This package provides efficient R and C++ routines to simulate cognitive diagnostic model data for Deterministic Input, Noisy "And" Gate ('DINA') and reduced Reparameterized Unified Model ('rRUM') from Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>, Culpepper (2015) <doi:10.3102/1076998615595403>, and de la Torre (2009) <doi:10.3102/1076998607309474>.

This package provides tools to simulate multi-omics datasets with predefined signal structures. The generated data can be used for testing, validating, and benchmarking integrative analysis methods such as factor models and clustering approaches. This version includes enhanced signal customization, visualization tools (scatter, histogram, 3D), MOFA-based analysis pipelines, PowerPoint export, and statistical profiling of datasets. Designed for both method development and teaching, SUMO supports real and synthetic data pipelines with interpretable outputs. Tini, Giulia, et al (2019) <doi:10.1093/bib/bbx167>.

Import, process, summarize and visualize raw data from metabolic carts. See Robergs, Dwyer, and Astorino (2010) <doi:10.2165/11319670-000000000-00000> for more details on data processing.

This package provides functions and datasets from Jones, O.D., R. Maillardet, and A.P. Robinson. 2014. An Introduction to Scientific Programming and Simulation, Using R. 2nd Ed. Chapman And Hall/CRC.

This package provides a comprehensive toolkit for extracting latent signals from panel data through multivariate time series analysis. Implements spectral decomposition methods including wavelet multiresolution analysis via maximal overlap discrete wavelet transform, Percival and Walden (2000) <doi:10.1017/CBO9780511841040>, empirical mode decomposition for non-stationary signals, Huang et al. (1998) <doi:10.1098/rspa.1998.0193>, and Bayesian trend extraction via the Grant-Chan embedded Hodrick-Prescott filter, Grant and Chan (2017) <doi:10.1016/j.jedc.2016.12.007>. Features Bayesian variable selection through regularized Horseshoe priors, Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>, for identifying structurally relevant predictors from high-dimensional candidate sets. Includes dynamic factor model estimation, principal component analysis with bootstrap significance testing, and automated technical interpretation of signal morphology and variance topology.

Allows TailwindCSS to be used in Shiny apps with just-in-time compiling, custom css with @apply directive, and custom tailwind configurations.

The price action at any given time is determined by investor sentiment and market conditions. Although there is no established principle, over a long period of time, things often move with a certain periodicity. This is sometimes referred to as anomaly. The seasonPlot() function in this package calculates and visualizes the average value of price movements over a year for any given period. In addition, the monthly increase or decrease in price movement is represented with a colored background. This seasonPlot() function can use the same symbols as the quantmod package (e.g. ^IXIC, ^DJI, SPY, BTC-USD, and ETH-USD etc).

Convert laboratory data to the Portuguese Information System for Water Resources SNIRH file format. SNIRH is Portugal's national water resources information system <https://snirh.apambiente.pt/>. The package validates station data, converts parameters and units, and generates compliant output files for data submission.

Succinctly and correctly format statistical summaries of various models and tests (F-test, Chi-Sq-test, Fisher-test, T-test, and rank-significance). This package also includes empirical tests, such as Monte Carlo and bootstrap distribution estimates.

Runs SQL statements on in-memory data frames within a temporary in-memory duckdb data base.

This package performs sensitivity analysis for Structural Equation Modeling (SEM). It determines which sample points need to be removed for the sign of a specific path in the SEM model to change, thus assessing the robustness of the model. Methodological manuscript in preparation.

This package provides methods for the computation of surface/image texture indices using a geostatistical based approach (Trevisani et al. (2023) <doi:10.1016/j.catena.2023.106927> and Trevisani and Guth (2025) <doi:10.3390/rs17233864>). It provides various functions for the computation of surface texture indices (e.g., omnidirectional roughness and roughness anisotropy), including the ones based on the robust MAD estimator. The kernels included in the software permit also to calculate the surface/image texture indices directly from the input surface (i.e., without de-trending) using increments of order 2 and of order 4. It also provides the new radial roughness index (RRI), representing the improvement of the popular topographic roughness index (TRI). The framework can be easily extended with ad-hoc surface/image texture indices.

