Reads water network simulation data in Epanet text-based .inp and .rpt formats into R. Also reads results from Epanet-msx'. Provides basic summary information and plots. The README file has a quick introduction. See <https://www.epa.gov/water-research/epanet> for more information on the Epanet software for modeling hydraulic and water quality behavior of water piping systems.

Provide an optimal histogram, in the sense of probability density estimation and features detection, by means of multiscale variational inference. In other words, the resulting histogram servers as an optimal density estimator, and meanwhile recovers the features, such as increases or modes, with both false positive and false negative controls. Moreover, it provides a parsimonious representation in terms of the number of blocks, which simplifies data interpretation. The only assumption for the method is that data points are independent and identically distributed, so it applies to fairly general situations, including continuous distributions, discrete distributions, and mixtures of both. For details see Li, Munk, Sieling and Walther (2016) <arXiv:1612.07216>.

This package provides statistical tests and graphics for assessing tests of equivalence. Such tests have similarity as the alternative hypothesis instead of the null. Sample data sets are included.

Making available in R the complete set of programs accompanying S. Wellek's (2010) monograph Testing Statistical Hypotheses of Equivalence and Noninferiority. Second Edition (Chapman&Hall/CRC).

This package provides a collection of functions developed to support the tutorial on using Exploratory Structural Equiation Modeling (ESEM) (Asparouhov & Muthén, 2009) <https://www.statmodel.com/download/EFACFA810.pdf>) with Longitudinal Study of Australian Children (LSAC) dataset (Mohal et al., 2023) <doi:10.26193/QR4L6Q>. The package uses tidyverse','psych', lavaan','semPlot and provides additional functions to conduct ESEM. The package provides general functions to complete ESEM, including esem_c(), creation of target matrix (if it is used) make_target(), generation of the Confirmatory Factor Analysis (CFA) model syntax esem_cfa_syntax(). A sample data is provided - the package includes a sample data of the Strengths and Difficulties Questionnaire of the Longitudinal Study of Australian Children (SDQ LSAC) in sdq_lsac(). ESEM package vignette presents the tutorial demonstrating the use of ESEM on SDQ LSAC data.

This package provides fast dynamic-programming algorithms in C++'/'Rcpp (with pure R fallbacks) for the exact finite-sample distributions and p-values of Christoffersen (1998) independence (IND) and conditional-coverage (CC) VaR backtests. For completeness, it also provides the exact unconditional-coverage (UC) test following Kupiec (1995) via a closed-form binomial enumeration. See Christoffersen (1998) <doi:10.2307/2527341> and Kupiec (1995) <doi:10.3905/jod.1995.407942>.

Fit the hierarchical and non-hierarchical Bayesian measurement models proposed by Bullock, Imai, and Shapiro (2011) <DOI:10.1093/pan/mpr031> to analyze endorsement experiments. Endorsement experiments are a survey methodology for eliciting truthful responses to sensitive questions. This methodology is helpful when measuring support for socially sensitive political actors such as militant groups. The model is fitted with a Markov chain Monte Carlo algorithm and produces the output containing draws from the posterior distribution.

Runs the eDITH (environmental DNA Integrating Transport and Hydrology) model, which implements a mass balance of environmental DNA (eDNA) transport at a river network scale coupled with a species distribution model to obtain maps of species distribution. eDITH can work with both eDNA concentration (e.g., obtained via quantitative polymerase chain reaction) or metabarcoding (read count) data. Parameter estimation can be performed via Bayesian techniques (via the BayesianTools package) or optimization algorithms. An interface to the DHARMa package for posterior predictive checks is provided. See Carraro and Altermatt (2024) <doi:10.1111/2041-210X.14317> for a package introduction; Carraro et al. (2018) <doi:10.1073/pnas.1813843115> and Carraro et al. (2020) <doi:10.1038/s41467-020-17337-8> for methodological details.

This package provides a tool to draw samples from a Empirical Likelihood Bayesian posterior of parameters using Hamiltonian Monte Carlo.

