Convert text (and text in R objects) to Mocking SpongeBob case <https://knowyourmeme.com/memes/mocking-spongebob> and show them off in fun ways. CoNVErT TexT (AnD TeXt In r ObJeCtS) To MOCkINg SpoNgebOb CAsE <https://knowyourmeme.com/memes/mocking-spongebob> aND shOw tHem OFf IN Fun WayS.

Implement the algorithm provided in scan for estimating the transmission route on railway network using passenger volume. It is a generalization of the scan statistic approach for railway network to identify the hot railway route for transmitting infectious diseases.

Fits time trend models for routine disease surveillance tasks and returns probability distributions for a variety of quantities of interest, including age-standardized rates, period and cumulative percent change, and measures of health inequality. The models are appropriate for count data such as disease incidence and mortality data, employing a Poisson or binomial likelihood and the first-difference (random-walk) prior for unknown risk. Optionally add a covariance matrix for multiple, correlated time series models. Inference is completed using Markov chain Monte Carlo via the Stan modeling language. References: Donegan, Hughes, and Lee (2022) <doi:10.2196/34589>; Stan Development Team (2021) <https://mc-stan.org>; Theil (1972, ISBN:0-444-10378-3).

Apache Drill is a low-latency distributed query engine designed to enable data exploration and analysis on both relational and non-relational data stores, scaling to petabytes of data. Methods are provided that enable working with Apache Drill instances via the REST API, DBI methods and using dplyr'/'dbplyr idioms. Helper functions are included to facilitate using official Drill Docker images/containers.

This package provides a sparklyr extension that enables reading and writing TensorFlow TFRecord files via Apache Spark'.

Calculate text polarity sentiment at the sentence level and optionally aggregate by rows or grouping variable(s).

This is the implementation of the novel structural Bayesian information criterion by Zhou, 2020 (under review). In this method, the prior structure is modeled and incorporated into the Bayesian information criterion framework. Additionally, we also provide the implementation of a two-step algorithm to generate the candidate model pool.

This package implements sparse generalized factor models (sparseGFM) for dimension reduction and variable selection in high-dimensional data with automatic adaptation to weak factor scenarios. The package supports multiple data types (continuous, count, binary) through generalized linear model frameworks and handles missing values automatically. It provides 12 different penalty functions including Least Absolute Shrinkage and Selection Operator (Lasso), adaptive Lasso, Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP), group Lasso, and their adaptive versions for inducing row-wise sparsity in factor loadings. Key features include cross-validation for regularization parameter selection using Sparsity Information Criterion (SIC), automatic determination of the number of factors via multiple information criteria, and specialized algorithms for row-sparse loading structures. The methodology employs alternating minimization with Singular Value Decomposition (SVD)-based identifiability constraints and is particularly effective for high-dimensional applications in genomics, economics, and social sciences where interpretable sparse dimension reduction is crucial. For penalty functions, see Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>, Fan and Li (2001) <doi:10.1198/016214501753382273>, and Zhang (2010) <doi:10.1214/09-AOS729>.

Estimate necessary sample sizes for comparing the location of data from two groups or categories when the distribution of the data is skewed. The package offers a non-parametric method for a Wilcoxon Mann-Whitney test of location shift as well as methods for several generalized linear models, for instance, Gamma regression.

This package contains functions for statistical data analysis based on spatially-clustered techniques. The package allows estimating the spatially-clustered spatial regression models presented in Cerqueti, Maranzano \& Mattera (2024), "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe", arXiv preprint 2407.15874 <doi:10.48550/arXiv.2407.15874>. Specifically, the current release allows the estimation of the spatially-clustered linear regression model (SCLM), the spatially-clustered spatial autoregressive model (SCSAR), the spatially-clustered spatial Durbin model (SCSEM), and the spatially-clustered linear regression model with spatially-lagged exogenous covariates (SCSLX). From release 0.0.2, the library contains functions to estimate spatial clustering based on Adiajacent Matrix K-Means (AMKM) as described in Zhou, Liu \& Zhu (2019), "Weighted adjacent matrix for K-means clustering", Multimedia Tools and Applications, 78 (23) <doi:10.1007/s11042-019-08009-x>.