This package provides a programmatic interface to <http://sp2000.org.cn>, re-written based on an accompanying Species 2000 API. Access tables describing catalogue of the Chinese known species of animals, plants, fungi, micro-organisms, and more. This package also supports access to catalogue of life global <http://catalogueoflife.org>, China animal scientific database <http://zoology.especies.cn> and catalogue of life Taiwan <https://taibnet.sinica.edu.tw/home_eng.php>. The development of SP2000 package were supported by Biodiversity Survey and Assessment Project of the Ministry of Ecology and Environment, China <2019HJ2096001006>,Yunnan University's "Double First Class" Project <C176240405> and Yunnan University's Research Innovation Fund for Graduate Students <2019227>.

It builds dynamic R shiny based dashboards to analyze any CSV files. It provides simple dashboard design to subset the data, perform exploratory data analysis and preliminary machine learning (supervised and unsupervised). It also provides filters based on columns of interest.

The saemix package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. It (i) computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, (ii) provides standard errors for the maximum likelihood estimator (iii) estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm (see Comets et al. (2017) <doi:10.18637/jss.v080.i03>). Many applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group. The full PDF documentation for the package including references about the algorithm and examples can be downloaded on the github of the IAME research institute for saemix': <https://github.com/iame-researchCenter/saemix/blob/7638e1b09ccb01cdff173068e01c266e906f76eb/docsaem.pdf>.

Single-Index Quantile Regression is effective in some scenarios. We provides functions that allow users to fit Single-Index Quantile Regression model. It also provides functions to do prediction, estimate standard errors of the single-index coefficients via bootstrap, and visualize the estimated univariate function. Please see W., Y., Y. (2010) <doi:10.1016/j.jmva.2010.02.003> for details.

Package provides a set of tools for robust estimation and inference for models with sample selectivity and endogenous treatment model. For details, see Zhelonkin and Ronchetti (2021) <doi:10.18637/jss.v099.i04>.

This package provides a compilation of functions designed to assist users on the correlation analysis of crop yield and soil test values. Functions to estimate crop response patterns to soil nutrient availability and critical soil test values using various approaches such as: 1) the modified arcsine-log calibration curve (Correndo et al. (2017) <doi:10.1071/CP16444>); 2) the graphical Cate-Nelson quadrants analysis (Cate & Nelson (1965)), 3) the statistical Cate-Nelson quadrants analysis (Cate & Nelson (1971) <doi:10.2136/sssaj1971.03615995003500040048x>), 4) the linear-plateau regression (Anderson & Nelson (1975) <doi:10.2307/2529422>), 5) the quadratic-plateau regression (Bullock & Bullock (1994) <doi:10.2134/agronj1994.00021962008600010033x>), and 6) the Mitscherlich-type exponential regression (Melsted & Peck (1977) <doi:10.2134/asaspecpub29.c1>). The package development stemmed from ongoing work with the Fertilizer Recommendation Support Tool (FRST) and Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification (SIIL) projects.

Implementations self-normalization (SN) based algorithms for change-points estimation in time series data. This comprises nested local-window algorithms for detecting changes in both univariate and multivariate time series developed in Zhao, Jiang and Shao (2022) <doi:10.1111/rssb.12552>.

This package provides functions to enumerate and reference figures, tables and equations in R Markdown documents that do not support these features (thus not bookdown or quarto'. Supporting functions for using Sweave and Knitr with LyX'.

This package provides some code to run simulations of state-space models, and then use these in the Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) algorithm of Toni et al. (2009) <doi:10.1098/rsif.2008.0172> and a bootstrap particle filter based particle Markov chain Monte Carlo (PMCMC) algorithm (Andrieu et al., 2010 <doi:10.1111/j.1467-9868.2009.00736.x>). Also provides functions to plot and summarise the outputs.

This package provides inference based on the survey package for the wide range of parametric models in the VGAM package.

Este paquete tiene la finalidad de ayudar a aprender de una forma interactiva, teniendo ejemplos y la posibilidad de resolver nuevos al mismo tiempo. Apuntes de clase interactivos.

Fast single trait Genome Wide Association Studies (GWAS) following the method described in Kang et al. (2010), <doi:10.1038/ng.548>. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris.