Bayesian variable selection using shrinkage priors to identify significant variables in high-dimensional datasets. The package includes methods for determining the number of significant variables through innovative clustering techniques of posterior distributions, specifically utilizing the 2-Means and Sequential 2-Means (S2M) approaches. The package aims to simplify the variable selection process with minimal tuning required in statistical analysis.

Access and analyze the World Bank's World Development Indicators (WDI) using the corresponding API <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation>. WDI provides more than 24,000 country or region-level indicators for various contexts. wbwdi enables users to download, process and work with WDI series across multiple countries, aggregates, and time periods.

Statistical models and utilities for the analysis of word frequency distributions. The utilities include functions for loading, manipulating and visualizing word frequency data and vocabulary growth curves. The package also implements several statistical models for the distribution of word frequencies in a population. (The name of this package derives from the most famous word frequency distribution, Zipf's law.).

Calculate RNNI distance between and manipulate with ranked trees. RNNI stands for Ranked Nearest Neighbour Interchange and is an extension of the classical NNI space (space of trees created by the NNI moves) to ranked trees, where internal nodes are ordered according to their heights (usually assumed to be times). The RNNI distance takes the tree topology into account, as standard NNI does, but also penalizes changes in the order of internal nodes, i.e. changes in the order of times of evolutionary events. For more information about the RNNI space see: Gavryushkin et al. (2018) <doi:10.1007/s00285-017-1167-9>, Collienne & Gavryushkin (2021) <doi:10.1007/s00285-021-01567-5>, Collienne et al. (2021) <doi:10.1007/s00285-021-01685-0>, and Collienne (2021) <http://hdl.handle.net/10523/12606>.

This package provides a comprehensive collection of practical and easy-to-use tools for regression analysis of recurrent events, with or without the presence of a (possibly) informative terminal event described in Chiou et al. (2023) <doi:10.18637/jss.v105.i05>. The modeling framework is based on a joint frailty scale-change model, that includes models described in Wang et al. (2001) <doi:10.1198/016214501753209031>, Huang and Wang (2004) <doi:10.1198/016214504000001033>, Xu et al. (2017) <doi:10.1080/01621459.2016.1173557>, and Xu et al. (2019) <doi:10.5705/SS.202018.0224> as special cases. The implemented estimating procedure does not require any parametric assumption on the frailty distribution. The package also allows the users to specify different model forms for both the recurrent event process and the terminal event.

The r-mhsmm package implements estimation and prediction methods for hidden Markov and semi-Markov models for multiple observation sequences. Such techniques are of interest when observed data is thought to be dependent on some unobserved (or hidden) state. Also, this package is suitable for equidistant time series data, with multivariate and/or missing data. Allows user defined emission distributions.

The prebs package aims at making RNA-sequencing (RNA-seq) data more comparable to microarray data. The comparability is achieved by summarizing sequencing-based expressions of probe regions using a modified version of RMA algorithm. The pipeline takes mapped reads in BAM format as an input and produces either gene expressions or original microarray probe set expressions as an output.

Estimate the causal treatment effect for subjects that can adhere to one or both of the treatments. Given longitudinal data with missing observations, consistent causal effects are calculated. Unobserved potential outcomes are estimated through direct integration as described in: Qu et al., (2019) <doi:10.1080/19466315.2019.1700157> and Zhang et. al., (2021) <doi:10.1080/19466315.2021.1891965>.

This package performs goodness of fit test for the Birnbaum-Saunders distribution and provides the maximum likelihood estimate and the method-of-moments estimate. For more details, see Park and Wang (2013) <arXiv:2308.10150>. This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. 2022R1A2C1091319, RS-2023-00242528).

Use three methods to estimate parameters from a mediation analysis with a binary misclassified mediator. These methods correct for the problem of "label switching" using Youden's J criteria. A detailed description of the analysis methods is available in Webb and Wells (2024), "Effect estimation in the presence of a misclassified binary mediator" <doi:10.48550/arXiv.2407.06970>.

Fit of a double additive location-scale model with a nonparametric error distribution from possibly right- or interval censored data. The additive terms in the location and dispersion submodels, as well as the unknown error distribution in the location-scale model, are estimated using Laplace P-splines. For more details, see Lambert (2021) <doi:10.1016/j.csda.2021.107250>.

