This package provides a toolbox to make it easy to analyze plant disease epidemics. It provides a common framework for plant disease intensity data recorded over time and/or space. Implemented statistical methods are currently mainly focused on spatial pattern analysis (e.g., aggregation indices, Taylor and binary power laws, distribution fitting, SADIE and mapcomp methods). See Laurence V. Madden, Gareth Hughes, Franck van den Bosch (2007) <doi:10.1094/9780890545058> for further information on these methods. Several data sets that were mainly published in plant disease epidemiology literature are also included in this package.

The EM algorithm is a powerful tool for computing maximum likelihood estimates with incomplete data. This package will help to applying EM algorithm based on triangular and trapezoidal fuzzy numbers (as two kinds of incomplete data). A method is proposed for estimating the unknown parameter in a parametric statistical model when the observations are triangular or trapezoidal fuzzy numbers. This method is based on maximizing the observed-data likelihood defined as the conditional probability of the fuzzy data; for more details and formulas see Denoeux (2011) <doi:10.1016/j.fss.2011.05.022>.

We provide a non-parametric and a parametric approach to investigate the equivalence (or non-inferiority) of two survival curves, obtained from two given datasets. The test is based on the creation of confidence intervals at pre-specified time points. For the non-parametric approach, the curves are given by Kaplan-Meier curves and the variance for calculating the confidence intervals is obtained by Greenwood's formula. The parametric approach is based on estimating the underlying distribution, where the user can choose between a Weibull, Exponential, Gaussian, Logistic, Log-normal or a Log-logistic distribution. Estimates for the variance for calculating the confidence bands are obtained by a (parametric) bootstrap approach. For this bootstrap censoring is assumed to be exponentially distributed and estimates are obtained from the datasets under consideration. All details can be found in K.Moellenhoff and A.Tresch: Survival analysis under non-proportional hazards: investigating non-inferiority or equivalence in time-to-event data <arXiv:2009.06699>.

Extracting desired data using the proper Census variable names can be time-consuming. This package takes the pain out of that process by providing functions to quickly locate variables and download labeled tables from the Census APIs (<https://www.census.gov/data/developers/data-sets.html>).

Allows calculating global scores for characteristics of visual stimuli as assessed by human raters. Stimuli are presented as sequence of pairwise comparisons ('contests'), during each of which a rater expresses preference for one stimulus over the other (forced choice). The algorithm for calculating global scores is based on Elo rating, which updates individual scores after each single pairwise contest. Elo rating is widely used to rank chess players according to their performance. Its core feature is that dyadic contests with expected outcomes lead to smaller changes of participants scores than outcomes that were unexpected. As such, Elo rating is an efficient tool to rate individual stimuli when a large number of such stimuli are paired against each other in the context of experiments where the goal is to rank stimuli according to some characteristic of interest. Clark et al (2018) <doi:10.1371/journal.pone.0190393> provide details.

This package provides a set of methods to access and parse live filing information from the U.S. Securities and Exchange Commission (SEC - <https://www.sec.gov/>) including company and fund filings along with all associated metadata.

Utilities for building certain kinds of common matrices and models in the extended structural equation modeling package, OpenMx'.

Tests the equality of two covariance matrices, used in paper "Two sample tests for high dimensional covariance matrices." Li and Chen (2012) <arXiv:1206.0917>.

Fit, plot and compare several (extreme value) distribution functions. Compute (truncated) distribution quantile estimates and plot return periods on a linear scale. On the fitting method, see Asquith (2011): Distributional Analysis with L-moment Statistics [...] ISBN 1463508417.

Estimation for high conditional quantiles based on quantile regression.

Estimation of unknown historical or archaeological dates subject to relationships with other relative dates and absolute constraints, derived as marginal densities from the full joint conditional, using a two-stage Gibbs sampler with consistent batch means to assess convergence. Features reporting on Monte Carlo standard errors, as well as tools for rule-based estimation of dates of production and use of artifact types, aligning and checking relative sequences, and evaluating the impact of the omission of relative/absolute events upon one another.

Produce maximum likelihood estimates of common accuracy statistics for multiple measurement methods when a gold standard is not available. An R implementation of the expectation maximization algorithms described in Zhou et al. (2011) <doi:10.1002/9780470906514> with additional functions for creating simulated data and visualizing results. Supports binary, ordinal, and continuous measurement methods.

Evolutionary game theory applies game theory to evolving populations in biology, see e.g. one of the books by Weibull (1994, ISBN:978-0262731218) or by Sandholm (2010, ISBN:978-0262195874) for more details. A comprehensive set of tools to illustrate the core concepts of evolutionary game theory, such as evolutionary stability or various evolutionary dynamics, for teaching and academic research is provided.

