To perform main effect matrix factor model (MEFM) estimation for a given matrix time series as described in Lam and Cen (2024) <doi:10.48550/arXiv.2406.00128>. Estimation of traditional matrix factor models is also supported. Supplementary functions for testing MEFM over factor models are included.

Various utilities for the Multiplicative Multinomial distribution.

The Society of Actuaries (SOA) provides an extensive online database called Mortality and Other Rate Tables ('MORT') at <https://mort.soa.org/>. This database contains mortality, lapse, and valuation tables that cover a variety of product types and nations. Users of the database can download any tables in Excel', CSV', or XML formats. This package provides convenience functions that read XML formats from the database and return R objects.

Offers automation tools to parallelize Mplus operations when using R for data generation. It facilitates streamlined integration between Mplus and R', allowing users to run and manage multiple Mplus models simultaneously and efficiently in R'.

Unbiased estimators of overall and per-class thematic map accuracy and area published in Olofsson et al. (2014) <doi:10.1016/j.rse.2014.02.015> and Stehman (2014) <doi:10.1080/01431161.2014.930207>.

An implementation of the alternating expectation conditional maximization (AECM) algorithm for matrix-variate variance gamma (MVVG) and normal-inverse Gaussian (MVNIG) linear models. These models are designed for settings of multivariate analysis with clustered non-uniform observations and correlated responses. The package includes fitting and prediction functions for both models, and an example dataset from a periodontal on Gullah-speaking African Americans, with responses in gaad_res, and covariates in gaad_cov. For more details on the matrix-variate distributions used, see Gallaugher & McNicholas (2019) <doi:10.1016/j.spl.2018.08.012>.

Climate-sensitive, single-tree forest simulator based on data-driven machine learning. It simulates the main forest processesâ radial growth, height growth, mortality, crown recession, regeneration, and harvestingâ so users can assess stand development under climate and management scenarios. The height model is described by Skudnik and JevÅ¡enak (2022) <doi:10.1016/j.foreco.2022.120017>, the basal-area increment model by JevÅ¡enak and Skudnik (2021) <doi:10.1016/j.foreco.2020.118601>, and an overview of the MLFS package, workflow, and applications is provided by JevÅ¡enak, ArniÄ , Krajnc, and Skudnik (2023), Ecological Informatics <doi:10.1016/j.ecoinf.2023.102115>.

It performs the followings Multivariate Process Capability Indices: Shahriari et al. (1995) Multivariate Capability Vector, Taam et al. (1993) Multivariate Capability Index (MCpm), Pan and Lee (2010) proposal (NMCpm) and the followings based on Principal Component Analysis (PCA):Wang and Chen (1998), Xekalaki and Perakis (2002) and Wang (2005). Two datasets are included.

Perform calculations for the WHO International Reference Reagents for the microbiome. Using strain, species or genera abundance tables generated through analysis of 16S ribosomal RNA sequencing or shotgun sequencing which included a reference reagent. This package will calculate measures of sensitivity, False positive relative abundance, diversity, and similarity based on mean average abundances with respect to the reference reagent.

This is the very popular mine sweeper game! The game requires you to find out tiles that contain mines through clues from unmasking neighboring tiles. Each tile that does not contain a mine shows the number of mines in its adjacent tiles. If you unmask all tiles that do not contain mines, you win the game; if you unmask any tile that contains a mine, you lose the game. For further game instructions, please run `help(run_game)` and check details. This game runs in X11-compatible devices with `grDevices::x11()`.

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

Helping psychologists and other behavioural scientists to analyze mouse movement (and other 2-D trajectory) data. Bundles together several functions that compute spatial measures (e.g., maximum absolute deviation, area under the curve, sample entropy) or provide a shorthand for procedures that are frequently used (e.g., time normalization, linear interpolation, extracting initiation and movement times). For more information on these dependent measures, see Wirth et al. (2020) <doi:10.3758/s13428-020-01409-0>.

Calculates k-best solutions and costs for an assignment problem following the method outlined in Murty (1968) <doi:10.1287/opre.16.3.682>.

