This package performs parallel analysis (Timmerman & Lorenzo-Seva, 2011 <doi:10.1037/a0023353>) and hull method (Lorenzo-Seva, Timmerman, & Kiers, 2011 <doi:10.1080/00273171.2011.564527>) for assessing the dimensionality of a set of variables using minimum rank factor analysis (see ten Berge & Kiers, 1991 <doi:10.1007/BF02294464> for more information). The package also includes the option to compute minimum rank factor analysis by itself, as well as the greater lower bound calculation.

Estimation of the sample univariate, cross and return time extremograms. The package can also adds empirical confidence bands to each of the extremogram plots via a permutation procedure under the assumption that the data are independent. Finally, the stationary bootstrap allows us to construct credible confidence bands for the extremograms.

Correlation chart of two set (x and y) of data. Using Quantiles. Visualize the effect of factor.

This package provides tools for the analysis of epidemiological and surveillance data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, computation of confidence intervals around incidence risk and incidence rate estimates and sample size calculations for cross-sectional, case-control and cohort studies. Surveillance tools include functions to calculate an appropriate sample size for 1- and 2-stage representative freedom surveys, functions to estimate surveillance system sensitivity and functions to support scenario tree modelling analyses.

Package computes and displays tables with support for SPSS'-style labels, multiple and nested banners, weights, multiple-response variables and significance testing. There are facilities for nice output of tables in knitr', Shiny', *.xlsx files, R and Jupyter notebooks. Methods for labelled variables add value labels support to base R functions and to some functions from other packages. Additionally, the package brings popular data transformation functions from SPSS Statistics and Excel': RECODE', COUNT', COUNTIF', VLOOKUP and etc. These functions are very useful for data processing in marketing research surveys. Package intended to help people to move data processing from Excel and SPSS to R.

Easily create interactive charts by leveraging the Echarts Javascript library which includes 36 chart types, themes, Shiny proxies and animations.

This package provides methods to simulate and analyse the size and length of branching processes with an arbitrary offspring distribution. These can be used, for example, to analyse the distribution of chain sizes or length of infectious disease outbreaks, as discussed in Farrington et al. (2003) <doi:10.1093/biostatistics/4.2.279>.

Computes exact p-values for multinomial goodness-of-fit tests based on multiple test statistics, namely, Pearson's chi-square, the log-likelihood ratio and the probability mass statistic. Implements the algorithm detailed in Resin (2023) <doi:10.1080/10618600.2022.2102026>. Estimates based on the classical asymptotic chi-square approximation or Monte-Carlo simulation can also be computed.

Estimates linear panel event study models. Plots coefficients following the recommendations in Freyaldenhoven et al. (2021) <doi:10.3386/w29170>. Includes sup-t bands, testing for key hypotheses, least wiggly path through the Wald region. Allows instrumental variables estimation following Freyaldenhoven et al. (2019) <doi:10.1257/aer.20180609>.

An implementation of the ESS algorithm following Amol Deshpande, Minos Garofalakis, Michael I Jordan (2013) <doi:10.48550/arXiv.1301.2267>. The ESS algorithm is used for model selection in decomposable graphical models.

This package implements a Markov Chain Monte Carlo algorithm to approximate exact conditional inference for logistic regression models. Exact conditional inference is based on the distribution of the sufficient statistics for the parameters of interest given the sufficient statistics for the remaining nuisance parameters. Using model formula notation, users specify a logistic model and model terms of interest for exact inference. See Zamar et al. (2007) <doi:10.18637/jss.v021.i03> for more details.

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 methods for estimating parameter-dependent network centrality measures with linear-in-means models. Both non linear least squares and maximum likelihood estimators are implemented. The methods allow for both link and node heterogeneity in network effects, endogenous network formation and the presence of unconnected nodes. The routines also compare the explanatory power of parameter-dependent network centrality measures with those of standard measures of network centrality. Benefits and features of the econet package are illustrated using data from Battaglini and Patacchini (2018) and Battaglini, Patacchini, and Leone Sciabolazza (2020). For additional details, see the vignette <doi:10.18637/jss.v102.i08>.

The peak fitting of spectral data is performed by using the frame work of EM algorithm. We adapted the EM algorithm for the peak fitting of spectral data set by considering the weight of the intensity corresponding to the measurement energy steps (Matsumura, T., Nagamura, N., Akaho, S., Nagata, K., & Ando, Y. (2019, 2021 and 2023) <doi:10.1080/14686996.2019.1620123>, <doi:10.1080/27660400.2021.1899449> <doi:10.1080/27660400.2022.2159753>. The package efficiently estimates the parameters of Gaussian mixture model during iterative calculation between E-step and M-step, and the parameters are converged to a local optimal solution. This package can support the investigation of peak shift with two advantages: (1) a large amount of data can be processed at high speed; and (2) stable and automatic calculation can be easily performed.

