This package contains model-based treatment of missing data for regression models with missing values in covariates or the dependent variable using maximum likelihood or Bayesian estimation (Ibrahim et al., 2005; <doi:10.1198/016214504000001844>; Luedtke, Robitzsch, & West, 2020a, 2020b; <doi:10.1080/00273171.2019.1640104><doi:10.1037/met0000233>). The regression model can be nonlinear (e.g., interaction effects, quadratic effects or B-spline functions). Multilevel models with missing data in predictors are available for Bayesian estimation. Substantive-model compatible multiple imputation can be also conducted.

This package provides two variants of multiple correspondence analysis (ca): multiple ca and ordered multiple ca via orthogonal polynomials of Emerson.

Inference of a multi-states birth-death model from a phylogeny, comprising a number of states N, birth and death rates for each state and on which edges each state appears. Inference is done using a hybrid approach: states are progressively added in a greedy approach. For a fixed number of states N the best model is selected via maximum likelihood. Reference: J. Barido-Sottani, T. G. Vaughan and T. Stadler (2018) <doi:10.1098/rsif.2018.0512>.

This package provides functions to calculate hazard and survival function of Multi-Stage Clonal Expansion Models used in cancer epidemiology. For the Two-Stage Clonal Expansion Model an exact solution is implemented assuming piecewise constant parameters, see Heidenreich, Luebeck, Moolgavkar (1997) <doi:10.1111/j.1539-6924.1997.tb00878.x>. Numerical solutions are provided for its extensions, see also Little, Vineis, Li (2008) <doi:10.1016/j.jtbi.2008.05.027>.

This package implements the algorithm of Remez (1962) for polynomial minimax approximation and of Cody et al. (1968) <doi:10.1007/BF02162506> for rational minimax approximation.

Analysis of annual average ocean water level time series from long (minimum length 80 years) individual records, providing improved estimates of trend (mean sea level) and associated real-time velocities and accelerations. Improved trend estimates are based on Singular Spectrum Analysis methods. Various gap-filling options are included to accommodate incomplete time series records. The package also contains a forecasting module to consider the implication of user defined quantum of sea level rise between the end of the available historical record and the year 2100. A wide range of screen and pdf plotting options are available in the package.

This package provides functions to calculate Unique Trait Combinations (UTC) and scaled Unique Trait Combinations (sUTC) as measures of multivariate richness. The package can also calculate beta-diversity for trait richness and can partition this into nestedness-related and turnover components. The code will also calculate several measures of overlap. See Keyel and Wiegand (2016) <doi:10.1111/2041-210X.12558> for more details.

Implementing various things including functions for LaTeX tables, the Kalman filter, QQ-plots with simulation-based confidence intervals, linear regression diagnostics, web scraping, development tools, relative risk and odds rati, GARCH(1,1) Forecasting.

This package provides functions for the robust estimation of parametric families of copulas using minimization of the Maximum Mean Discrepancy, following the article Alquier, Chérief-Abdellatif, Derumigny and Fermanian (2022) <doi:10.1080/01621459.2021.2024836>.

Data sets from a variety of biological sample matrices, analysed using a number of mass spectrometry based metabolomic analytical techniques. The example data sets are stored remotely using GitHub releases <https://github.com/aberHRML/metaboData/releases> which can be accessed from R using the package. The package also includes the abr1 FIE-MS data set from the FIEmspro package <https://users.aber.ac.uk/jhd/> <doi:10.1038/nprot.2007.511>.

Facilitates tidy calculation of popular quantitative marketing metrics. It also includes functions for doing analysis that will help marketers and data analysts better understand the drivers and/or trends of these metrics. These metrics include Customer Experience Index <https://go.forrester.com/analytics/cx-index/> and Net Promoter Score <https://www.netpromoter.com/know/>.

In the context of multistate models, which are popular in sociology, demography, and epidemiology, Markov chain with rewards calculations can help to refine transition timings and so obtain more accurate estimates. The package code accommodates up to nine transient states and irregular age (time) intervals. Traditional demographic life tables result as a special case. Formulas and methods involved are explained in detail in the accompanying article: Schneider / Myrskyla / van Raalte (2021): Flexible Transition Timing in Discrete-Time Multistate Life Tables Using Markov Chains with Rewards, MPIDR Working Paper WP-2021-002.