Carries out model-based clustering, classification and discriminant analysis using five different models. The models are all based on the generalized hyperbolic distribution. The first model MGHD (Browne and McNicholas (2015) <doi:10.1002/cjs.11246>) is the classical mixture of generalized hyperbolic distributions. The MGHFA (Tortora et al. (2016) <doi:10.1007/s11634-015-0204-z>) is the mixture of generalized hyperbolic factor analyzers for high dimensional data sets. The MSGHD is the mixture of multiple scaled generalized hyperbolic distributions, the cMSGHD is a MSGHD with convex contour plots and the MCGHD', mixture of coalesced generalized hyperbolic distributions is a new more flexible model (Tortora et al. (2019)<doi:10.1007/s00357-019-09319-3>. The paper related to the software can be found at <doi:10.18637/jss.v098.i03>.

This package provides a set of functions for some multivariate analyses utilizing a structural equation modeling (SEM) approach through the OpenMx package. These analyses include canonical correlation analysis (CANCORR), redundancy analysis (RDA), and multivariate principal component regression (MPCR). It implements procedures discussed in Gu and Cheung (2023) <doi:10.1111/bmsp.12301>, Gu, Yung, and Cheung (2019) <doi:10.1080/00273171.2018.1512847>, and Gu et al. (2023) <doi:10.1080/00273171.2022.2141675>.

This package contains several functions for statistical data analysis; e.g. for sample size and power calculations, computation of confidence intervals and tests, and generation of similarity matrices.

Deploy file changes across multiple GitHub repositories using the GitHub Web API <https://docs.github.com/en/rest>. Allows synchronizing common files, Continuous Integration ('CI') workflows, or configurations across many repositories with a single command.

This package provides methods for high-dimensional multi-view learning based on the multi-view stacking (MVS) framework. For technical details on the MVS and stacked penalized logistic regression (StaPLR) methods see Van Loon, Fokkema, Szabo, & De Rooij (2020) <doi:10.1016/j.inffus.2020.03.007> and Van Loon et al. (2022) <doi:10.3389/fnins.2022.830630>.

Developed for the following tasks. 1- simulating realizations from the canonical, restricted, and unrestricted finite mixture models. 2- Monte Carlo approximation for density function of the finite mixture models. 3- Monte Carlo approximation for the observed Fisher information matrix, asymptotic standard error, and the corresponding confidence intervals for parameters of the mixture models sing the method proposed by Basford et al. (1997) <https://espace.library.uq.edu.au/view/UQ:57525>.

This package provides functions for fitting monotone polynomials to data. Detailed discussion of the methodologies used can be found in Murray, Mueller and Turlach (2013) <doi:10.1007/s00180-012-0390-5> and Murray, Mueller and Turlach (2016) <doi:10.1080/00949655.2016.1139582>.

Providing the kubernetes-like class ManagedCloudProvider as a child class of the CloudProvider class in the DockerParallel package. The class is able to manage the cloud instance made by the non-kubernetes cloud service. For creating a provider for the non-kubernetes cloud service, the developer needs to define a reference class inherited from ManagedCloudProvider and define the method for the generics runDockerWorkerContainers(), getDockerWorkerStatus() and killDockerWorkerContainers(). For more information, please see the vignette in this package and <https://CRAN.R-project.org/package=DockerParallel>.

Takes QC signal for each day and normalize metabolomic data that has been acquired in a certain period of time. At least three QC per day are required.

Runs a Shiny web application that merges raw qPCR fluorescence data with related metadata to visualize species presence/absence detection patterns and assess data quality. The application calculates threshold values from raw fluorescence data using a method based on the second derivative method, Luu-The et al (2005) <doi:10.2144/05382RR05>, and utilizes the âchipPCRâ package by Rödiger, Burdukiewicz, & Schierack (2015) <doi:10.1093/bioinformatics/btv205> to calculate Cq values. The application has the ability to connect to a custom developed MySQL database to populate the applications interface. The application allows users to interact with visualizations such as a dynamic map, amplification curves and standard curves, that allow for zooming and/or filtering. It also enables the generation of customized exportable reports based on filtered mapping data.

Partial Replacement Imputation Estimation (PRIME) can overcome problems caused by missing covariates in additive partially linear model. PRIME conducts imputation and regression simultaneously with known and unknown model structure. More details can be referred to Zishu Zhan, Xiangjie Li and Jingxiao Zhang. (2022) <arXiv:2205.14994>.