Current layout algorithms such as Kamada Kawai do not take into consideration disjoint clusters in a network, often resulting in a high overlap among the clusters, resulting in a visual â hairballâ that often is uninterpretable. The ExplodeLayout algorithm takes as input (1) an edge list of a unipartite or bipartite network, (2) node layout coordinates (x, y) generated by a layout algorithm such as Kamada Kawai, (3) node cluster membership generated from a clustering algorithm such as modularity maximization, and (4) a radius to enable the node clusters to be â explodedâ to reduce their overlap. The algorithm uses these inputs to generate new layout coordinates of the nodes which â explodesâ the clusters apart, such that the edge lengths within the clusters are preserved, while the edge lengths between clusters are recalculated. The modified network layout with nodes and edges are displayed in two dimensions. The user can experiment with different explode radii to generate a layout which has sufficient separation of clusters, while reducing the overall layout size of the network. This package is a basic version of an earlier version called [epl]<https://github.com/UTMB-DIVA-Lab/epl> that searched for an optimal explode radius, and offered multiple ways to separate clusters in a network (Bhavnani et al(2017) <https://pmc.ncbi.nlm.nih.gov/articles/PMC5543384/>). The example dataset is for a bipartite network, but the algorithm can work also for unipartite networks.

This package provides a flexible framework for Agent-Based Models (ABM), the epiworldR package provides methods for prototyping disease outbreaks and transmission models using a C++ backend, making it very fast. It supports multiple epidemiological models, including the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Removed (SIR), Susceptible-Exposed-Infected-Removed (SEIR), and others, involving arbitrary mitigation policies and multiple-disease models. Users can specify infectiousness/susceptibility rates as a function of agents features, providing great complexity for the model dynamics. Furthermore, epiworldR is ideal for simulation studies featuring large populations.

This package provides an R interface to the Evolution API <https://evoapicloud.com>, enabling sending and receiving WhatsApp messages directly from R'. Functions include sending text, images, documents, stickers, geographic locations, and interactive messages (lists). Also includes webhook parsing utilities and channel health checks.

This package provides an interface to e-Stat API, the one-stop service for official statistics of the Japanese government.

This package provides tools for transforming R expressions. Provides functions for finding, extracting, and replacing patterns in R language objects, similarly to how regular expressions can be used to find, extract, and replace patterns in text. Also provides functions for generating code using specially-formatted template files and for translating R expressions into similar expressions in other programming languages. The package may be helpful for advanced uses of R expressions, such as developing domain-specific languages.

This package provides basic distribution functions for a mixture model of a Gaussian and exponential distribution.

This package provides a user friendly, easy to understand way of doing event history regression for marginal estimands of interest, including the cumulative incidence and the restricted mean survival, using the pseudo observation framework for estimation. For a review of the methodology, see Andersen and Pohar Perme (2010) <doi:10.1177/0962280209105020> or Sachs and Gabriel (2022) <doi:10.18637/jss.v102.i09>. The interface uses the well known formulation of a generalized linear model and allows for features including plotting of residuals, the use of sampling weights, and corrected variance estimation.

This package provides a framework to simulate ecosystem dynamics through ordinary differential equations (ODEs). You create an ODE model, tells ecode to explore its behaviour, and perform numerical simulations on the model. ecode also allows you to fit model parameters by machine learning algorithms. Potential users include researchers who are interested in the dynamics of ecological community and biogeochemical cycles.

This package provides methods for data analysis from an entropic perspective. These methods are nonparametric and perform well on non-ordinal data. Currently includes HeatMap() for visualizing distributional characteristics among multiple populations (groups).

An eikosogram (ancient Greek for probability picture) divides the unit square into rectangular regions whose areas, sides, and widths represent various probabilities associated with the values of one or more categorical variates. Rectangle areas are joint probabilities, widths are always marginal (though possibly joint margins, i.e. marginal joint distributions of two or more variates), and heights of rectangles are always conditional probabilities. Eikosograms embed the rules of probability and are useful for introducing elementary probability theory, including axioms, marginal, conditional, and joint probabilities, and their relationships (including Bayes theorem as a completely trivial consequence). They provide advantages over Venn diagrams for this purpose, particularly in distinguishing probabilistic independence, mutually exclusive events, coincident events, and associations. They also are useful for identifying and understanding conditional independence structure. Eikosograms can be thought of as mosaic plots when only two categorical variates are involved; the layout is quite different when there are more than two variates. Only one categorical variate, designated the "response", presents on the vertical axis and all others, designated the "conditioning" variates, appear on the horizontal. In this way, conditional probability appears only as height and marginal probabilities as widths. The eikosogram is ideal for response models (e.g. logistic models) but equally useful when no variate is distinguished as the response. In such cases, each variate can appear in turn as the response, which is handy for assessing conditional independence in discrete graphical models (i.e. "Bayesian networks" or "BayesNets"). The eikosogram and its value over Venn diagrams in teaching probability is described in W.H. Cherry and R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/paper.pdf>, its value in exploring conditional independence structure and relation to graphical and log-linear models is described in R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/independence/paper.pdf>, and a number of problems, puzzles, and paradoxes that are easily explained with eikosograms are given in R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/examples/paper.pdf>.