This package provides a framework for creating interactive figures for data exploration. All plots are automatically linked and support several kinds of interactive features, including selection, zooming, panning, and parameter manipulation. The figures can be interacted with either manually, using a mouse and a keyboard, or by running code from inside an active R session.

This package implements the method described at the UCLA Statistical Consulting site <https://stats.idre.ucla.edu/r/dae/ordinal-logistic-regression/> for checking if the proportional odds assumption holds for a cumulative logit model.

Uses provenance post-execution to help the user understand and debug their script by providing functions to look at intermediate steps and data values, their forwards and backwards lineage, and to understand the steps leading up to warning and error messages. provDebugR uses provenance produced by rdtLite (available on CRAN), stored in PROV-JSON format.

Calculates a comprehensive list of features from profile hidden Markov models (HMMs) of proteins. Adapts and ports features for use with HMMs instead of Position Specific Scoring Matrices, in order to take advantage of more accurate multiple sequence alignment by programs such as HHBlits Remmert et al. (2012) <DOI:10.1038/nmeth.1818> and HMMer Eddy (2011) <DOI:10.1371/journal.pcbi.1002195>. Features calculated by this package can be used for protein fold classification, protein structural class prediction, sub-cellular localization and protein-protein interaction, among other tasks. Some examples of features extracted are found in Song et al. (2018) <DOI:10.3390/app8010089>, Jin & Zhu (2021) <DOI:10.1155/2021/8629776>, Lyons et al. (2015) <DOI:10.1109/tnb.2015.2457906> and Saini et al. (2015) <DOI:10.1016/j.jtbi.2015.05.030>.

Use probability theory under the Bayesian framework for calculating the risk of selecting candidates in a multi-environment context. Contained are functions used to fit a Bayesian multi-environment model (based on the available presets), extract posterior values and maximum posterior values, compute the variance components, check the modelâ s convergence, and calculate the probabilities. For both across and within-environments scopes, the package computes the probability of superior performance and the pairwise probability of superior performance. Furthermore, the probability of superior stability and the pairwise probability of superior stability across environments is estimated. A joint probability of superior performance and stability is also provided.

Allows users to stem Persian texts for text analysis.

This function obtains a Random Number Generator (RNG) or collection of RNGs that replicate the required parameter(s) of a distribution for a time series of data. Consider the case of reproducing a time series data set of size 20 that uses an autoregressive (AR) model with phi = 0.8 and standard deviation equal to 1. When one checks the arima.sin() function's estimated parameters, it's possible that after a single trial or a few more, one won't find the precise parameters. This enables one to look for the ideal RNG setting for a simulation that will accurately duplicate the desired parameters.

Convert English letters to numbers or numbers to English letters as on a telephone keypad. When converting letters to numbers, a character vector is returned with "A," "B," or "C" becoming 2, "D," "E", or "F" becoming 3, etc. When converting numbers to letters, a character vector is returned with multiple elements (i.e., "2" becomes a vector of "A," "B," and "C").

This package provides a Shiny application for calculating phytosanitary inspection plans based on risks. It generates a diagram of pallets in a lot, highlights the units to be sampled, and documents them based on the selected sampling method (simple random or systematic sampling).

This package provides tools for statistical testing of correlation coefficients through robust permutation method and large sample approximation method. Tailored to different types of correlation coefficients including Pearson correlation coefficient, weighted Pearson correlation coefficient, Spearman correlation coefficient, and Lin's concordance correlation coefficient.The robust permutation test controls type I error under general scenarios when sample size is small and two variables are dependent but uncorrelated. The large sample approximation test generally controls type I error when the sample size is large (>200).

This package provides functions for data normalization and transformation in preprocessing stages. Implements scaling methods (min-max, Z-score, L2 normalization) and power transformations (Box-Cox, Yeo-Johnson). Box-Cox transformation is described in Box and Cox (1964) <doi:10.1111/j.2517-6161.1964.tb00553.x>, Yeo-Johnson transformation in Yeo and Johnson (2000) <doi:10.1093/biomet/87.4.954>.

This package provides various functions for retrieving and interpreting information from Pubmed via the API, <https://www.ncbi.nlm.nih.gov/home/develop/api/>.

