This package implements random number generation, plotting, and estimation algorithms for the two-parameter one-sided and two-sided M-Wright (Mainardi-Wright) family. The M-Wright distributions naturally generalize the widely used one-sided (Airy and half-normal or half-Gaussian) and symmetric (Airy and Gaussian or normal) models. These are widely studied in time-fractional differential equations. References: Cahoy and Minkabo (2017) <doi:10.3233/MAS-170388>; Cahoy (2012) <doi:10.1007/s00180-011-0269-x>; Cahoy (2012) <doi:10.1080/03610926.2010.543299>; Cahoy (2011); Mainardi, Mura, and Pagnini (2010) <doi:10.1155/2010/104505>.

Common mass spectrometry tools described in John Roboz (2013) <doi:10.1201/b15436>. It allows checking element isotopes, calculating (isotope labelled) exact monoisitopic mass, m/z values and mass accuracy, and inspecting possible contaminant mass peaks, examining possible adducts in electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI) ion sources.

This package is deprecated. Please use redatamx instead. Provides an API to work with Redatam (see <https://redatam.org>) databases in both formats: RXDB (new format) and DICX (old format) and running Redatam programs written in SPC language. It's a wrapper around Redatam core and provides functions to open/close a database (redatam_open()/redatam_close()), list entities and variables from the database (redatam_entities(), redatam_variables()) and execute a SPC program and gets the results as data frames (redatam_query(), redatam_run()).

Permutation tests for variance components for 2-level, 3-level and 4-level data with univariate or multivariate responses.

This package provides a method for multivariate ordinal data generation given marginal distributions and correlation matrix based on the methodology proposed by Demirtas (2006) <DOI:10.1080/10629360600569246>.

You can use the set of wrappers for analytical schemata to reduce the effort in writing machine-readable data. The set of all-in-one wrappers will cover widely used functions from data analysis packages.

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>.

This grants the functionality of the Maxar Geospatial Platform (MGP) Streaming API. It can search for images using the WFS method. It can Download images using WMS WMTS. It can also Download a full resolution image.

Multivariate analysis, having functions that perform simple correspondence analysis (CA) and multiple correspondence analysis (MCA), principal components analysis (PCA), canonical correlation analysis (CCA), factorial analysis (FA), multidimensional scaling (MDS), linear (LDA) and quadratic discriminant analysis (QDA), hierarchical and non-hierarchical cluster analysis, simple and multiple linear regression, multiple factor analysis (MFA) for quantitative, qualitative, frequency (MFACT) and mixed data, biplot, scatter plot, projection pursuit (PP), grant tour method and other useful functions for the multivariate analysis.

The main objective of this package is to support the definition of Moodle elements taking advantage of the power that R offers. In this first version, it allows the definition of quizzes to be included in the question bank.

Given independent and identically distributed observations X(1), ..., X(n) from a density f, provides five methods to perform a multiscale analysis about f as well as the necessary critical values. The first method, introduced in Duembgen and Walther (2008), provides simultaneous confidence statements for the existence and location of local increases (or decreases) of f, based on all intervals I(all) spanned by any two observations X(j), X(k). The second method approximates the latter approach by using only a subset of I(all) and is therefore computationally much more efficient, but asymptotically equivalent. Omitting the additive correction term Gamma in either method offers another two approaches which are more powerful on small scales and less powerful on large scales, however, not asymptotically minimax optimal anymore. Finally, the block procedure is a compromise between adding Gamma or not, having intermediate power properties. The latter is again asymptotically equivalent to the first and was introduced in Rufibach and Walther (2010).

Test the marginal correlation between a scalar response variable with a vector of explanatory variables using the max-type test with bootstrap. The test is based on the max-type statistic and its asymptotic distribution under the null hypothesis of no marginal correlation. The bootstrap procedure is used to approximate the null distribution of the test statistic. The package provides a function for performing the test. For more technical details, refer to Zhang and Laber (2014) <doi:10.1080/01621459.2015.1106403>.

Imputes missing values of an incomplete data matrix by minimizing the Mahalanobis distance of each sample from the overall mean [Labita, GJ.D. and Tubo, B.F. (2024) <doi:10.24412/1932-2321-2024-278-115-123>].

