Forecasting of univariate time series using feature extraction with variable prediction methods is provided. Feature extraction is done with a redundant Haar wavelet transform with filter h = (0.5, 0.5). The advantage of the approach compared to typical Fourier based methods is an dynamic adaptation to varying seasonalities. Currently implemented prediction methods based on the selected wavelets levels and scales are a regression and a multi-layer perceptron. Forecasts can be computed for horizon 1 or higher. Model selection is performed with an evolutionary optimization. Selection criteria are currently the AIC criterion, the Mean Absolute Error or the Mean Root Error. The data is split into three parts for model selection: Training, test, and evaluation dataset. The training data is for computing the weights of a parameter set. The test data is for choosing the best parameter set. The evaluation data is for assessing the forecast performance of the best parameter set on new data unknown to the model. This work is published in Stier, Q.; Gehlert, T.; Thrun, M.C. Multiresolution Forecasting for Industrial Applications. Processes 2021, 9, 1697. <doi:10.3390/pr9101697>.

Acoustic template detection and monitoring database interface. Create, modify, save, and use templates for detection of animal vocalizations. View, verify, and extract results. Upload a MySQL schema to a existing instance, manage survey metadata, write and read templates and detections locally or to the database.

Addressing a central challenge encountered in Mendelian randomization (MR) studies, where MR primarily focuses on discerning the effects of individual exposures on specific outcomes and establishes causal links between them. Using a network-based methodology, the intricacy involving interdependent outcomes due to numerous factors has been tackled through this routine. Based on Ni et al. (2018) <doi:10.1214/17-BA1087>, MR.RGM extends to a broader exploration of the causal landscape by leveraging on network structures and involves the construction of causal graphs that capture interactions between response variables and consequently between responses and instrument variables. The resulting Graph visually represents these causal connections, showing directed edges with effect sizes labeled. MR.RGM facilitates the navigation of various data availability scenarios effectively by accommodating three input formats, i.e., individual-level data and two types of summary-level data. The method also optionally incorporates measured covariates (when available) and allows flexible modeling of the error variance structure, including correlated errors that may reflect unmeasured confounding among responses. In the process, causal effects, adjacency matrices, and other essential parameters of the complex biological networks, are estimated. Besides, MR.RGM provides uncertainty quantification for specific network structures among response variables. Parts of the Inverse Wishart sampler are adapted from the econ722 repository by DiTraglia (GPL-2.0).

An implementation of a Bayesian sparse group model using spike and slab priors in a regression context. It is designed for regression with a multivariate response variable, but also provides an implementation for univariate response.

This package provides a formula-driven framework for maximizing target functions via the minorization-maximization (MM) algorithm. The package represents the target as a symbolic expression tree, infers its curvature via disciplined-convex-programming rules, and constructs a separable surrogate at each iterate using only Jensen's inequality and the supporting hyperplane. The driver maximizes the surrogate via block-coordinate Newton with line search, falling back to a multivariate step on any non-separable residue. A formula interface accepts standard R expressions (including `sum()` reductions and `X %*% theta` design-matrix products) so statistical models such as Poisson regression can be written in one line.

This package provides a collection of functions for conducting a meta-analysis with mean differences data. It uses recommended procedures as described in The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009).

Detection of multivariate outliers using robust estimates of location and scale. The Minimum Covariance Determinant (MCD) estimator is used to calculate robust estimates of the mean vector and covariance matrix. Outliers are determined based on robust Mahalanobis distances using either an unstructured covariance matrix, a principal components structured covariance matrix, or a factor analysis structured covariance matrix. Includes options for specifying the direction of interest for outlier detection for each variable.

Nonparametric estimation and inference for natural direct and indirect effects by Chan, Imai, Yam and Zhang (2016) <arXiv:1601.03501>.

This package provides a collection of helper functions for analyzing Second Primary Cancer data, including functions to reshape data, to calculate patient states and analyze cancer incidence.