We provide functions to fit finite mixtures of multivariate normal or t-distributions to data with various factor analytic structures adopted for the covariance/scale matrices. The factor analytic structures available include mixtures of factor analyzers and mixtures of common factor analyzers. The latter approach is so termed because the matrix of factor loadings is common to components before the component-specific rotation of the component factors to make them white noise. Note that the component-factor loadings are not common after this rotation. Maximum likelihood estimators of model parameters are obtained via the Expectation-Maximization algorithm. See descriptions of the algorithms used in McLachlan GJ, Peel D (2000) <doi:10.1002/0471721182.ch8> McLachlan GJ, Peel D (2000) <ISBN:1-55860-707-2> McLachlan GJ, Peel D, Bean RW (2003) <doi:10.1016/S0167-9473(02)00183-4> McLachlan GJ, Bean RW, Ben-Tovim Jones L (2007) <doi:10.1016/j.csda.2006.09.015> Baek J, McLachlan GJ, Flack LK (2010) <doi:10.1109/TPAMI.2009.149> Baek J, McLachlan GJ (2011) <doi:10.1093/bioinformatics/btr112> McLachlan GJ, Baek J, Rathnayake SI (2011) <doi:10.1002/9781119995678.ch9>.

Variable selection methods have been extensively developed for analyzing highdimensional omics data within both the frequentist and Bayesian frameworks. This package provides implementations of the spike-and-slab quantile (group) LASSO which have been developed along the line of Bayesian hierarchical models but deeply rooted in frequentist regularization methods by utilizing Expectationâ Maximization (EM) algorithm. The spike-and-slab quantile LASSO can handle data irregularity in terms of skewness and outliers in response variables, compared to its non-robust alternative, the spike-and-slab LASSO, which has also been implemented in the package. In addition, procedures for fitting the spike-and-slab quantile group LASSO and its non-robust counterpart have been implemented in the form of quantile/least-square varying coefficient mixed effect models for high-dimensional longitudinal data. The core module of this package is developed in C++'.

This package provides functions supporting the reading and parsing of internal e-book content from EPUB files. The epubr package provides functions supporting the reading and parsing of internal e-book content from EPUB files. E-book metadata and text content are parsed separately and joined together in a tidy, nested tibble data frame. E-book formatting is not completely standardized across all literature. It can be challenging to curate parsed e-book content across an arbitrary collection of e-books perfectly and in completely general form, to yield a singular, consistently formatted output. Many EPUB files do not even contain all the same pieces of information in their respective metadata. EPUB file parsing functionality in this package is intended for relatively general application to arbitrary EPUB e-books. However, poorly formatted e-books or e-books with highly uncommon formatting may not work with this package. There may even be cases where an EPUB file has DRM or some other property that makes it impossible to read with epubr'. Text is read as is for the most part. The only nominal changes are minor substitutions, for example curly quotes changed to straight quotes. Substantive changes are expected to be performed subsequently by the user as part of their text analysis. Additional text cleaning can be performed at the user's discretion, such as with functions from packages like tm or qdap'.

Simulation and estimation of Exponential Random Graph Models (ERGMs) for small networks using exact statistics as shown in Vega Yon et al. (2020) <DOI:10.1016/j.socnet.2020.07.005>. As a difference from the ergm package, ergmito circumvents using Markov-Chain Maximum Likelihood Estimator (MC-MLE) and instead uses Maximum Likelihood Estimator (MLE) to fit ERGMs for small networks. As exhaustive enumeration is computationally feasible for small networks, this R package takes advantage of this and provides tools for calculating likelihood functions, and other relevant functions, directly, meaning that in many cases both estimation and simulation of ERGMs for small networks can be faster and more accurate than simulation-based algorithms.

Ensemble correlation-based low-rank matrix completion method (ECLRMC) is an extension to the LRMC based methods. Traditionally, the LRMC based methods give identical importance to the whole data which results in emphasizing on the commonality of the data and overlooking the subtle but crucial differences. This method aims to overcome the equality assumption problem that exists in the current LRMS based methods. Ensemble correlation-based low-rank matrix completion (ECLRMC) takes consideration of the specific characteristic of each sample and performs LRMC on the set of samples with a strong correlation. It uses an ensemble learning method to improve the imputation performance. Since each sample is analyzed independently this method can be parallelized by distributing imputation across many computation units or GPU platforms. This package provides three different methods (LRMC, CLRMC and ECLRMC) for data imputation. There is also an NRMS function for evaluating the result. Chen, Xiaobo, et al (2017) <doi:10.1016/j.knosys.2017.06.010>.

Reliable and rapid ethnicity annotation from whole exome and targeted sequencing data.

This package provides a wrapper of different methods from Linear Algebra for the equations introduced in The Atlas of Economic Complexity and related literature. This package provides standard matrix and graph output that can be used seamlessly with other packages. See <doi:10.21105/joss.01866> for a summary of these methods and its evolution in literature.

This package provides a tool for conducting exact parametric regression-based causal mediation analysis of binary outcomes as described in Samoilenko, Blais and Lefebvre (2018) <doi:10.1353/obs.2018.0013>; Samoilenko, Lefebvre (2021) <doi:10.1093/aje/kwab055>; and Samoilenko, Lefebvre (2023) <doi:10.1002/sim.9621>.

Deliver the full functionality of ECharts with minimal overhead. echarty users build R lists for ECharts API. Lean set of powerful commands.

Collection of functions related to benchmark with prediction models for data analysis and editing of clinical and epidemiological data.

This package provides a lightweight implementation of functions and methods for fast and fully automatic time series modeling and forecasting using Echo State Networks (ESNs).