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.

Evidential regression analysis for dichotomous and quantitative outcome data. The following references described the methods in this package: Strug, L. J., Hodge, S. E., Chiang, T., Pal, D. K., Corey, P. N., & Rohde, C. (2010) <doi:10.1038/ejhg.2010.47>. Strug, L. J., & Hodge, S. E. (2006) <doi:10.1159/000094709>. Royall, R. (1997) <ISBN:0-412-04411-0>.

This package provides statistical and visualization tools for the analysis of demographic indicators, and spatio-temporal behavior and characterization of outbreaks of vector-borne diseases (VBDs) in Colombia. It implements travel times estimated in Bravo-Vega C., Santos-Vega M., & Cordovez J.M. (2022), and the endemic channel method (Bortman, M. (1999) <https://iris.paho.org/handle/10665.2/8562>).

Fits Zeta distributions (discrete power laws) to data that arises from forensic surveys of clothing on the presence of glass and paint in various populations. The general method is described to some extent in Coulson, S.A., Buckleton, J.S., Gummer, A.B., and Triggs, C.M. (2001) <doi:10.1016/S1355-0306(01)71847-3>, although the implementation differs.

Fast, numerically robust computation of weighted moments via Rcpp'. Supports computation on vectors and matrices, and Monoidal append of moments. Moments and cumulants over running fixed length windows can be computed, as well as over time-based windows. Moment computations are via a generalization of Welford's method, as described by Bennett et. (2009) <doi:10.1109/CLUSTR.2009.5289161>.

This package implements methods for network estimation and forecasting of high-dimensional time series exhibiting strong serial and cross-sectional correlations under a factor-adjusted vector autoregressive model. See Barigozzi, Cho and Owens (2024+) <doi:10.1080/07350015.2023.2257270> for further descriptions of FNETS methodology and Owens, Cho and Barigozzi (2024+) <arXiv:2301.11675> accompanying the R package.

Process in-situ Gamma-Ray Spectrometry for Luminescence Dating. This package allows to import, inspect and correct the energy shifts of gamma-ray spectra. It provides methods for estimating the gamma dose rate by the use of a calibration curve as described in Mercier and Falguères (2007). The package only supports Canberra CNF and TKA and Kromek SPE files.

Modern Parallel Coordinate Plots have been introduced in the 1980s as a way to visualize arbitrarily many numeric variables. This Grammar of Graphics implementation also incorporates categorical variables into the plots in a principled manner. By separating the data managing part from the visual rendering, we give full access to the users while keeping the number of parameters manageably low.

Scan multiple Git repositories, pull specified files content and process it with large language models. You can summarize the content in specific way, extract information and data, or find answers to your questions about the repositories. The output can be stored in vector database and used for semantic search or as a part of a RAG (Retrieval Augmented Generation) prompt.

This package provides tools for creating publication-ready dimensionality reduction plots, including Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). This package helps visualize high-dimensional data with options for custom labels, density plots, and faceting, using the ggplot2 framework Wickham (2016) <doi:10.1007/978-3-319-24277-4>.

This package provides a lightweight framework for building server-driven web applications in R'. htmxr combines the simplicity of htmx for partial page updates with the power of plumber2 for non-blocking HTTP endpoints. Build interactive dashboards and data applications without writing JavaScript', using familiar R patterns inspired by Shiny'. For more information on htmx', see <https://htmx.org>.

This package provides a integrated variance correlation is proposed to measure the dependence between a categorical or continuous random variable and a continuous random variable or vector. This package is designed to estimate the new correlation coefficient with parametric and nonparametric approaches. Test of independence for different problems can also be implemented via the new correlation coefficient with this package.

Implementation of various kernel adaptive methods in nonparametric curve estimation like density estimation as introduced in Stute and Srihera (2011) <doi:10.1016/j.spl.2011.01.013> and Eichner and Stute (2013) <doi:10.1016/j.jspi.2012.03.011> for pointwise estimation, and like regression as described in Eichner and Stute (2012) <doi:10.1080/10485252.2012.760737>.