Analysis of multivariate functional spatial data, including spectral multivariate functional principal component analysis and related statistical procedures (Si-Ahmed, Idris, et al. "Principal component analysis of multivariate spatial functional data." Big Data Research 39 (2025) 100504). (Kuenzer, T., Hörmann, S., & Kokoszka, P. (2021). "Principal component analysis of spatially indexed functions." Journal of the American Statistical Association, 116(535), 1444-1456.) (Happ, C., & Greven, S. (2018). "Multivariate functional principal component analysis for data observed on different (dimensional) domains." Journal of the American Statistical Association, 113(522), 649-659.).

Correlation coefficients for multivariate data, namely the squared correlation coefficient and the RV coefficient (multivariate generalization of the squared Pearson correlation coefficient). References include Mardia K.V., Kent J.T. and Bibby J.M. (1979). "Multivariate Analysis". ISBN: 978-0124712522. London: Academic Press.

An implementation of metaheuristic algorithms for continuous optimization. Currently, the package contains the implementations of 21 algorithms, as follows: particle swarm optimization (Kennedy and Eberhart, 1995), ant lion optimizer (Mirjalili, 2015 <doi:10.1016/j.advengsoft.2015.01.010>), grey wolf optimizer (Mirjalili et al., 2014 <doi:10.1016/j.advengsoft.2013.12.007>), dragonfly algorithm (Mirjalili, 2015 <doi:10.1007/s00521-015-1920-1>), firefly algorithm (Yang, 2009 <doi:10.1007/978-3-642-04944-6_14>), genetic algorithm (Holland, 1992, ISBN:978-0262581110), grasshopper optimisation algorithm (Saremi et al., 2017 <doi:10.1016/j.advengsoft.2017.01.004>), harmony search algorithm (Mahdavi et al., 2007 <doi:10.1016/j.amc.2006.11.033>), moth flame optimizer (Mirjalili, 2015 <doi:10.1016/j.knosys.2015.07.006>, sine cosine algorithm (Mirjalili, 2016 <doi:10.1016/j.knosys.2015.12.022>), whale optimization algorithm (Mirjalili and Lewis, 2016 <doi:10.1016/j.advengsoft.2016.01.008>), clonal selection algorithm (Castro, 2002 <doi:10.1109/TEVC.2002.1011539>), differential evolution (Das & Suganthan, 2011), shuffled frog leaping (Eusuff, Landsey & Pasha, 2006), cat swarm optimization (Chu et al., 2006), artificial bee colony algorithm (Karaboga & Akay, 2009), krill-herd algorithm (Gandomi & Alavi, 2012), cuckoo search (Yang & Deb, 2009), bat algorithm (Yang, 2012), gravitational based search (Rashedi et al., 2009) and black hole optimization (Hatamlou, 2013).

Matrix eQTL is designed for fast eQTL analysis on large datasets. Matrix eQTL can test for association between genotype and gene expression using linear regression with either additive or ANOVA genotype effects. The models can include covariates to account for factors as population stratification, gender, and clinical variables. It also supports models with heteroscedastic and/or correlated errors, false discovery rate estimation and separate treatment of local (cis) and distant (trans) eQTLs. For more details see Shabalin (2012) <doi:10.1093/bioinformatics/bts163>.

Calculate morphine milligram equivalents (MME) for opioid dose comparison using standardized methods. Can directly call the NIH HEAL MME Online Calculator <https://research-mme.wakehealth.edu/api> API or replicate API calculations on the user's local machine from the comfort of R'. Creation of the NIH HEAL MME Online Calculator and the MME calculations implemented in this package are described in Adams MCB, Sward KA, Perkins ML, Hurley RW (2025) <doi:10.1097/j.pain.0000000000003529>.

