Dry seed germinates by imbibing water from soil where the physiological process of germination starts after sufficient water has been imbibed by the seed. The germination time of the seed is inversely proportion to the difference between soil water potential and the base seed water potential which is described by hydro time model (Bradford, 2002 <https://www.jstor.org/stable/4046371>). The parameters of the model like speed of germination, stress tolerance, uniformity of germination are unknown fixed values (Ghosh et al., 2026 <doi:10.1111/aab.70041>) which are to be estimated using statistical regression model where the validity of the adopted statistical model has been established theoretically. The package will help to estimate the tuning parameter for proportion of viable seeds along with standard error and p- values for inference.

This package implements three complementary pipelines for causal analysis on macroeconomic time series: (1) Error-Correction Models with Multivariate Adaptive Regression Splines (ECM-MARS), (2) Bayesian Structural Time Series (BSTS), and (3) Bayesian GLM with AR(1) errors validated with Leave-Future-Out (LFO). Heavy backends (Stan) are optional and never used in examples or tests.

Exploratory principal component analysis for large-scale dataset, including sparse principal component analysis and sparse matrix approximation.

This package provides simple, fast, and stable functions to fit the normal means model using empirical Bayes. For available models and details, see function ebnm(). Our JSS article, Willwerscheid, Carbonetto, and Stephens (2025) <doi:10.18637/jss.v114.i03>, provides a detailed introduction to the package.

The equality of a large number k of densities is tested by measuring the L2 distance between the corresponding kernel density estimators and the one based on the pooled sample. The test even works for sample sizes as small as 2.

Event dataset repository including both real-life and artificial event logs. They can be used in combination with functionalities provided by the bupaR packages. Janssenswillen et al. (2020) <http://ceur-ws.org/Vol-2703/paperTD7.pdf>.

Enables launching a series of simulations of a computer code from the R session, and to retrieve the simulation outputs in an appropriate format for post-processing treatments. Five sequential sampling schemes and three coupled-to-MCMC schemes are implemented.

This package implements the Exploratory Graph Analysis (EGA) framework for dimensionality and psychometric assessment. EGA estimates the number of dimensions in psychological data using network estimation methods and community detection algorithms. A bootstrap method is provided to assess the stability of dimensions and items. Fit is evaluated using the Entropy Fit family of indices. Unique Variable Analysis evaluates the extent to which items are locally dependent (or redundant). Network loadings provide similar information to factor loadings and can be used to compute network scores. A bootstrap and permutation approach are available to assess configural and metric invariance. Hierarchical structures can be detected using Hierarchical EGA. Time series and intensive longitudinal data can be analyzed using Dynamic EGA, supporting individual, group, and population level assessments.

Predicts enrollment and events at the design or analysis stage using specified enrollment and time-to-event models through simulations.

This package provides functions for the simulation and the nonparametric estimation of elliptical distributions, meta-elliptical copulas and trans-elliptical distributions, following the article Derumigny and Fermanian (2022) <doi:10.1016/j.jmva.2022.104962>.

Analysis of dichotomous and polytomous response data using the explanatory item response modeling framework, as described in Bulut, Gorgun, & Yildirim-Erbasli (2021) <doi:10.3390/psych3030023>, Stanke & Bulut (2019) <doi:10.21449/ijate.515085>, and De Boeck & Wilson (2004) <doi:10.1007/978-1-4757-3990-9>. Generalized linear mixed modeling is used for estimating the effects of item-related and person-related variables on dichotomous and polytomous item responses.

Extends the ergm.multi packages from the Statnet suite to fit (temporal) exponential-family random graph models for signed networks. The framework models positive and negative ties as interdependent, which allows estimation and testing of structural balance theory. The package also includes options for descriptive summaries, visualization, and simulation of signed networks. See Krivitsky, Koehly, and Marcum (2020) <doi:10.1007/s11336-020-09720-7> and Fritz, C., Mehrl, M., Thurner, P. W., & Kauermann, G. (2025) <doi:10.1017/pan.2024.21>.

An implementation of Bayesian hierarchical models for faecal egg count data to assess anthelmintic efficacy. Bayesian inference is done via MCMC sampling using Stan <https://mc-stan.org/>.

Runs a series of configurable tests against a user's compute environment. This can be used for checking that things like a specific directory or an environment variable is available before you start an analysis. Alternatively, you can use the package's situation report when filing error reports with your compute infrastructure.

Infer the adjacency matrix of a network from time course data using an empirical Bayes estimation procedure based on Dynamic Bayesian Networks.

This package provides functions to align curves and to compute mean curves based on the elastic distance defined in the square-root-velocity framework. For more details on this framework see Srivastava and Klassen (2016, <doi:10.1007/978-1-4939-4020-2>). For more theoretical details on our methods and algorithms see Steyer et al. (2023, <doi:10.1111/biom.13706>) and Steyer et al. (2023, <arXiv:2305.02075>).

Ever read or wrote source files containing sectioning comments? If these comments are markdown style section comments, you can excerpt them and set a table of contents using the python package excerpts (<https://pypi.org/project/excerpts/>).

Biotracers and stomach content analyses are combined in a Bayesian hierarchical model to estimate a probabilistic topology matrix (all trophic link probabilities) and a diet matrix (all diet proportions). The package relies on the JAGS software and the jagsUI package to run a Markov chain Monte Carlo approximation of the different variables.

EB-PRS is a novel method that leverages information for effect sizes across all the markers to improve the prediction accuracy. No parameter tuning is needed in the method, and no external information is needed. This R-package provides the calculation of polygenic risk scores from the given training summary statistics and testing data. We can use EB-PRS to extract main information, estimate Empirical Bayes parameters, derive polygenic risk scores for each individual in testing data, and evaluate the PRS according to AUC and predictive r2. See Song et al. (2020) <doi:10.1371/journal.pcbi.1007565> for a detailed presentation of the method.

This package provides tools to analyze the embryo growth and the sexualisation thermal reaction norms. See <doi:10.7717/peerj.8451> for tsd functions; see <doi:10.1016/j.jtherbio.2014.08.005> for thermal reaction norm of embryo growth.

This package provides functions for computing critical values and implementing the one-sided/two-sided EL tests.

Provide the EMU Speech Database Management System (EMU-SDMS) with database management, data extraction, data preparation and data visualization facilities. See <https://ips-lmu.github.io/The-EMU-SDMS-Manual/> for more details.

This package provides a wrapper of different methods from Linear Algebra for the equations introduced in The Atlas of Economic Complexity and related literature. This package provides standard matrix and graph output that can be used seamlessly with other packages. See <doi:10.21105/joss.01866> for a summary of these methods and its evolution in literature.

Forecasting univariate time series with different decomposition based time delay neural network models. For method details see Yu L, Wang S, Lai KK (2008). <doi:10.1016/j.eneco.2008.05.003>.