Implementation of single-source capture-recapture methods for population size estimation using zero-truncated, zero-one truncated and zero-truncated one-inflated Poisson, Geometric and Negative Binomial regression as well as Zelterman's and Chao's regression. Package includes point and interval estimators for the population size with variances estimated using analytical or bootstrap method. Details can be found in: van der Heijden et all. (2003) <doi:10.1191/1471082X03st057oa>, Böhning and van der Heijden (2019) <doi:10.1214/18-AOAS1232>, Böhning et al. (2020) Capture-Recapture Methods for the Social and Medical Sciences or Böhning and Friedl (2021) <doi:10.1007/s10260-021-00556-8>.

Offers a comprehensive solution for managing empty states in Shiny applications. It provides tools to create both default and customizable components for scenarios where data is absent or doesn't match user-defined filters. The package prioritizes user experience, ensuring clarity and consistency even when data is not available to display.

Density, distribution function, quantile function and random generation for the skewed t distribution of Fernandez and Steel.

This package implements survival-model-based imputation for censored laboratory measurements, including Tobit-type models with several distribution options. Suitable for data with values below detection or quantification limits, the package identifies the best-fitting distribution and produces realistic imputations that respect the censoring thresholds.

It computes the solutions to a generic stochastic growth model for a given set of user supplied parameters. It includes the solutions to the model, plots of the solution, a summary of the features of the model, a function that covers different types of consumption preferences, and a function that computes the moments of a Markov process. Merton, Robert C (1971) <doi:10.1016/0022-0531(71)90038-X>, Tauchen, George (1986) <doi:10.1016/0165-1765(86)90168-0>, Wickham, Hadley (2009, ISBN:978-0-387-98140-6 ).

This package provides a set of plotting methods for simmer trajectories and simulations.

Nonparametric and semiparametric estimations of the time-dependent ROC curve for an incomplete failure time data with surrogate failure time endpoints.

S4 class wrappers for the ODBC and Pool DBI connection, also provides some utilities to paste small datasets to clipboard, rename columns. It is used by the package stacomiR for connections to the database. Development versions of stacomiR are available in R-forge.

This package provides several Bayesian survival models for spatial/non-spatial survival data: proportional hazards (PH), accelerated failure time (AFT), proportional odds (PO), and accelerated hazards (AH), a super model that includes PH, AFT, PO and AH as special cases, Bayesian nonparametric nonproportional hazards (LDDPM), generalized accelerated failure time (GAFT), and spatially smoothed Polya tree density estimation. The spatial dependence is modeled via frailties under PH, AFT, PO, AH and GAFT, and via copulas under LDDPM and PH. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou, Hanson and Zhang (2020) <doi:10.18637/jss.v092.i09>.

Simple implementation of Semantic Versioning 2.0.0 ('SemVer') on the vctrs package. This package provides a simple way to create, compare, and manipulate semantic versions in R. It is designed to be lightweight and easy to use.

It allows running Praat scripts from R and it provides some wrappers for basic plotting. It also adds support for literate markdown tangling. The package is designed to bring reproducible phonetic research into R.

Parameter estimation for stochastic volatility models using maximum likelihood. The latent log-volatility is integrated out of the likelihood using the Laplace approximation. The models are fitted via TMB (Template Model Builder) (Kristensen, Nielsen, Berg, Skaug, and Bell (2016) <doi:10.18637/jss.v070.i05>).

Includes bases for litholog generation: graphical functions based on R base graphics, interval management functions and svg importation functions among others. Also include stereographic projection functions, and other functions made to deal with large datasets while keeping options to get into the details of the data. When using for publication please cite Sebastien Wouters, Anne-Christine Da Silva, Frederic Boulvain and Xavier Devleeschouwer, 2021. The R Journal 13:2, 153-178. The palaeomagnetism functions are based on: Tauxe, L., 2010. Essentials of Paleomagnetism. University of California Press. <https://earthref.org/MagIC/books/Tauxe/Essentials/>; Allmendinger, R. W., Cardozo, N. C., and Fisher, D., 2013, Structural Geology Algorithms: Vectors & Tensors: Cambridge, England, Cambridge University Press, 289 pp.; Cardozo, N., and Allmendinger, R. W., 2013, Spherical projections with OSXStereonet: Computers & Geosciences, v. 51, no. 0, p. 193 - 205, <doi: 10.1016/j.cageo.2012.07.021>.

Manipulating input and output files of the STICS crop model. Files are either JavaSTICS XML files or text files used by the model fortran executable. Most basic functionalities are reading or writing parameter names and values in both XML or text input files, and getting data from output files. Advanced functionalities include XML files generation from XML templates and/or spreadsheets, or text files generation from XML files by using xslt transformation.