This package implements proper and so-called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>. A number of different imputation methods are available, by utilising the norm', cat and mix packages. Inferences can be performed either using Rubin's rules (for proper imputation), or a modified version for maximum likelihood imputation. For maximum likelihood imputations a likelihood score based approach based on theory by Wang and Robins (1998) <doi:10.1093/biomet/85.4.935> is also available.

We introduce factor models designed to jointly analyze high-dimensional count data from multiple studies by extracting study-shared and specified factors. Our factor models account for heterogeneous noises and overdispersion among counts with augmented covariates. We propose an efficient and speedy variational estimation procedure for estimating model parameters, along with a novel criterion for selecting the optimal number of factors and the rank of regression coefficient matrix. More details can be referred to Liu et al. (2024) <doi:10.48550/arXiv.2402.15071>.

Analyzes subject-level data in clinical trials using the metalite data structure. The package simplifies the workflow to create production-ready tables, listings, and figures discussed in the subject-level analysis chapters of "R for Clinical Study Reports and Submission" by Zhang et al. (2022) <https://r4csr.org/>.

Implementation of Multiple Comparison Procedures with Modeling (MCP-Mod) procedure with bias-corrected estimators and second-order covariance matrices as described in Diniz, Gallardo and Magalhaes (2023) <doi:10.1002/pst.2303>.

This package provides methods for color labeling, calculation of eigengenes, merging of closely related modules.

Display processing results using the GWR (Geographically Weighted Regression) method, display maps, and show the results of the Mixed GWR (Mixed Geographically Weighted Regression) model which automatically selects global variables based on variability between regions. This function refers to Yasin, & Purhadi. (2012). "Mixed Geographically Weighted Regression Model (Case Study the Percentage of Poor Households in Mojokerto 2008)". European Journal of Scientific Research, 188-196. <https://www.researchgate.net/profile/Hasbi-Yasin-2/publication/289689583_Mixed_geographically_weighted_regression_model_case_study_The_percentage_of_poor_households_in_Mojokerto_2008/links/58e46aa40f7e9bbe9c94d641/Mixed-geographically-weighted-regression-model-case-study-The-percentage-of-poor-households-in-Mojokerto-2008.pdf>.

This package provides functions and wrappers for using the Multiple Aggregation Prediction Algorithm (MAPA) for time series forecasting. MAPA models and forecasts time series at multiple temporal aggregation levels, thus strengthening and attenuating the various time series components for better holistic estimation of its structure. For details see Kourentzes et al. (2014) <doi:10.1016/j.ijforecast.2013.09.006>.

Maximum a posteriori (MAP) estimation for topic models (i.e., Latent Dirichlet Allocation) in text analysis, as described in Taddy (2012) On estimation and selection for topic models'. Previous versions of this code were included as part of the textir package. If you want to take advantage of openmp parallelization, uncomment the relevant flags in src/MAKEVARS before compiling.

This package provides a set of functions which use the Expectation Maximisation (EM) algorithm (Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x> Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, 39(1), 1--22) to take a finite mixture model approach to clustering. The package is designed to cluster multivariate data that have categorical and continuous variables and that possibly contain missing values. The method is described in Hunt, L. and Jorgensen, M. (1999) <doi:10.1111/1467-842X.00071> Australian & New Zealand Journal of Statistics 41(2), 153--171 and Hunt, L. and Jorgensen, M. (2003) <doi:10.1016/S0167-9473(02)00190-1> Mixture model clustering for mixed data with missing information, Computational Statistics & Data Analysis, 41(3-4), 429--440.

This package provides an extension to MadanText for creating and analyzing co-occurrence networks in Persian text data. This package mainly makes use of the PersianStemmer (Safshekan, R., et al. (2019). <https://CRAN.R-project.org/package=PersianStemmer>), udpipe (Wijffels, J., et al. (2023). <https://CRAN.R-project.org/package=udpipe>), and shiny (Chang, W., et al. (2023). <https://CRAN.R-project.org/package=shiny>) packages.

This package provides methods for the analysis of how ecological drivers affect the multifunctionality of an ecosystem based on methods of Byrnes et al. 2016 <doi:10.1111/2041-210X.12143> and Byrnes et al. 2022 <doi:10.1101/2022.03.17.484802>. Most standard methods in the literature are implemented (see vignettes) in a tidy format.

This package performs treatment allocation in two-arm clinical trials by the maximal procedure described by Berger et al. (2003) <doi:10.1002/sim.1538>. To that end, the algorithm provided by Salama et al. (2008) <doi:10.1002/sim.3014> is implemented.

This package implements nonparametric bootstrap tests for detecting monotonicity in regression functions from Hall, P. and Heckman, N. (2000) <doi:10.1214/aos/1016120363> Includes tools for visualizing results using Nadaraya-Watson kernel regression and supports efficient computation with C++'. Tutorials and shiny application demo are available at <https://www.laylaparast.com/monotonicitytest> and <https://parastlab.shinyapps.io/MonotonicityTest>.