Fits mixed membership models with discrete multivariate data (with or without repeated measures) following the general framework of Erosheva et al (2004). This package uses a Variational EM approach by approximating the posterior distribution of latent memberships and selecting hyperparameters through a pseudo-MLE procedure. Currently supported data types are Bernoulli, multinomial and rank (Plackett-Luce). The extended GoM model with fixed stayers from Erosheva et al (2007) is now also supported. See Airoldi et al (2014) for other examples of mixed membership models.

Cooperative learning combines the usual squared error loss of predictions with an agreement penalty to encourage the predictions from different data views to agree. By varying the weight of the agreement penalty, we get a continuum of solutions that include the well-known early and late fusion approaches. Cooperative learning chooses the degree of agreement (or fusion) in an adaptive manner, using a validation set or cross-validation to estimate test set prediction error. In the setting of cooperative regularized linear regression, the method combines the lasso penalty with the agreement penalty (Ding, D., Li, S., Narasimhan, B., Tibshirani, R. (2021) <doi:10.1073/pnas.2202113119>).

MedDRA data is used for defining adverse events in clinical studies. You can load and merge the data for use in categorizing the adverse events using this package. The package requires the data licensed from MedDRA <https://www.meddra.org/>.

Used for general multiple mediation analysis. The analysis method is described in Yu and Li (2022) (ISBN: 9780367365479) "Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS", published by Chapman and Hall/CRC; and Yu et al.(2017) <DOI:10.1016/j.sste.2017.02.001> "Exploring racial disparity in obesity: a mediation analysis considering geo-coded environmental factors", published on Spatial and Spatio-temporal Epidemiology, 21, 13-23.

Gene Expression datasets for the MM2S package. Contains normalized expression data for Human Medulloblastoma ('GSE37418') as well as Mouse Medulloblastoma models ('GSE36594'). Deena Gendoo et al. (2015) <doi:10.1016/j.ygeno.2015.05.002>.

Quick and simple Tcl/Tk Graphical User Interface to call functions. Also comprises a very simple experimental GUI framework.

This package contains auxiliary routines for influx software. This packages is not intended to be used directly. Influx was published here: Sokol et al. (2012) <doi:10.1093/bioinformatics/btr716>.

Fit mixture of Markov chains of higher orders from multiple sequences. It is also compatible with ordinary 1-component, 1-order or single-sequence Markov chains. Various utility functions are provided to derive transition patterns, transition probabilities per component and component priors. In addition, print(), predict() and component extracting/replacing methods are also defined as a convention of mixture models.

This package provides estimation methods for markets in equilibrium and disequilibrium. Supports the estimation of an equilibrium and four disequilibrium models with both correlated and independent shocks. Also provides post-estimation analysis tools, such as aggregation, marginal effect, and shortage calculations. See Karapanagiotis (2024) <doi:10.18637/jss.v108.i02> for an overview of the functionality and examples. The estimation methods are based on full information maximum likelihood techniques given in Maddala and Nelson (1974) <doi:10.2307/1914215>. They are implemented using the analytic derivative expressions calculated in Karapanagiotis (2020) <doi:10.2139/ssrn.3525622>. Standard errors can be estimated by adjusting for heteroscedasticity or clustering. The equilibrium estimation constitutes a case of a system of linear, simultaneous equations. Instead, the disequilibrium models replace the market-clearing condition with a non-linear, short-side rule and allow for different specifications of price dynamics.

This package implements methods for estimating generalized estimating equations (GEE) with advanced options for flexible modeling and handling missing data. This package provides tools to fit and analyze GEE models for longitudinal data, allowing users to address missingness using a variety of imputation techniques. It supports both univariate and multivariate modeling, visualization of missing data patterns, and facilitates the transformation of data for efficient statistical analysis. Designed for researchers working with complex datasets, it ensures robust estimation and inference in longitudinal and clustered data settings.

This package provides a collection of tools for analyzing significance of Markowitz portfolios, using the delta method on the second moment matrix, <arxiv:1312.0557>.