Set of functions designed to help in the analysis of TDP sensors. Features includes dates and time conversion, weather data interpolation, daily maximum of tension analysis and calculations required to convert sap flow density data to sap flow rates at the tree and plot scale (For more information see : Granier (1985) <DOI:10.1051/forest:19850204> & Granier (1987) <DOI:10.1093/treephys/3.4.309>).

Compute arbitrary non-parametric bootstrap statistics on data in tidy data frames.

This package provides functions for performing time domain signal coding as used in Chesmore (2001) <doi:10.1016/S0003-682X(01)00009-3>, and related tasks. This package creates the standard S-matrix and A-matrix (with variable lag), has tools to convert coding matrices into distributed matrices, provides published codebooks and allows for extraction of code sequences.

This package implements proximal weighting estimators for the expectation of an arbitrarily transformed event time under dependent left truncation, with optional inverse probability of censoring weighting to handle right censoring. The methods leverage proxy variables to handle dependent left truncation in settings where dependence-inducing factors are not fully observed.

This package provides functions for assigning taxonomy to NCBI accession numbers and taxon IDs based on NCBI's accession2taxid and taxdump files. This package allows the user to download NCBI data dumps and create a local database for fast and local taxonomic assignment.

An RStudio add-in to visualize time series. Time series are searched in the global environment as data.frame objects with a column of type date and a column of type numeric. Interactive charts are produced using plotly package.

Processing and analysis of pathomics, omics and other medical datasets. tRigon serves as a toolbox for descriptive and statistical analysis, correlations, plotting and many other methods for exploratory analysis of high-dimensional datasets.

Built on top of the tibble package, tibbletime is an extension that allows for the creation of time aware tibbles. Some immediate advantages of this include: the ability to perform time-based subsetting on tibbles, quickly summarising and aggregating results by time periods, and creating columns that can be used as dplyr time-based groups.

Helper functions for MASCOTNUM / RT-UQ <https://uq.math.cnrs.fr/> algorithm template, for design of numerical experiments practice: algorithm template parser to support MASCOTNUM specification <https://github.com/MASCOTNUM/algorithms>, ask & tell decoupling injection (inspired by <https://search.r-project.org/CRAN/refmans/sensitivity/html/decoupling.html>) to use "crimped" algorithms (like uniroot(), optim(), ...) from outside R, basic template examples: Brent algorithm for 1 dim root finding and L-BFGS-B from base optim().

This package provides tools to import, clean, validate, and analyze freshwater quality data in Brazil. Implements water quality indices including the Water Quality Index ('WQI'/'IQA') using the weighted geometric mean following CETESB methodology, the Trophic State Index ('TSI'/'IET') after Carlson (1977) <doi:10.4319/lo.1977.22.2.0361> and Lamparelli (2004) <https://teses.usp.br/teses/disponiveis/41/41134/tde-20032006-075813/publico/TeseLamparelli2004.pdf>, and the National Sanitation Foundation Water Quality Index ('NSF WQI', Brown (1970)). The package also checks compliance with Brazilian standard CONAMA Resolution 357/2005 <https://conama.mma.gov.br/?id=450&option=com_sisconama&task=arquivo.download> including the legal frequency rule (Art. 15, 80% conformity over six or more samples per year), and provides seasonal analysis with regional flow-season calendars, pollutant load computation, exceedance probability estimation, IET visualization, and multivariate PCA tools for routine monitoring workflows. The example dataset ('wq_demo') is a real subset from INEMA monitoring data from a river in Bahia, Brazil (2020-2024).

Likelihood-based methods for model fitting and assessment, prediction and intervention analysis of count time series following generalized linear models are provided. Models with the identity and with the logarithmic link function are allowed. The conditional distribution can be Poisson or Negative Binomial.

TensorFlow Hub is a library for the publication, discovery, and consumption of reusable parts of machine learning models. A module is a self-contained piece of a TensorFlow graph, along with its weights and assets, that can be reused across different tasks in a process known as transfer learning. Transfer learning train a model with a smaller dataset, improve generalization, and speed up training.

This package provides a set of fast tidy functions for wrangling, completing and summarising date and date-time data. It combines tidyverse syntax with the efficiency of data.table and speed of collapse'.

This package provides a framework for high-dimensional mediation analysis using transfer learning. The main function TransHDM() integrates large-scale source data to improve the detection power of potential mediators in small-sample target studies. It addresses data heterogeneity via transfer regularization and debiased estimation while controlling the false discovery rate. The package also includes utilities for data generation (gen_simData_homo(), gen_simData_hetero()), baseline methods such as lasso() and dblasso(), sure independence screening via SIS(), and model diagnostics through source_detection(). The methodology is described in Pan et al. (2025) <doi:10.1093/bib/bbaf460>.

This package implements tic-tac-toe game to play on console, either with human or AI players. Various levels of AI players are trained through the Q-learning algorithm.

Record all tree-ring Shapefile of tree disk with GIS soft Qgis and interpolating model from high resolution tree disk image.

Estimates the parameters of a Transformed Ornstein-Uhlenbeck (TOU) stochastic model for adsorption data and also the parameters of the related pseudo-n-order (PNO) model, such as the maximum adsorption capacity (qe), the adsorption rate constant (kn) and the order of the model (n).

Helper functions for processing REDCap data in R. REDCap is a web-enabled application for building and managing surveys and databases developed at Vanderbilt University.

Extends the test-based Bayes factor (TBF) methodology to multinomial regression models and discrete time-to-event models with competing risks. The TBF methodology has been well developed and implemented for the generalised linear model [Held et al. (2015) <doi:10.1214/14-STS510>] and for the Cox model [Held et al. (2016) <doi:10.1002/sim.7089>].

Collection of functions that allow to export data frames to excel workbook.

Interactively gate points on a scatter plot. Interactively drawn gates are recorded and can be applied programmatically to reproduce results exactly. Programmatic gating is based on the package gatepoints by Wajid Jawaid.

Computes the product moments of the truncated multivariate normal distribution, particularly for cases involving patterned variance-covariance matrices. It also has the capability to calculate these moments with arbitrary positive-definite matrices, although performance may degrade for high-dimensional variables.

Uses the optimal test design approach by Birnbaum (1968, ISBN:9781593119348) and van der Linden (2018) <doi:10.1201/9781315117430> to construct fixed, adaptive, and parallel tests. Supports the following mixed-integer programming (MIP) solver packages: Rsymphony', highs', gurobi', lpSolve', and Rglpk'. The gurobi package is not available from CRAN; see <https://www.gurobi.com/downloads/>.

This package provides ggplot2 geoms for drawing treemaps.