This package provides a graphical user interface (GUI) to the functions implemented in the R package DQAstats'. Publication: Mang et al. (2021) <doi:10.1186/s12911-022-01961-z>.

This package provides a collection of functions to estimate parameters of a diffusion model via a D*M analysis. Build in models are: the Ratcliff diffusion model, the RWiener diffusion model, and Linear Ballistic Accumulator models. Custom models functions can be specified as long as they have a density function.

This package provides sample size and power calculations when the treatment time-lag effect is present and the lag duration is either homogeneous across the individual subject, or varies heterogeneously from individual to individual within a certain domain and following a specific pattern. The methods used are described in Xu, Z., Zhen, B., Park, Y., & Zhu, B. (2017) <doi:10.1002/sim.7157>.

This package contains data organized by topics: categorical data, regression model, means comparisons, independent and repeated measures ANOVA, mixed ANOVA and ANCOVA.

Set of functions for Data Envelopment Analysis, including classical, fuzzy, cross-efficiency, bootstrapping, and Malmquist models. See: Banker, R.; Charnes, A.; Cooper, W.W. (1984). <doi:10.1287/mnsc.30.9.1078>, Charnes, A.; Cooper, W.W.; Rhodes, E. (1978). <doi:10.1016/0377-2217(78)90138-8> and Charnes, A.; Cooper, W.W.; Rhodes, E. (1981). <doi:10.1287/mnsc.27.6.668>.

Compute the fixed effects dynamic panel threshold model suggested by Ramà rez-Rondán (2020) <doi:10.1080/07474938.2019.1624401>, and dynamic panel linear model suggested by Hsiao et al. (2002) <doi:10.1016/S0304-4076(01)00143-9>, where maximum likelihood type estimators are used. Multiple thresholds estimation based on Markov Chain Monte Carlo (MCMC) is allowed, and model selection of linear model, threshold model and multiple threshold model is also allowed.

Utility functions to be used to analyse datasets obtained from seed germination/emergence assays. Fits several types of seed germination/emergence models, including those reported in Onofri et al. (2018) "Hydrothermal-time-to-event models for seed germination", European Journal of Agronomy, 101, 129-139 <doi:10.1016/j.eja.2018.08.011>. Contains several datasets for practicing.

It generates summary statistics on the input dataset using different descriptive univariate statistical measures on entire data or at a group level. Though there are other packages which does similar job but each of these are deficient in one form or other, in the measures generated, in treating numeric, character and date variables alike, no functionality to view these measures on a group level or the way the output is represented. Given the foremost role of the descriptive statistics in any of the exploratory data analysis or solution development, there is a need for a more constructive, structured and refined version over these packages. This is the idea behind the package and it brings together all the required descriptive measures to give an initial understanding of the data quality, distribution in a faster,easier and elaborative way.The function brings an additional capability to be able to generate these statistical measures on the entire dataset or at a group level. It calculates measures of central tendency (mean, median), distribution (count, proportion), dispersion (min, max, quantile, standard deviation, variance) and shape (skewness, kurtosis). Addition to these measures, it provides information on the data type, count on no. of rows, unique entries and percentage of missing entries. More importantly the measures are generated based on the data types as required by them,rather than applying numerical measures on character and data variables and vice versa. Output as a dataframe object gives a very neat representation, which often is useful when working with a large number of columns. It can easily be exported as csv and analyzed further or presented as a summary report for the data.

Solving large scale distance weighted discrimination. The main algorithm is a symmetric Gauss-Seidel based alternating direction method of multipliers (ADMM) method. See Lam, X.Y., Marron, J.S., Sun, D.F., and Toh, K.C. (2018) <doi:10.48550/arXiv.1604.05473> for more details.

Computes the ATM (Attractor Transition Matrix) structure and the tree-like structure describing the cell differentiation process (based on the Threshold Ergodic Set concept introduced by Serra and Villani), starting from the Boolean networks with synchronous updating scheme of the BoolNet R package. TESs (Threshold Ergodic Sets) are the mathematical abstractions that represent the different cell types arising during ontogenesis. TESs and the powerful model of biological differentiation based on Boolean networks to which it belongs have been firstly described in "A Dynamical Model of Genetic Networks for Cell Differentiation" Villani M, Barbieri A, Serra R (2011) A Dynamical Model of Genetic Networks for Cell Differentiation. PLOS ONE 6(3): e17703.

