This package provides a collection of functions for processing raw data from Stream Temperature, Intermittency, and Conductivity (STIC) loggers. STICr (pronounced "sticker") includes functions for tidying, calibrating, classifying, and doing quality checks on data from STIC sensors. Some package functionality is described in Wheeler/Zipper et al. (2023) <doi:10.31223/X5636K>.

R Codes and Datasets for Stroup, W. W. (2012). Generalized Linear Mixed Models Modern Concepts, Methods and Applications, CRC Press.

Enables the creation of Chain Event Graphs over spatial areas, with an optional Shiny user interface. Allows users to fully customise both the structure and underlying model of the Chain Event Graph, offering a high degree of flexibility for tailored analyses. For more details on Chain Event Graphs, see Freeman, G., & Smith, J. Q. (2011) <doi:10.1016/j.jmva.2011.03.008>, Collazo R. A., Görgen C. and Smith J. Q. (2018, ISBN:9781498729604) and Barclay, L. M., Hutton, J. L., & Smith, J. Q. (2014) <doi:10.1214/13-BA843>.

High dimensional survival data analysis with Markov Chain Monte Carlo(MCMC). Currently supports frailty data analysis. Allows for Weibull and Exponential distribution. Includes function for interval censored data.

Spectra viewer, organizer, data preparation and property blocks from within R or stand-alone. Binary (application) part is installed separately using spnInstallApp() from spectrino package.

This package performs parametric and non-parametric estimation and simulation for multi-state discrete-time semi-Markov processes. For the parametric estimation, several discrete distributions are considered for the sojourn times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial. The non-parametric estimation concerns the sojourn time distributions, where no assumptions are done on the shape of distributions. Moreover, the estimation can be done on the basis of one or several sample paths, with or without censoring at the beginning or/and at the end of the sample paths. Reliability indicators such as reliability, maintainability, availability, BMP-failure rate, RG-failure rate, mean time to failure and mean time to repair are available as well. The implemented methods are described in Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>, Barbu, V.S., Limnios, N. (2008) <doi:10.1080/10485250701261913> and Trevezas, S., Limnios, N. (2011) <doi:10.1080/10485252.2011.555543>. Estimation and simulation of discrete-time k-th order Markov chains are also considered.

The most important function of the R package is the genetic effects analysis of small RNA in hybrid plants via two methods, and at the same time, it provides various forms of graph related to data characteristics and expression analysis. In terms of two classification methods, one is the calculation of the additive (a) and dominant (d), the other is the evaluation of expression level dominance by comparing the total expression of the small RNA in progeny with the expression level in the parent species.

Allows the creation and manipulation of C++ std::vector's in R.

This package provides a pair of functions that allow for the generation and tracking of coordinate data clouds without a time dimension, primarily for use in super-resolution plant micro-tubule image segmentation.

RCON(V, E) models are a kind of restriction of the Gaussian Graphical Models defined by a set of equality constraints on the entries of the concentration matrix. sglasso package implements the structured graphical lasso (sglasso) estimator proposed in Abbruzzo et al. (2014) for the weighted l1-penalized RCON(V, E) model. Two cyclic coordinate algorithms are implemented to compute the sglasso estimator, i.e. a cyclic coordinate minimization (CCM) and a cyclic coordinate descent (CCD) algorithm.

Unofficial client for Sentry <https://sentry.io>, a self-hosted or cloud-based error-monitoring service. It will inform about errors in real-time, and includes integration with the Plumber package.

In the past decade, genome-scale metabolic reconstructions have widely been used to comprehend the systems biology of metabolic pathways within an organism. Different GSMs are constructed using various techniques that require distinct steps, but the input data, information conversion and software tools are neither concisely defined nor mathematically or programmatically formulated in a context-specific manner.The tool that quantitatively and qualitatively specifies each reconstruction steps and can generate a template list of reconstruction steps dynamically selected from a reconstruction step reservoir, constructed based on all available published papers.

This package provides a fast implementation of the SWAG algorithm for Generalized Linear Models which allows to perform a meta-learning procedure that combines screening and wrapper methods to find a set of extremely low-dimensional attribute combinations. The package then performs test on the network of selected models to identify the variables that are highly predictive by using entropy-based network measures.

Easy-to-use interface to X-13-ARIMA-SEATS, the seasonal adjustment software by the US Census Bureau. It offers full access to almost all options and outputs of X-13, including X-11 and SEATS, automatic ARIMA model search, outlier detection and support for user defined holiday variables, such as Chinese New Year or Indian Diwali. A graphical user interface can be used through the seasonalview package. Uses the X-13-binaries from the x13binary package.

Implementation of the direct Monte Carlo approach of Zellner and Ando (2010) <doi:10.1016/j.jeconom.2010.04.005> to sample from posterior of Seemingly Unrelated Regression (SUR) models. In addition, a Gibbs sampler is implemented that allows the user to analyze SUR models using the power prior.

Capture screenshots in Shiny applications. Screenshots can either be of the entire viewable page, or a specific section of the page. The captured image is automatically downloaded as a PNG image, or it can also be saved on the server. Powered by the html2canvas JavaScript library.

Web application using shiny for the SSD (Species Sensitivity Distribution) module of the MOSAIC (MOdeling and StAtistical tools for ecotoxICology) platform. It estimates the Hazardous Concentration for x% of the species (HCx) from toxicity values that can be censored and provides various plotting options for a better understanding of the results. See our companion paper Kon Kam King et al. (2014) <doi:10.48550/arXiv.1311.5772>.

Calibration of thresholds of control charts such as CUSUM charts based on past data, taking estimation error into account.

This package provides functions to estimate the proportion of treatment effect on a censored primary outcome that is explained by the treatment effect on a censored surrogate outcome/event. All methods are described in detail in Parast, Tian, Cai (2020) "Assessing the Value of a Censored Surrogate Outcome" <doi:10.1007/s10985-019-09473-1>. The main functions are (1) R.q.event() which calculates the proportion of the treatment effect (the difference in restricted mean survival time at time t) explained by surrogate outcome information observed up to a selected landmark time, (2) R.t.estimate() which calculates the proportion of the treatment effect explained by primary outcome information only observed up to a selected landmark time, and (3) IV.event() which calculates the incremental value of the surrogate outcome information.

Simultaneously infers state-dependent diversification across two or more states of a single or multiple traits while accounting for the role of a possible concealed trait. See Herrera-Alsina et al. (2019) <doi:10.1093/sysbio/syy057>.

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.

Contemporary software commonly used to design stated preference experiments are expensive and the code is closed source. This is a free software package with an easy to use interface to make flexible stated preference experimental designs using state-of-the-art methods. For an overview of stated choice experimental design theory, see e.g., Rose, J. M. & Bliemer, M. C. J. (2014) in Hess S. & Daly. A. <doi:10.4337/9781781003152>. The package website can be accessed at <https://spdesign.edsandorf.me>. We acknowledge funding from the European Unionâ s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant INSPiRE (Grant agreement ID: 793163).

This package provides a tool to plot data with a large sample size using shiny and plotly'. Relatively small samples are obtained from the original data using a specific algorithm. The samples are updated according to a user-defined x range. Jonas Van Der Donckt, Jeroen Van Der Donckt, Emiel Deprost (2022) <https://github.com/predict-idlab/plotly-resampler>.

This package provides fitting functions and other tools for decision confidence and metacognition researchers, including meta-d'/d', often considered to be the gold standard to measure metacognitive efficiency, and information-theoretic measures of metacognition. Also allows to fit and compare several static models of decision making and confidence.