Implementations of various robust and flexible model-based clustering methods for data sets with missing values at random (Tong and Tortora, 2025, <doi:10.18637/jss.v115.i03>). Two main models are: Multivariate Contaminated Normal Mixture (MCNM, Tong and Tortora, 2022, <doi:10.1007/s11634-021-00476-1>) and Multivariate Generalized Hyperbolic Mixture (MGHM, Wei et al., 2019, <doi:10.1016/j.csda.2018.08.016>). Mixtures via some special or limiting cases of the multivariate generalized hyperbolic distribution are also included: Normal-Inverse Gaussian, Symmetric Normal-Inverse Gaussian, Skew-Cauchy, Cauchy, Skew-t, Student's t, Normal, Symmetric Generalized Hyperbolic, Hyperbolic Univariate Marginals, Hyperbolic, and Symmetric Hyperbolic. Funding: This work was partially supported by the National Science foundation NSF Grant NO. 2209974.

Topological data analysis (TDA) is a method of data analysis that uses techniques from topology to analyze high-dimensional data. Here we implement Mapper, an algorithm from this area developed by Singh, Mémoli and Carlsson (2007) which generalizes the concept of a Reeb graph <https://en.wikipedia.org/wiki/Reeb_graph>.

The latest guidelines proposed by International Expert Consensus are used for the clinical diagnosis of Metabolic Associated Fatty Liver Disease (MAFLD). The new definition takes hepatic steatosis (determined by elastography or histology or biomarker-based fatty liver index) as a major criterion. In addition, race, gender, body mass index (BMI), waist circumference (WC), fasting plasma glucose (FPG), systolic blood pressure (SBP), diastolic blood pressure (DBP), triglycerides (TG), high-density lipoprotein cholesterol (HDLC), homeostatic model assessment of insulin resistance (HOMAIR), high sensitive c-reactive protein (HsCRP) for the diagnosis of MAFLD. Each parameter has to be interpreted based on the proposed cut-offs, making the diagnosis slightly complex and error-prone. This package is developed by incorporating the latest international expert consensus guidelines, and it will aid in the easy and quick diagnosis of MAFLD based on FibroScan in busy healthcare settings and also for research purposes. The new definition for MAFLD as per the International Consensus Statement is described by Eslam M et al (2020). <doi:10.1016/j.jhep.2020.03.039>.

Fit multilevel manifest or latent time-series models, including popular Dynamic Structural Equation Models (DSEM). The models can be set up and modified with user-friendly functions and are fit to the data using Stan for Bayesian inference. Path models and formulas for user-defined models can be easily created with functions using knitr'. Asparouhov, Hamaker, & Muthen (2018) <doi:10.1080/10705511.2017.1406803>.

An implementation of MLMC (Multi-Level Monte Carlo), Giles (2008) <doi:10.1287/opre.1070.0496>, Heinrich (1998) <doi:10.1006/jcom.1998.0471>, for R. This package builds on the original Matlab and C++ implementations by Mike Giles to provide a full MLMC driver and example level samplers. Multi-core parallel sampling of levels is provided built-in.

This package implements the MEC-TX (MEchanistic Clustering - Treatment eXposure) framework for encoding, clustering, and survival analysis of real-world oncology treatment timelines. Provides functions for normalising medication records, computing treatment intervals, performing k-means clustering in PCA space, assigning line-of-therapy labels, and comparing survival outcomes across treatment groups. Designed for use with registry-based cohorts such as the ORIEN AVATAR dataset. Methods follow the digital-twin framework described in Dhrubo and Spakowicz (2026) <https://github.com/spakowiczlab/mec-tx>. treatment timelines using the MEC-TX digital-twin framework.

The routine twosample_test() in this package runs the two-sample test using various test statistic for multivariate data. The user can also run several tests and then find a p value adjusted for simultaneous inference. The p values are found via permutation or via the parametric bootstrap. The routine twosample_power() allows the estimation of the power of the tests. The routine run.studies() allows a user to quickly study the power of a new method and how it compares to those included in the package. For details of the methods and references see the included vignettes.

This package provides a flexible computational framework for mixture distributions with the focus on the composite models.

This package provides a set of functions to manage data shared on a MOLGENIS Armadillo server.

Make all elements of a character vector unique. Differs from make.unique by starting at 1 and allowing users to customise suffix format.