Assign and listen to keyboard shortcuts in shiny using the Mousetrap Javascript library.

Computes Khattree-Bahuguna's univariate and multivariate skewness, principal-component-based Khattree-Bahuguna's multivariate skewness. It also provides several measures of univariate or multivariate skewnesses including, Pearsonâ s coefficient of skewness, Bowleyâ s univariate skewness and Mardia's multivariate skewness. See Khattree, R. and Bahuguna, M. (2019) <doi: 10.1007/s41060-018-0106-1>.

Estimation of Latent Order Logistic (LOLOG) Models for Networks. LOLOGs are a flexible and fully general class of statistical graph models. This package provides functions for performing MOM, GMM and variational inference. Visual diagnostics and goodness of fit metrics are provided. See Fellows (2018) <doi:10.48550/arXiv.1804.04583> for a detailed description of the methods.

Various efficient and robust bootstrap methods are implemented for linear models with least squares estimation. Functions within this package allow users to create bootstrap sampling distributions for model parameters, test hypotheses about parameters, and visualize the bootstrap sampling or null distributions. Methods implemented for linear models include the wild bootstrap by Wu (1986) <doi:10.1214/aos/1176350142>, the residual and paired bootstraps by Efron (1979, ISBN:978-1-4612-4380-9), the delete-1 jackknife by Quenouille (1956) <doi:10.2307/2332914>, and the Bayesian bootstrap by Rubin (1981) <doi:10.1214/aos/1176345338>.

Compute and visualize using the visNetwork package all the bivariate correlations of a dataframe. Several and different types of correlation coefficients (Pearson's r, Spearman's rho, Kendall's tau, distance correlation, maximal information coefficient and equal-freq discretization-based maximal normalized mutual information) are used according to the variable couple type (quantitative vs categorical, quantitative vs quantitative, categorical vs categorical).

Linear Liu regression coefficient's estimation and testing with different Liu related measures such as MSE, R-squared etc. REFERENCES i. Akdeniz and Kaciranlar (1995) <doi:10.1080/03610929508831585> ii. Druilhet and Mom (2008) <doi:10.1016/j.jmva.2006.06.011> iii. Imdadullah, Aslam, and Saima (2017) iv. Liu (1993) <doi:10.1080/03610929308831027> v. Liu (2001) <doi:10.1016/j.jspi.2010.05.030>.

This package provides a single analysis path that includes distance-based ordination, global tests of any effect of the microbiome, and tests of the effects of individual taxa with false-discovery-rate (FDR) control. It accommodates both continuous and discrete covariates as well as interaction terms to be tested either singly or in combination, allows for adjustment of confounding covariates, and uses permutation-based p-values that can control for sample correlations. It can be applied to transformed data, and an omnibus test can combine results from analyses conducted on different transformation scales. It can also be used for testing presence-absence associations based on infinite number of rarefaction replicates, testing mediation effects of the microbiome, analyzing censored time-to-event outcomes, and for compositional analysis by fitting linear models to centered-log-ratio taxa count data.

Four measures of linkage disequilibrium are provided: the usual r^2 measure, the r^2_S measure (r^2 corrected by the structure sample), the r^2_V (r^2 corrected by the relatedness of genotyped individuals), the r^2_VS measure (r^2 corrected by both the relatedness of genotyped individuals and the structure of the sample).

This package provides a framework to load text and excel files through a shiny graphical interface. It allows renaming, transforming, ordering and removing variables. It includes basic exploratory methods such as the mean, median, mode, normality test, histogram and correlation.

Managing and exploring parameter estimation results derived from Maximum Likelihood Estimation (MLE) using the likelihood package. It provides functions for organizing, visualizing, and summarizing MLE outcomes, streamlining statistical analysis workflows. By improving interpretation and facilitating model evaluation, it helps users gain deeper insights into parameter estimation and model fitting, making MLE result exploration more efficient and accessible. See Goffe et al. (1994) <doi:10.1016/0304-4076(94)90038-8> for details on MLE, and Canham and Uriarte (2006) <doi:10.1890/04-0657> for application of MLE using likelihood'.