Allows the user to generate a list of features (gene, pseudo, RNA, CDS, and/or UTR) directly from NCBI database for any species with a current build available. Option to save downloaded and formatted files is available, and the user can prioritize the feature list based on type and assembly builds present in the current build used. The user can then use the list of features generated or provide a list to map a set of markers (designed for SNP markers with a single base pair position available) to the closest feature based on the map build. This function does require map positions of the markers to be provided and the positions should be based on the build being queried through NCBI.

Various functions for random number generation, density estimation, classification, curve fitting, and spatial data analysis.

Simulating data and fitting multi-species N-mixture models using nimble'. Includes features for handling zero-inflation and temporal correlation, Bayesian inference, model diagnostics, parameter estimation, and predictive checks. Designed for ecological studies with zero-altered or time-series data. Mimnagh, N., Parnell, A., Prado, E., & Moral, R. A. (2022) <doi:10.1007/s10651-022-00542-7>. Royle, J. A. (2004) <doi:10.1111/j.0006-341X.2004.00142.x>.

Conjoint measurement is a psychophysical procedure in which stimulus pairs are presented that vary along 2 or more dimensions and the observer is required to compare the stimuli along one of them. This package contains functions to estimate the contribution of the n scales to the judgment by a maximum likelihood method under several hypotheses of how the perceptual dimensions interact. Reference: Knoblauch & Maloney (2012) "Modeling Psychophysical Data in R". <doi:10.1007/978-1-4614-4475-6>.

Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees and time series. mvMORPH also proposes high-dimensional multivariate comparative tools (linear models using Generalized Least Squares and multivariate tests) based on penalized likelihood. See Clavel et al. (2015) <DOI:10.1111/2041-210X.12420>, Clavel et al. (2019) <DOI:10.1093/sysbio/syy045>, and Clavel & Morlon (2020) <DOI:10.1093/sysbio/syaa010>.

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>.

Fits the Bayesian multinomial probit model via Markov chain Monte Carlo. The multinomial probit model is often used to analyze the discrete choices made by individuals recorded in survey data. Examples where the multinomial probit model may be useful include the analysis of product choice by consumers in market research and the analysis of candidate or party choice by voters in electoral studies. The MNP package can also fit the model with different choice sets for each individual, and complete or partial individual choice orderings of the available alternatives from the choice set. The estimation is based on the efficient marginal data augmentation algorithm that is developed by Imai and van Dyk (2005). "A Bayesian Analysis of the Multinomial Probit Model Using the Data Augmentation." Journal of Econometrics, Vol. 124, No. 2 (February), pp. 311-334. <doi:10.1016/j.jeconom.2004.02.002> Detailed examples are given in Imai and van Dyk (2005). "MNP: R Package for Fitting the Multinomial Probit Model." Journal of Statistical Software, Vol. 14, No. 3 (May), pp. 1-32. <doi:10.18637/jss.v014.i03>.

With the provision of several tools and templates the MOSAIC project (DFG-Grant Number HO 1937/2-1) supports the implementation of a central data management in epidemiological research projects. The MOQA package enables epidemiologists with none or low experience in R to generate basic data quality reports for a wide range of application scenarios. See <https://mosaic-greifswald.de/> for more information. Please read and cite the corresponding open access publication (using the former package-name) in METHODS OF INFORMATION IN MEDICINE by M. Bialke, H. Rau, T. Schwaneberg, R. Walk, T. Bahls and W. Hoffmann (2017) <doi:10.3414/ME16-01-0123>. <https://methods.schattauer.de/en/contents/most-recent-articles/issue/2483/issue/special/manuscript/27573/show.html>.

Based on the work of Curi, Converse, Hajewski, and Oliveira (2019) <doi:10.1109/IJCNN.2019.8852333>. This package provides easy-to-use functions which create a variational autoencoder (VAE) to be used for parameter estimation in Item Response Theory (IRT) - namely the Multidimensional Logistic 2-Parameter (ML2P) model. To use a neural network as such, nontrivial modifications to the architecture must be made, such as restricting the nonzero weights in the decoder according to some binary matrix Q. The functions in this package allow for straight-forward construction, training, and evaluation so that minimal knowledge of tensorflow or keras is required.

Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software Stan'. References: Clark & Wells (2023) <doi:10.1111/2041-210X.13974>.

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

Generalization of Shapiro-Wilk test for multivariate variables.