Identifies clusters of individual longitudinal trajectories. In the spirit of Leffondre et al. (2004), the procedure involves identifying each trajectory to a point in the space of measures. In this context, a measure is a quantity meant to capture a certain characteristic feature of the trajectory. The points in the space of measures are then clustered using a version of the Spectral Clustering algorithm.

An open-access tool/framework that constitutes the core functions to analyze terrestrial water cycle data across various spatio-temporal scales.

Theme ggplot2', lattice', and base graphics based on a few choices, including foreground color, background color, accent color, and font family. Fonts that aren't available on the system, but are available via download on Google Fonts', can be automatically downloaded, cached, and registered for use with the showtext and ragg packages.

This package provides S3 vector types for functional data represented on grids, in spline bases, or via functional principal components. Supports arithmetic and summary methods, plotting, derivation, integration, smoothing, registration, and data import/export for these functional vectors. Includes data-wrangling tools for re-evaluation, subsetting, sub-assignment, zooming into sub-domains, and extracting functional features such as minima, maxima, and their locations. Enables joint analysis of functional and scalar variables by integrating functional vectors into standard data frames.

Tensor Composition Analysis (TCA) allows the deconvolution of two-dimensional data (features by observations) coming from a mixture of heterogeneous sources into a three-dimensional matrix of signals (features by observations by sources). The TCA framework further allows to test the features in the data for different statistical relations with an outcome of interest while modeling source-specific effects; particularly, it allows to look for statistical relations between source-specific signals and an outcome. For example, TCA can deconvolve bulk tissue-level DNA methylation data (methylation sites by individuals) into a three-dimensional tensor of cell-type-specific methylation levels for each individual (i.e. methylation sites by individuals by cell types) and it allows to detect cell-type-specific statistical relations (associations) with phenotypes. For more details see Rahmani et al. (2019) <DOI:10.1038/s41467-019-11052-9>.

Covers k-table control analysis using multivariate control charts for qualitative variables using fundamentals of multiple correspondence analysis and multiple factor analysis. The graphs can be shown in a flat or interactive way, in the same way all the outputs can be shown in an interactive shiny panel.

Generates a game of 2048 that can be played in the console. Supports grids of arbitrary sizes, undoing the last move, and resuming a game that was exited during the current session.

This package provides bindings to Tree-sitter', an incremental parsing system for programming tools. Tree-sitter builds concrete syntax trees for source files of any language, and can efficiently update those syntax trees as the source file is edited. It also includes a robust error recovery system that provides useful parse results even in the presence of syntax errors.

This package implements target trial emulation methods to apply randomized clinical trial design and analysis in an observational setting. Using marginal structural models, it can estimate intention-to-treat and per-protocol effects in emulated trials using electronic health records. A description and application of the method can be found in Danaei et al (2013) <doi:10.1177/0962280211403603>.

This package provides methods from tidyverse packages for SpatRaster and SpatVector objects created with terra', plus ggplot2 geoms and scales for plotting those objects. It is designed to let users manipulate spatial data with familiar dplyr and tidyr verbs before visualizing results with ggplot2'.

The main purpose of this package is to propose a rigorous framework to fairly compare trip distribution laws and models as described in Lenormand et al. (2016) <doi:10.1016/j.jtrangeo.2015.12.008>.

Pest monitoring is crucial, especially during the early season, to understand the distribution and the proliferation of the target pest. Raw count data from pest monitoring/traps can be coupled with derived environmental variables such as growing degree-day ('GDD') to get useful insights about the pest phenology. This package pulls temperature data from the Daymet application programming interface ('API', <https://daymet.ornl.gov>), or Open-Meteo ('API', <https://open-meteo.com/>) or manual user-supplied CSV file from the California Irrigation Management Information System ('CIMIS', <https://cimis.water.ca.gov>), for a user-specified time period and calculates cumulative growing degree-days. Users provide intended date range, pest of concern, and the geographic coordinates of the trap location to track pest emergence and phenology throughout the growing season.

This package provides functions for managing cashflows and interest rate curves.

Fits time-varying effect models (TVEM). These are a kind of application of varying-coefficient models in the context of longitudinal data, allowing the strength of linear, logistic, or Poisson regression relationships to change over time. These models are described further in Tan, Shiyko, Li, Li & Dierker (2012) <doi:10.1037/a0025814>. We thank Kaylee Litson, Patricia Berglund, Yajnaseni Chakraborti, and Hanjoo Kim for their valuable help with testing the package and the documentation. The development of this package was part of a research project supported by National Institutes of Health grants P50 DA039838 from the National Institute of Drug Abuse and 1R01 CA229542-01 from the National Cancer Institute and the NIH Office of Behavioral and Social Science Research. Content is solely the responsibility of the authors and does not necessarily represent the official views of the funding institutions mentioned above. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

This package provides functions for defining and conducting a time series prediction process including pre(post)processing, decomposition, modelling, prediction and accuracy assessment. The generated models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.

