Interaction and analysis of multiple response data, along with other tools for analysing these types of data including missing value analysis and calculation of standard errors for a range of covariance matrix results (proportions, multinomial, independent samples, and multiple response).

This package provides access to low-level operating system mechanisms for performing atomic operations on shared data structures. Mutexes provide shared and exclusive locks. Semaphores act as counters. Message queues move text strings from one process to another. All these interprocess communication (IPC) tools can optionally block with or without a timeout. Implemented using the cross-platform boost C++ library <https://www.boost.org/doc/libs/release/libs/interprocess/>.

Based on large margin principle, this package performs feature selection methods: "IM4E"(Iterative Margin-Maximization under Max-Min Entropy Algorithm); "Immigrate"(Iterative Max-Min Entropy Margin-Maximization with Interaction Terms Algorithm); "BIM"(Boosted version of IMMIGRATE algorithm); "Simba"(Iterative Search Margin Based Algorithm); "LFE"(Local Feature Extraction Algorithm). This package also performs prediction for the above feature selection methods.

Reverse engineer a regular expression pattern for the characters contained in an R object. Individual characters can be categorised into digits, letters, punctuation or spaces and encoded into run-lengths. This can be used to summarise the structure of a dataset or identify non-standard entries. Many non-character inputs such as numeric vectors and data frames are supported.

High resolution mass spectrometry yields often large data sets of spectra from compounds which are not present in available libraries. These spectra need to be annotated and interpreted. InterpretMSSpectrum provides a set of functions to perform such tasks for Electrospray-Ionization and Atmospheric-Pressure-Chemical-Ionization derived data in positive and negative ionization mode.

For environmental chemists, ecologists, researchers and agricultural scientists to understand the dissipation kinetics, calculate the half-life periods and rate constants of compounds, pesticides, contaminants in different matrices.

R interface to access the web services of the ICES Stock Assessment Graphs database <https://sg.ices.dk>.

This package provides a wrapper around the same API <https://app.americansocceranalysis.com/api/v1/__docs__/> that powers the American Soccer Analysis app.

This package contains data on Post-Secondary Institution Statistics in 2020 <https://nces.ed.gov/ipeds/use-the-data>. The package allows easy access to a wide variety of information regarding Post-secondary Institutions, its students, faculty, and their demographics, financial aid, educational and recreational offerings, and completions. This package can be used by students, college counselors, or involved parents interested in pursuing higher education, considering their options, and securing admission into their school of choice.

Mining informative genes with certain biological meanings are important for clinical diagnosis of disease and discovery of disease mechanisms in plants and animals. This process involves identification of relevant genes and removal of redundant genes as much as possible from a whole gene set. This package selects the informative genes related to a specific trait using gene expression dataset. These trait specific genes are considered as informative genes. This package returns the informative gene set from the high dimensional gene expression data using a combination of methods SVM and MRMR (for feature selection) with bootstrapping procedure.

Paquete creado con el fin de facilitar el cálculo y distribución del à ndice Socio Material Territorial (ISMT), elaborado por el Observatorio de Ciudades UC. La metodologà a completa está disponible en "ISMT" (<https://ideocuc-ocuc.hub.arcgis.com/datasets/6ed956450cfc4293b7d90df3ce3474e4/about>) [Observatorio de Ciudades UC (2019)]. || Package created to facilitate the calculation and distribution of the Socio-Material Territorial Index by Observatorio de Ciudades UC. The full methodology is available at "ISMT" (<https://ideocuc-ocuc.hub.arcgis.com/datasets/6ed956450cfc4293b7d90df3ce3474e4/about>) [Observatorio de Ciudades UC (2019)].

This package provides a suite for identifying causal models using relative concordances and identifying causal polymorphisms in case-control genetic association data, especially with large controls re-sequenced data.