Computes the probability density function, the cumulative distribution function, the hazard rate function, the quantile function and random generation for Lindley Power Series distributions, see Nadarajah and Si (2018) <doi:10.1007/s13171-018-0150-x>.

Data sets on various litter types like beach litter, riverain litter, floating litter, and seafloor litter are rapidly growing. This package offers a simple user interface to analyse these litter data in a consistent and reproducible way. It also provides functions to facilitate several kinds of litter analysis, e.g., trend analysis, power analysis, and baseline analysis. Under the hood, these functions are also used by the user interface. See Schulz et al. (2019) <doi:10.1016/j.envpol.2019.02.030> for details. MS-Windows users are advised to run litteR in RStudio'. See our vignette: Installation manual for RStudio and litteR'.

Maximum likelihood estimation and likelihood ratio test are essential for modern statistics. This package supports in calculating likelihood based inference. Reference: Pawitan Y. (2001, ISBN:0-19-850765-8).

This package implements the letter value boxplot which extends the standard boxplot to deal with both larger and smaller number of data points by dynamically selecting the appropriate number of letter values to display.

Allow to visualise data on the NCBI phylogenetic tree as presented in Lifemap <https://lifemap.cnrs.fr/>. It takes as input a dataframe with at least a "taxid" column containing NCBI format TaxIds and allows to draw multiple layers with different visualisation tools.

Affords an alternative, vector-based syntax to lavaan', as well as other convenience functions such as naming paths and defining indirect links automatically, in addition to convenience formatting optimized for a publication and script sharing workflow.

This package performs likelihood-based inference for stationary time series extremes. The general approach follows Fawcett and Walshaw (2012) <doi:10.1002/env.2133>. Marginal extreme value inferences are adjusted for cluster dependence in the data using the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>, producing an adjusted log-likelihood for the model parameters. A log-likelihood for the extremal index is produced using the K-gaps model of Suveges and Davison (2010) <doi:10.1214/09-AOAS292>. These log-likelihoods are combined to make inferences about extreme values. Both maximum likelihood and Bayesian approaches are available.

Estimate the mean of a Gaussian vector, by choosing among a large collection of estimators, following the method developed by Y. Baraud, C. Giraud and S. Huet (2014) <doi:10.1214/13-AIHP539>. In particular it solves the problem of variable selection by choosing the best predictor among predictors emanating from different methods as lasso, elastic-net, adaptive lasso, pls, randomForest. Moreover, it can be applied for choosing the tuning parameter in a Gauss-lasso procedure.

Data sets exemplifying statistical methods, and some facilitatory utility functions used in ``Analyzing Linguistic Data: A practical introduction to statistics using R'', Cambridge University Press, 2008.

Due to lack of proper inference procedure and software, the ordinary linear regression model is seldom used in practice for the analysis of right censored data. This paper presents an S-Plus/R program that implements a recently developed inference procedure (Jin, Lin and Ying, 2006) <doi:10.1093/biomet/93.1.147> for the accelerated failure time model based on the least-squares principle.

This package provides a set of functions that allow stationary analysis and locally stationary time series analysis.

The primary purpose of lavaan.mi is to extend the functionality of the R package lavaan', which implements structural equation modeling (SEM). When incomplete data have been multiply imputed, the imputed data sets can be analyzed by lavaan using complete-data estimation methods, but results must be pooled across imputations (Rubin, 1987, <doi:10.1002/9780470316696>). The lavaan.mi package automates the pooling of point and standard-error estimates, as well as a variety of test statistics, using a familiar interface that allows users to fit an SEM to multiple imputations as they would to a single data set using the lavaan package.

This package provides a framework for clustering longitudinal datasets in a standardized way. The package provides an interface to existing R packages for clustering longitudinal univariate trajectories, facilitating reproducible and transparent analyses. Additionally, standard tools are provided to support cluster analyses, including repeated estimation, model validation, and model assessment. The interface enables users to compare results between methods, and to implement and evaluate new methods with ease. The akmedoids package is available from <https://github.com/MAnalytics/akmedoids>.

Connect to the Less Annoying CRM API with ease to get your crm data in a clean and tidy format. Less Annoying CRM is a simple CRM built for small businesses, more information is available on their website <https://www.lessannoyingcrm.com/>.