Sometimes data for analysis are obtained using more convenient or less expensive means yielding "surrogate" variables for what could be obtained more accurately, albeit with less convenience; or less conveniently or at more expense yielding "reference" variables, thought of as being measured without error. Analysis of the surrogate variables measured with error generally yields biased estimates when the objective is to make inference about the reference variables. Often it is thought that ignoring the measurement error in surrogate variables only biases effects toward the null hypothesis, but this need not be the case. Measurement errors may bias parameter estimates either toward or away from the null hypothesis. If one has a data set with surrogate variable data from the full sample, and also reference variable data from a randomly selected subsample, then one can assess the bias introduced by measurement error in parameter estimation, and use this information to derive improved estimates based upon all available data. Formulaically these estimates based upon the reference variables from the validation subsample combined with the surrogate variables from the whole sample can be interpreted as starting with the estimate from reference variables in the validation subsample, and "augmenting" this with additional information from the surrogate variables. This suggests the term "augmented" estimate. The meerva package calculates these augmented estimates in the regression setting when there is a randomly selected subsample with both surrogate and reference variables. Measurement errors may be differential or non-differential, in any or all predictors (simultaneously) as well as outcome. The augmented estimates derive, in part, from the multivariate correlation between regression model parameter estimates from the reference variables and the surrogate variables, both from the validation subset. Because the validation subsample is chosen at random any biases imposed by measurement error, whether non-differential or differential, are reflected in this correlation and these correlations can be used to derive estimates for the reference variables using data from the whole sample. The main functions in the package are meerva.fit which calculates estimates for a dataset, and meerva.sim.block which simulates multiple datasets as described by the user, and analyzes these datasets, storing the regression coefficient estimates for inspection. The augmented estimates, as well as how measurement error may arise in practice, is described in more detail by Kremers WK (2021) <arXiv:2106.14063> and is an extension of the works by Chen Y-H, Chen H. (2000) <doi:10.1111/1467-9868.00243>, Chen Y-H. (2002) <doi:10.1111/1467-9868.00324>, Wang X, Wang Q (2015) <doi:10.1016/j.jmva.2015.05.017> and Tong J, Huang J, Chubak J, et al. (2020) <doi:10.1093/jamia/ocz180>.

Enables preparation of maps to be printed and drawn on. Modified maps can then be scanned back in, and hand-drawn marks converted to spatial objects.

This package provides a procedure for comparing multivariate samples associated with different groups. It uses principal component analysis to convert multivariate observations into a set of linearly uncorrelated statistical measures, which are then compared using a number of statistical methods. The procedure is independent of the distributional properties of samples and automatically selects features that best explain their differences, avoiding manual selection of specific points or summary statistics. It is appropriate for comparing samples of time series, images, spectrometric measures or similar multivariate observations. This package is described in Fachada et al. (2016) <doi:10.32614/RJ-2016-055>.

This package contains functions for data analysis of Repeated measurement using GEE. Data may contain missing value in response and covariates. For parameter estimation through Fisher Scoring algorithm, Mean Score and Inverse Probability Weighted method combining with Multiple Imputation are used when there is missing value in covariates/response. Reference for mean score method, inverse probability weighted method is Wang et al(2007)<doi:10.1093/biostatistics/kxl024>.

This package provides functions to impute missing values using Gaussian copulas for mixed data types as described by Christoffersen et al. (2021) <arXiv:2102.02642>. The method is related to Hoff (2007) <doi:10.1214/07-AOAS107> and Zhao and Udell (2019) <arXiv:1910.12845> but differs by making a direct approximation of the log marginal likelihood using an extended version of the Fortran code created by Genz and Bretz (2002) <doi:10.1198/106186002394> in addition to also support multinomial variables.

Multivariate ARIMA and ARIMA-X estimation using Spliid's algorithm (marima()) and simulation (marima.sim()).

In breeding experiments, mating environmental (ME) designs are very popular as mating designs are directly implemented in the field environment using block or row-column designs. Here, three functions are given related to three new methods which will generate mating diallel cross designs (Hinkelmann and Kempthorne, 1963<doi:10.2307/2333899>) or mating environmental (ME) designs along with design parameters, C matrix, eigenvalues (EVs), degree of fractionations (DF) and canonical efficiency factor (CEF). Another one function is added to check the properties of a given ME diallel cross design.

Econometric analysis of multiple-input-multiple-output production technologies with ray-based input distance functions as suggested by Price and Henningsen (2022): "A Ray-Based Input Distance Function to Model Zero-Valued Output Quantities: Derivation and an Empirical Application", <https://ideas.repec.org/p/foi/wpaper/2022_03.html>.

Three generalizations of the synthetic control method (which has already an implementation in package Synth') are implemented: first, MSCMT allows for using multiple outcome variables, second, time series can be supplied as economic predictors, and third, a well-defined cross-validation approach can be used. Much effort has been taken to make the implementation as stable as possible (including edge cases) without losing computational efficiency. A detailed description of the main algorithms is given in Becker and Klöà ner (2018) <doi:10.1016/j.ecosta.2017.08.002>.

This package implements the three parallel forecast combinations of Markov Switching GARCH and extreme learning machine model along with the selection of appropriate model for volatility forecasting. For method details see Hsiao C, Wan SK (2014). <doi:10.1016/j.jeconom.2013.11.003>, Hansen BE (2007). <doi:10.1111/j.1468-0262.2007.00785.x>, Elliott G, Gargano A, Timmermann A (2013). <doi:10.1016/j.jeconom.2013.04.017>.

Approximate node interaction parameters of Markov Random Fields graphical networks. Models can incorporate additional covariates, allowing users to estimate how interactions between nodes in the graph are predicted to change across covariate gradients. The general methods implemented in this package are described in Clark et al. (2018) <doi:10.1002/ecy.2221>.