This package provides a plug-in for the text mining framework tm to support text mining in a distributed way. The package provides a convenient interface for handling distributed corpus objects based on distributed list objects.

Interface to TensorFlow Estimators <https://www.tensorflow.org/guide/estimator>, a high-level API that provides implementations of many different model types including linear models and deep neural networks.

The data that is generated from independent and consecutive GillespieSSA runs for a generic biochemical network is formatted as rows and constitutes an observation. The first column of each row is the computed timestep for each run. Subsequent columns are used for the number of molecules of each participating molecular species or "metabolite" of a generic biochemical network. In this way TemporalGSSA', is a wrapper for the R-package GillespieSSA'. The number of observations must be at least 30. This will generate data that is statistically significant. TemporalGSSA', transforms this raw data into a simulation time-dependent and metabolite-specific trial. Each such trial is defined as a set of linear models (n >= 30) between a timestep and number of molecules for a metabolite. Each linear model is characterized by coefficients such as the slope, arbitrary constant, etc. The user must enter an integer from 1-4. These specify the statistical modality utilized to compute a representative timestep (mean, median, random, all). These arguments are mandatory and will be checked. Whilst, the numeric indicator "0" indicates suitability, "1" prompts the user to revise and re-enter their data. An optional logical argument controls the output to the console with the default being "TRUE" (curtailed) whilst "FALSE" (verbose). The coefficients of each linear model are averaged (mean slope, mean constant) and are incorporated into a metabolite-specific linear regression model as the dependent variable. The independent variable is the representative timestep chosen previously. The generated data is the imputed molecule number for an in silico experiment with (n >=30) observations. These steps can be replicated with multiple set of observations. The generated "technical replicates" can be statistically evaluated (mean, standard deviation) and will constitute simulation time-dependent molecules for each metabolite. For SSA-generated datasets with varying simulation times TemporalGSSA will generate a simulation time-dependent trajectory for each metabolite of the biochemical network under study. The relevant publication with the mathematical derivation of the algorithm is (2022, Journal of Bioinformatics and Computational Biology) <doi:10.1142/S0219720022500184>. The algorithm has been deployed in the following publications (2021, Heliyon) <doi:10.1016/j.heliyon.2021.e07466> and (2016, Journal of Theoretical Biology) <doi:10.1016/j.jtbi.2016.07.002>.

This package provides a modular package for simulating phylogenetic trees and species traits jointly. Trees can be simulated using modular birth-death parameters (e.g. changing starting parameters or algorithm rules). Traits can be simulated in any way designed by the user. The growth of the tree and the traits can influence each other through modifiers objects providing rules for affecting each other. Finally, events can be created to modify both the tree and the traits under specific conditions ( Guillerme, 2024 <DOI:10.1111/2041-210X.14306>).

Node centrality measures for temporal networks. Available measures are temporal degree centrality, temporal closeness centrality and temporal betweenness centrality defined by Kim and Anderson (2012) <doi:10.1103/PhysRevE.85.026107>. Applying the REN algorithm by Hanke and Foraita (2017) <doi:10.1186/s12859-017-1677-x> when calculating the centrality measures keeps the computational running time linear in the number of graph snapshots. Further, all methods can run in parallel up to the number of nodes in the network.

It analyzes text to create a count of top n-grams, including tokens (one-word), bigrams(two-word), and trigrams (three-word), while removing all stopwords. It also plots the n-grams and corresponding counts as a bar chart.

The twelvedata REST service offers access to current and historical data on stocks, standard as well as digital crypto currencies, and other financial assets covering a wide variety of course and time spans. See <https://twelvedata.com/> for details, to create an account, and to request an API key for free-but-capped access to the data.

Calculate Expert Team on Climate Change Detection and Indices (ETCCDI) <-- (acronym) climate indices from daily or hourly temperature and precipitation data. Provides flexible data handling.

This package implements the TWO-Component Single Cell Model-Based Association Method (TWO-SIGMA) for gene-level differential expression (DE) analysis and DE-based gene set testing of single-cell RNA-sequencing datasets. See Van Buren et al. (2020) <doi:10.1002/gepi.22361> and Van Buren et al. (2021) <doi:10.1101/2021.01.24.427979>.