This package provides user tokens for ICES web services that require authentication and authorization. Web services covered by this package are ICES VMS database, the ICES DATSU web services, and the ICES SharePoint site <https://www.ices.dk/data/tools/Pages/WebServices.aspx>.

Calculates event rates and compares means and variances of groups of interval data corrected for missed arrival observations.

Graphical visualization tools for analyzing the data produced by irace'. The iraceplot package enables users to analyze the performance and the parameter space data sampled by the configuration during the search process. It provides a set of functions that generate different plots to visualize the configurations sampled during the execution of irace and their performance. The functions just require the log file generated by irace and, in some cases, they can be used with user-provided data.

Estimates the probability of informed trading (PIN) initially introduced by Easley et. al. (1996) <doi:10.1111/j.1540-6261.1996.tb04074.x> . Contribution of the package is that it uses likelihood factorizations of Easley et. al. (2010) <doi:10.1017/S0022109010000074> (EHO factorization) and Lin and Ke (2011) <doi:10.1016/j.finmar.2011.03.001> (LK factorization). Moreover, the package uses different estimation algorithms. Specifically, the grid-search algorithm proposed by Yan and Zhang (2012) <doi:10.1016/j.jbankfin.2011.08.003> , hierarchical agglomerative clustering approach proposed by Gan et. al. (2015) <doi:10.1080/14697688.2015.1023336> and later extended by Ersan and Alici (2016) <doi:10.1016/j.intfin.2016.04.001> .

Facilitates spatial and general latent Gaussian modeling using integrated nested Laplace approximation via the INLA package (<https://www.r-inla.org>). Additionally, extends the GAM-like model class to more general nonlinear predictor expressions, and implements a log Gaussian Cox process likelihood for modeling univariate and spatial point processes based on ecological survey data. Model components are specified with general inputs and mapping methods to the latent variables, and the predictors are specified via general R expressions, with separate expressions for each observation likelihood model in multi-likelihood models. A prediction method based on fast Monte Carlo sampling allows posterior prediction of general expressions of the latent variables. Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019) <doi:10.1111/2041-210X.13168>.

Calculates various intraclass correlation coefficients used to quantify inter-rater and intra-rater reliability. The assumption here is that the raters produced quantitative ratings. Most of the statistical procedures implemented in this package are described in details in Gwet, K.L. (2014, ISBN:978-0970806284): "Handbook of Inter-Rater Reliability," 4th edition, Advanced Analytics, LLC.

This package provides a user-friendly interface, using Shiny, to analyse glucose-stimulated insulin secretion (GSIS) assays in pancreatic beta cells or islets. The package allows the user to import several sets of experiments from different spreadsheets and to perform subsequent steps: summarise in a tidy format, visualise data quality and compare experimental conditions without omitting to account for technical confounders such as the date of the experiment or the technician. Together, insane is a comprehensive method that optimises pre-processing and analyses of GSIS experiments in a friendly-user interface. The Shiny App was initially designed for EndoC-betaH1 cell line following method described in Ndiaye et al., 2017 (<doi:10.1016/j.molmet.2017.03.011>).

Allows the simulation of the recruitment and both the event and treatment phase of a clinical trial. Based on these simulations, the timing of interim analyses can be assessed.

An implementation of the Canny Edge Detector for detecting edges in images. The package provides an interface to the algorithm available at <https://github.com/Neseb/canny>.

Utilities to work with data from the Internal Displacement Monitoring Centre (IDMC) (<https://www.internal-displacement.org/>), with convenient functions for loading events data from the IDMC API and transforming events data to daily displacement estimates.

Fitting and validation of machine learning algorithms for volume prediction of trees, currently for conifer trees based on diameter at breast height and height as explanatory variables.

This package provides functions to make inference about the standardized mortality ratio (SMR) when evaluating the effect of a screening program. The package is based on methods described in Sasieni (2003) <doi: 10.1097/00001648-200301000-00026> and Talbot et al. (2011) <doi: 10.1002/sim.4334>.