This package provides a unified framework to building Area Deprivation Index (ADI), Social Vulnerability Index (SVI), and Neighborhood Deprivation Index (NDI) deprivation measures and accessing related data from the U.S. Census Bureau such as Gini coefficient data. Tools are also available for calculating percentiles, quantiles, and for creating clear map breaks for data visualization.

This package provides functions providing an easy and intuitive way for fitting and clusters data using the Mixture of Unigrams models by means the Expectation-Maximization algorithm (Nigam, K. et al. (2000). <doi:10.1023/A:1007692713085>), Mixture of Dirichlet-Multinomials estimated by Gradient Descent (Anderlucci, Viroli (2020) <doi:10.1007/s11634-020-00399-3>) and Deep Mixture of Multinomials whose estimates are obtained with Gibbs sampling scheme (Viroli, Anderlucci (2020) <doi:10.1007/s11222-020-09989-9>). There are also functions for graphical representation of clusters obtained.

An interface to explore, analyze, and visualize droplet digital PCR (ddPCR) data in R. This is the first non-proprietary software for analyzing two-channel ddPCR data. An interactive tool was also created and is available online to facilitate this analysis for anyone who is not comfortable with using R.

The DImodels package is suitable for analysing data from biodiversity and ecosystem function studies using the Diversity-Interactions (DI) modelling approach introduced by Kirwan et al. (2009) <doi:10.1890/08-1684.1>. Suitable data will contain proportions for each species and a community-level response variable, and may also include additional factors, such as blocks or treatments. The package can perform data manipulation tasks, such as computing pairwise interactions (the DI_data() function), can perform an automated model selection process (the autoDI() function) and has the flexibility to fit a wide range of user-defined DI models (the DI() function).

This package provides a decorator is a function that receives a function, extends its behaviour, and returned the altered function. Any caller that uses the decorated function uses the same interface as it were the original, undecorated function. Decorators serve two primary uses: (1) Enhancing the response of a function as it sends data to a second component; (2) Supporting multiple optional behaviours. An example of the first use is a timer decorator that runs a function, outputs its execution time on the console, and returns the original function's result. An example of the second use is input type validation decorator that during running time tests whether the caller has passed input arguments of a particular class. Decorators can reduce execution time, say by memoization, or reduce bugs by adding defensive programming routines.

This package provides tools for converting and imputing date values to the ISO 8601 standard format and for reconciling differences between two versions of a data set. The package automatically detects date patterns within data frame columns and converts them to consistent ISO-formatted dates, with optional imputation of missing day or month components based on user-defined rules. It also includes functionality to identify inserted, deleted, and updated records, as well as column- and value-level changes, when comparing old and new versions of a data frame. Only one date format may be applied within a single column.

Efficient methods for computing distance covariance and relevant statistics. See Székely et al.(2007) <doi:10.1214/009053607000000505>; Székely and Rizzo (2013) <doi:10.1016/j.jmva.2013.02.012>; Székely and Rizzo (2014) <doi:10.1214/14-AOS1255>; Huo and Székely (2016) <doi:10.1080/00401706.2015.1054435>.

Fast & memory-efficient functions to analyze and manipulate large spatial data data sets. It leverages the fast analytical capabilities of DuckDB and its spatial extension (see <https://duckdb.org/docs/stable/core_extensions/spatial/overview>) while maintaining compatibility with Râ s spatial data ecosystem to work with spatial vector data.

Comparison of the accuracy of two binary diagnostic tests in a "paired" study design, i.e. when each test is applied to each subject in the study.

This package performs the drifting Markov models (DMM) which are non-homogeneous Markov models designed for modeling the heterogeneities of sequences in a more flexible way than homogeneous Markov chains or even hidden Markov models. In this context, we developed an R package dedicated to the estimation, simulation and the exact computation of associated reliability of drifting Markov models. The implemented methods are described in Vergne, N. (2008), <doi:10.2202/1544-6115.1326> and Barbu, V.S., Vergne, N. (2019) <doi:10.1007/s11009-018-9682-8> .

Calculate adjusted means and proportions of a variable by groups defined by another variable by direct standardisation, standardised to the structure of the dataset.

Output graphics to EMF+/EMF.

Have you ever been tempted to create roxygen2'-style documentation comments for one of your functions that was not part of one of your packages (yet)? This is exactly what this package is about: running roxygen2 on (chunks of) a single code file.

Create a details HTML tag around R objects to place in a Markdown, Rmarkdown and roxygen2 documentation.