622>; Marsh et al. (1991) <doi:10.1093/bja/67.1.41>; Shafer and Gregg (1993) <doi:10.1007/BF01070999>; Schnider et al. (1998) <doi:10.1097/00000542-199805000-00006>; Abuhelwa, Foster, and Upton (2015) <doi:10.1016/j.vascn.2015.03.004>; Eleveld et al. (2018) <doi:10.1016/j.bja.2018.01.018>.

This package provides a new measure of similarity between a pair of mass spectrometry (MS) experiments, called truncated rank correlation (TRC). To provide a robust metric of similarity in noisy high-dimensional data, TRC uses truncated top ranks (or top m-ranks) for calculating correlation. Truncated rank correlation as a robust measure of test-retest reliability in mass spectrometry data. For more details see Lim et al. (2019) <doi:10.1515/sagmb-2018-0056>.

This package provides methods to calculate the expected value of information from a decision-analytic model. This includes the expected value of perfect information (EVPI), partial perfect information (EVPPI) and sample information (EVSI), and the expected net benefit of sampling (ENBS). A range of alternative computational methods are provided under the same user interface. See Heath et al. (2024) <doi:10.1201/9781003156109>, Jackson et al. (2022) <doi:10.1146/annurev-statistics-040120-010730>.

Variance function estimation for models proposed by W. Sadler in his variance function program ('VFP', www.aacb.asn.au/AACB/Resources/Variance-Function-Program). Here, the idea is to fit multiple variance functions to a data set and consequently assess which function reflects the relationship Var ~ Mean best. For in-vitro diagnostic ('IVD') assays modeling this relationship is of great importance when individual test-results are used for defining follow-up treatment of patients.

This package provides drop-in replacements for the base system2() function with fine control and consistent behavior across platforms. It supports clean interruption, timeout, background tasks, and streaming STDIN / STDOUT / STDERR over binary or text connections. The package also provides functions for evaluating expressions inside a temporary fork. Such evaluations have no side effects on the main R process, and support reliable interrupts and timeouts. This provides the basis for a sandboxing mechanism.

This package provides tools for evaluating timely epidemic detection models within school absenteeism-based surveillance systems. Introduces the concept of alert time quality as an evaluation metric. Includes functions to simulate populations, epidemics, and alert metrics associated with epidemic spread using population census data. The methods are based on research published in Vanderkruk et al. (2023) <doi:10.1186/s12889-023-15747-z> and Ward et al. (2019) <doi:10.1186/s12889-019-7521-7>.

Using the Theory of Belief Functions for evidence calculus. Basic probability assignments, or mass functions, can be defined on the subsets of a set of possible values and combined. A mass function can be extended to a larger frame. Marginalization, i.e. reduction to a smaller frame can also be done. These features can be combined to analyze small belief networks and take into account situations where information cannot be satisfactorily described by probability distributions.

This package provides a system for importing electrophysiological signal, based on the Waveform Database (WFDB) software package, written by Moody et al 2022 <doi:10.13026/gjvw-1m31>. A wrapper for utilizing WFDB functions for reading and writing signal data, as well as functions for visualization and analysis are provided. A stable and broadly compatible class for working with signal data, supporting the reading in of cardiac electrophysiogical files such as intracardiac electrograms, is introduced.

Conduct a noncompartmental analysis as closely as possible to the most widely used commercial software. Some features are 1) CDISC SDTM terms 2) Automatic slope selection with the same criterion of WinNonlin(R) 3) Supporting both linear-up linear-down and linear-up log-down method 4) Interval(partial) AUCs with linear or log interpolation method * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107).

Eco-phylogenetic and community phylogenetic analyses. Keeps community ecological and phylogenetic data matched up and comparable using comparative.comm objects. Wrappers for common community phylogenetic indices ('pez.shape', pez.evenness', pez.dispersion', and pez.dissimilarity metrics). Implementation of Cavender-Bares (2004) correlation of phylogenetic and ecological matrices ('fingerprint.regression'). Phylogenetic Generalised Linear Mixed Models (PGLMMs; pglmm') following Ives & Helmus (2011) and Rafferty & Ives (2013). Simulation of null assemblages, traits, and phylogenies ('scape', sim.meta.comm').

Generates Skew Factor Models data and applies Sparse Online Principal Component (SOPC), Incremental Principal Component (IPC), Projected Principal Component (PPC), Perturbation Principal Component (PPC), Stochastic Approximation Principal Component (SAPC), Sparse Principal Component (SPC) and other PC methods to estimate model parameters. It includes capabilities for calculating mean squared error, relative error, and sparsity of the loading matrix.The philosophy of the package is described in Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.

Implement the transformed additive Gaussian (TAG) process and the transformed approximately additive Gaussian (TAAG) process proposed in Lin and Joseph (2020) <DOI:10.1080/00401706.2019.1665592>. These functions can be used to model deterministic computer experiments, obtain predictions at new inputs, and quantify the uncertainty of the predictions. This research is supported by a U.S. National Science Foundation grant DMS-1712642 and a U.S. Army Research Office grant W911NF-17-1-0007.

The Reactive Extensions for C++ (RxCpp) is a library of algorithms for values-distributed-in-time. ReactiveX is a library for composing asynchronous and event-based programs by using observable sequences.

It extends the observer pattern to support sequences of data and/or events and adds operators that allow you to compose sequences together declaratively while abstracting away concerns about things like low-level threading, synchronization, thread-safety, concurrent data structures, and non-blocking I/O.

This algorithm conducts variable selection in the classification setting. It repeatedly subsamples variables and runs linear discriminant analysis (LDA) on the subsampled variables. Variables are scored based on the AUC and the t-statistics. Variables then enter a competition and the semi-finalist variables will be evaluated in a final round of LDA classification. The algorithm then outputs a list of variable selected. Qiao, Sun and Fan (2017) <http://people.math.binghamton.edu/qiao/swa.html>.

This package provides tools to calculate trait probability density functions (TPD) at any scale (e.g. populations, species, communities). TPD functions are used to compute several indices of functional diversity, as well as its partition across scales. These indices constitute a unified framework that incorporates the underlying probabilistic nature of trait distributions into uni- or multidimensional functional trait-based studies. See Carmona et al. (2016) <doi:10.1016/j.tree.2016.02.003> for further information.

Alternating least squares is often used to resolve components contributing to data with a bilinear structure; the basic technique may be extended to alternating constrained least squares. This package provides an implementation of multivariate curve resolution alternating least squares (MCR-ALS).

Commonly applied constraints include unimodality, non-negativity, and normalization of components. Several data matrices may be decomposed simultaneously by assuming that one of the two matrices in the bilinear decomposition is shared between datasets.