This package provides a function for plotting multivariate time series data.

Computing package for Multidimensional Poverty Index (MPI) using Alkire-Foster method. Given N individuals, each person has D indicators of deprivation, the package compute MPI value to represent the degree of poverty in a population. The inputs are 1) an N by D matrix, which has the element (i,j) represents whether an individual i is deprived in an indicator j (1 is deprived and 0 is not deprived), and 2) the deprivation threshold. The main output is the MPI value, which has the range between zero and one. MPI value is approaching one if almost all people are deprived in all indicators, and it is approaching zero if almost no people are deprived in any indicator. Please see Alkire S., Chatterjee, M., Conconi, A., Seth, S. and Ana Vaz (2014) <doi:10.35648/20.500.12413/11781/ii039> for The Alkire-Foster methodology.

This package provides functions and datasets from Hilbe, J.M., and Robinson, A.P. 2013. Methods of Statistical Model Estimation. Chapman & Hall / CRC.

This package provides a multivariate generalization of the emulator package.

This package contains functions for mapping odds ratios, hazard ratios, or other effect estimates using individual-level data such as case-control study data, using generalized additive models (GAMs) or Cox models for smoothing with a two-dimensional predictor (e.g., geolocation or exposure to chemical mixtures) while adjusting linearly for confounding variables, using methods described by Kelsall and Diggle (1998), Webster at al. (2006), and Bai et al. (2020). Includes convenient functions for mapping point estimates and confidence intervals, efficient control sampling, and permutation tests for the null hypothesis that the two-dimensional predictor is not associated with the outcome variable (adjusting for confounders).

This package provides tools to create a layout for figures made of multiple panels, and to fill the panels with base, lattice', ggplot2 and ComplexHeatmap plots, grobs, as well as content from all image formats supported by ImageMagick (accessed through magick').

Quantitative RT-PCR data are analyzed using generalized linear mixed models based on lognormal-Poisson error distribution, fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). The package also implements a lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results. The detailed tutorial is available here: <https://matzlab.weebly.com/uploads/7/6/2/2/76229469/mcmc.qpcr.tutorial.v1.2.4.pdf>.

There are three different modules: (1) model fitting and selection using a set of the most commonly used equations describing developmental responses to temperature helped by already existing R packages ('rTPC') and nonlinear regression model functions from nls.multstart (Padfield et al. 2021, <doi:10.1111/2041-210X.13585>), with visualization of model predictions to guide ecological criteria for model selection; (2) calculation of suitability thermal limits, which consist on a temperature interval delimiting the optimal performance zone or suitability; and (3) climatic data extraction and visualization inspired on previous research (Taylor et al. 2019, <doi:10.1111/1365-2664.13455>), with either exportable rasters, static map images or html, interactive maps.

Fused lasso method to cluster and estimate regression coefficients of the same covariate across different data sets when a large number of independent data sets are combined. Package supports Gaussian, binomial, Poisson and Cox PH models.

This package provides a simple and trustworthy methodology for the analysis of misreported continuous time series. See Moriña, D, Fernández-Fontelo, A, Cabaña, A, Puig P. (2021) <arXiv:2003.09202v2>.

Allows the user to generate a friendly user interface for emails sending. The user can choose from the most popular free email services ('Gmail', Outlook', Yahoo') and his default email application. The package is a wrapper for the Mailtoui JavaScript library. See <https://mailtoui.com/#menu> for more information.

Citrus is a computational technique developed for the analysis of high dimensional cytometry data sets. This package extracts, statistically analyzes, and visualizes marker expression from citrus data. This code was used to generate data for Figures 3 and 4 in the forthcoming manuscript: Throm et al. â Identification of Enhanced Interferon-Gamma Signaling in Polyarticular Juvenile Idiopathic Arthritis with Mass Cytometryâ , JCI-Insight. For more information on Citrus, please see: Bruggner et al. (2014) <doi:10.1073/pnas.1408792111>. To download the citrus package, please see <https://github.com/nolanlab/citrus>.

Lattice functions for drawing folded empirical cumulative distribution plots, or mountain plots. A mountain plot is similar to an empirical CDF plot, except that the curve increases from 0 to 0.5, then decreases from 0.5 to 1 using an inverted scale at the right side. See Monti (1995) <doi:10.1080/00031305.1995.10476179>.

Publicly available data from Medicare frequently requires extensive initial effort to extract desired variables and merge them; this package formalizes the techniques I've found work best. More information on the Medicare program, as well as guidance for the publicly available data this package targets, can be found on CMS's website covering publicly available data. See <https://www.cms.gov/Research-Statistics-Data-and-Systems/Research-Statistics-Data-and-Systems.html>.

Parses information from text files with specific utility aimed at pulling information from Med Associate's (MPC) files. These functions allow for further analysis of MPC files.

This package performs treatment assignment for (field) experiments considering available, possibly multivariate and continuous, information (covariates, observable characteristics), that is: forms balanced treatment groups, according to the minMSE-method as proposed by Schneider and Schlather (2017) <DOI:10419/161931>.

Employing artificial intelligence to convert data analysis questions into executable code, explanations, and algorithms. The self-correction feature ensures the generated code is optimized for performance and accuracy. mergen features a user-friendly chat interface, enabling users to interact with the AI agent and extract valuable insights from their data effortlessly.

Calculate Sample Size and Power for Association Studies Involving Mitochondrial DNA Haplogroups. Based on formulae by Samuels et al. AJHG, 2006. 78(4):713-720. <DOI:10.1086/502682>.

It computes arbitrary products moments (mean vector and variance-covariance matrix), for some double truncated (and folded) multivariate distributions. These distributions belong to the family of selection elliptical distributions, which includes well known skewed distributions as the unified skew-t distribution (SUT) and its particular cases as the extended skew-t (EST), skew-t (ST) and the symmetric student-t (T) distribution. Analogous normal cases unified skew-normal (SUN), extended skew-normal (ESN), skew-normal (SN), and symmetric normal (N) are also included. Density, probabilities and random deviates are also offered for these members.

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

Estimation of marginal hazard ratios in clustered failure time data. It implements the weighted generalized estimating equation approach based on a semiparametric marginal proportional hazards model (See Niu, Y. Peng, Y.(2015). "A new estimating equation approach for marginal hazard ratio estimation"), accounting for within-cluster correlations. 5 different correlation structures are supported. The package is designed for researchers in biostatistics and epidemiology who require accurate and efficient estimation methods for survival analysis in clustered data settings.

Compute effect sizes and their sampling variances from factorial experimental designs. The package supports calculation of simple effects, overall effects, and interaction effects for use in factorial meta-analyses. See Gurevitch et al. (2000) <doi:10.1086/303337>, Morris et al. (2007) <doi:10.1890/06-0442>, Lajeunesse (2011) <doi:10.1890/11-0423.1> and Macartney et al. (2022) <doi:10.1016/j.neubiorev.2022.104554>.

Distance between multivariate Cauchy distributions, as presented by N. Bouhlel and D. Rousseau (2022) <doi:10.3390/e24060838>. Manipulation of multivariate Cauchy distributions.

Meta-analyses can be compromised by studies internal biases (e.g., confounding in nonrandomized studies) as well as by publication bias. This package conducts sensitivity analyses for the joint effects of these biases (per Mathur (2022) <doi:10.31219/osf.io/u7vcb>). These sensitivity analyses address two questions: (1) For a given severity of internal bias across studies and of publication bias, how much could the results change?; and (2) For a given severity of publication bias, how severe would internal bias have to be, hypothetically, to attenuate the results to the null or by a given amount?