Evaluate a function over a data frame of expressions.

This package implements event extraction and early classification of events in data streams in R. It has the functionality to generate 2-dimensional data streams with events belonging to 2 classes. These events can be extracted and features computed. The event features extracted from incomplete-events can be classified using a partial-observations-classifier (Kandanaarachchi et al. 2018) <doi:10.1371/journal.pone.0236331>.

This package provides functions for the computation of functional elastic shape means over sets of open planar curves. The package is particularly suitable for settings where these curves are only sparsely and irregularly observed. It uses a novel approach for elastic shape mean estimation, where planar curves are treated as complex functions and a full Procrustes mean is estimated from the corresponding smoothed Hermitian covariance surface. This is combined with the methods for elastic mean estimation proposed in Steyer, Stöcker, Greven (2022) <doi:10.1111/biom.13706>. See Stöcker et. al. (2022) <arXiv:2203.10522> for details.

Allows for forward-in-time simulation of epistatic networks with associated phenotypic output.

This package provides functions for treatment effect estimation, hypothesis testing, and future study design for settings where the surrogate is used in place of the primary outcome for individuals for whom the surrogate is valid, and the primary outcome is purposefully measured in the remaining patients. More details are available in: Knowlton, R., Parast, L. (2024) ``Efficient Testing Using Surrogate Information," Biometrical Journal, 67(6): e70086, <doi:10.1002/bimj.70086>. A tutorial for this package can be found at <https://www.laylaparast.com/etsi>.

Gene regulatory network constructed using combined score obtained from individual network inference method. The combined score measures the significance of edges in the ensemble network. Fisher's weighted method has been implemented to combine the outcomes of different methods based on the probability values. The combined score follows chi-square distribution with 2n degrees of freedom. <doi:10.22271/09746315.2020.v16.i3.1358>.

This package provides a comprehensive collection of datasets related to education, covering topics such as student performance, learning methods, test scores, absenteeism, and other educational metrics. This package serves as a resource for educational researchers, data analysts, and statisticians to explore and analyze data in the field of education.

This package provides tools for simulating draws from continuous time processes with well-defined exponential family random graph (ERGM) equilibria, i.e. ERGM generating processes (EGPs). A number of EGPs are supported, including the families identified in Butts (2023) <doi:10.1080/0022250X.2023.2180001>, as are functions for hazard calculation and timing calibration.

The new yield tables developed by the Northwest German Forest Research Institute (NW-FVA) provide a forest management tool for the five main commercial tree species oak, beech, spruce, Douglas-fir and pine for northwestern Germany. The new method applied for deriving yield tables combines measurements of growth and yield trials with growth simulations using a state-of-the-art single-tree growth simulator. By doing so, the new yield tables reflect the current increment level and the recommended graduated thinning from above is the underlying management concept. The yield tables are provided along with methods for deriving the site index and for interpolating between age and site indices and extrapolating beyond age and site index ranges. The inter-/extrapolations are performed traditionally by the rule of proportion or with a functional approach.

This cointegration based Time Delay Neural Network Model hybrid model allows the researcher to make use of the information extracted by the cointegrating vector as an input in the neural network model.

This package creates graphs of species associations (interactions) and ordination biplots from co-occurrence data by fitting discrete gaussian copula graphical models. Methods described in Popovic, GC., Hui, FKC., Warton, DI., (2018) <doi:10.1016/j.jmva.2017.12.002>.

Expectile and quantile regression of models with nonlinear effects e.g. spatial, random, ridge using least asymmetric weighed squares / absolutes as well as boosting; also supplies expectiles for common distributions.

Runs an evolutionary algorithm using the AlphaSimR machinery <doi:10.1093/g3journal/jkaa017> .

Extreme value theory, nonparametric kernel estimation, tail conditional probabilities, extreme conditional quantile, adaptive estimation, quantile regression, survival probabilities.

This package provides a collection of functions that allows for easy and consistent use of environment variables. This includes setting, checking, retrieving, transforming, and validating values stored in environment variables.