This package provides an interface to the PubChem database via the PUG REST <https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest> and PUG View <https://pubchem.ncbi.nlm.nih.gov/docs/pug-view> services. This package allows users to automatically access chemical and biological data from PubChem', including compounds, substances, assays, and various other data types. Functions are available to retrieve data in different formats, perform searches, and access detailed annotations.

Assessment of habitat selection by means of the permutation-based combination of sign tests (Fattorini et al., 2014 <DOI:10.1007/s10651-013-0250-7>). To exemplify the application of this procedure, habitat selection is assessed for a population of European Brown Hares settled in central Italy.

Projection Pursuit (PP) algorithm for dimension reduction based on Gaussian Mixture Models (GMMs) for density estimation using Genetic Algorithms (GAs) to maximise an approximated negentropy index. For more details see Scrucca and Serafini (2019) <doi:10.1080/10618600.2019.1598871>.

Understanding the dynamics of potentially heterogeneous variables is important in statistical applications. This package provides tools for estimating the degree of heterogeneity across cross-sectional units in the panel data analysis. The methods are developed by Okui and Yanagi (2019) <doi:10.1016/j.jeconom.2019.04.036> and Okui and Yanagi (2020) <doi:10.1093/ectj/utz019>.

Conservation planning datasets for learning how to use the prioritizr package <https://CRAN.R-project.org/package=prioritizr>.

Set of tools for reading, writing and transforming spatial and seasonal data, model selection and specific statistical tests for ecologists. It includes functions to interpolate regular positions of points between landmarks, to discretize polylines into regular point positions, link distant observations to points and convert a bounding box in a spatial object. It also provides miscellaneous functions for field ecologists such as spatial statistics and inference on diversity indexes, writing data.frame with Chinese characters.

Matches cases to controls based on genotype principal components (PC). In order to produce better results, matches are based on the weighted distance of PCs where the weights are equal to the % variance explained by that PC. A weighted Mahalanobis distance metric (Kidd et al. (1987) <DOI:10.1016/0031-3203(87)90066-5>) is used to determine matches.

These are harmonized datasets produced as part of the Clinical Trials Network (CTN) protocol number 0094. This is a US National Institute of Drug Abuse (NIDA) funded project; to learn more go to <https://ctnlibrary.org/protocol/ctn0094/>. These are datasets which have the data harmonized from CTN-0027 (<https://ctnlibrary.org/protocol/ctn0027/>), CTN-0030 (<https://ctnlibrary.org/protocol/ctn0030/>), and CTN-0051 (<https://ctnlibrary.org/protocol/ctn0051/>).

Calculate and optimize dynamic performance ratings of association football teams competing in matches, in accordance with the method used in the research paper "Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries", by Constantinou and Fenton (2013) <doi:10.1515/jqas-2012-0036> This dynamic rating system has proven to provide superior results for predicting association football outcomes.

Computes predicted probabilities and marginal effects for binary & ordinal logit and probit, (partial) generalized ordinal & multinomial logit models estimated with the glm(), clm() (in the ordinal package), and vglm() (in the VGAM package) functions.

Provide estimation for particular cases of the power series cure rate model <doi:10.1080/03610918.2011.639971>. For the distribution of the concurrent causes the alternative models are the Poisson, logarithmic, negative binomial and Bernoulli (which are includes in the original work), the polylogarithm model <doi:10.1080/00949655.2018.1451850> and the Flory-Schulz <doi:10.3390/math10244643>. The estimation procedure is based on the EM algorithm discussed in <doi:10.1080/03610918.2016.1202276>. For the distribution of the time-to-event the alternative models are slash half-normal, Weibull, gamma and Birnbaum-Saunders distributions.

Bivariate additive categorical regression via penalized maximum likelihood. Under a multinomial framework, the method fits bivariate models where both responses are nominal, ordinal, or a mix of the two. Partial proportional odds models are supported, with flexible (non-)uniform association structures. Various logit types and parametrizations can be specified for both marginals and the association, including Daleâ s model. The association structure can be regularized using polynomial-type penalty terms. Additive effects are modeled using P-splines. Standard methods such as summary(), residuals(), and predict() are available.