This package provides a collection of several functions related to construction and analysis of incomplete split-plot designs. The package contains functions to obtain and analyze incomplete split-plot designs for three kinds of situations namely (i) when blocks are complete with respect to main plot treatments and main plots are incomplete with respect to subplot treatments, (ii) when blocks are incomplete with respect to main plot treatments and main plots are complete with respect to subplot treatments and (iii) when blocks are incomplete with respect to main plot treatments and main plots are incomplete with respect to subplot treatments.

Generate plots based on the Item Pool Visualization concept for latent constructs. Item Pool Visualizations are used to display the conceptual structure of a set of items (self-report or psychometric). Dantlgraber, Stieger, & Reips (2019) <doi:10.1177/2059799119884283>.

This package provides coefficients of interrater reliability that are generalized to cope with randomly incomplete (i.e. unbalanced) datasets without any imputation of missing values or any (row-wise or column-wise) omissions of actually available data. Applied to complete (balanced) datasets, these generalizations yield the same results as the common procedures, namely the Intraclass Correlation according to McGraw & Wong (1996) \doi10.1037/1082-989X.1.1.30 and the Coefficient of Concordance according to Kendall & Babington Smith (1939) \doi10.1214/aoms/1177732186.

Several functions to calculate two important indexes (IBR (Integrated Biomarker Response) and IBRv2 (Integrated Biological Response version 2)), it also calculates the standardized values for enzyme activity for each index, and it has a graphing function to perform radarplots that make great data visualization for this type of data. Beliaeff, B., & Burgeot, T. (2002). <https://pubmed.ncbi.nlm.nih.gov/12069320/>. Sanchez, W., Burgeot, T., & Porcher, J.-M. (2013).<doi:10.1007/s11356-012-1359-1>. Devin, S., Burgeot, T., Giambérini, L., Minguez, L., & Pain-Devin, S. (2014). <doi:10.1007/s11356-013-2169-9>. Minato N. (2022). <https://minato.sip21c.org/msb/>.

Nicely formatted frequency tables and contingency tables (1-way, 2-way, 3-way and 4-way tables), that can easily be exported to HTML or Office documents. Designed to work with pipes.

The correction is achieved under the assumption that non-migrating cells of the essay approximately form a quadratic flow profile due to frictional effects, compare law of Hagen-Poiseuille for flow in a tube. The script fits a conical plane to give xyz-coordinates of the cells. It outputs the number of migrated cells and the new corrected coordinates.

Integration of disparate datasets is needed in order to make efficient use of all available data and thereby address the issues currently threatening biodiversity. Data integration is a powerful modeling framework which allows us to combine these datasets together into a single model, yet retain the strengths of each individual dataset. We therefore introduce the package, intSDM': an R package designed to help ecologists develop a reproducible workflow of integrated species distribution models, using data both provided from the user as well as data obtained freely online. An introduction to data integration methods is discussed in Issac, Jarzyna, Keil, Dambly, Boersch-Supan, Browning, Freeman, Golding, Guillera-Arroita, Henrys, Jarvis, Lahoz-Monfort, Pagel, Pescott, Schmucki, Simmonds and Oâ Hara (2020) <doi:10.1016/j.tree.2019.08.006>.

This package provides a collection of Item Response Theory (IRT) and Computerized Adaptive Testing (CAT) functions that are used in psychometrics.

Graphical User Interface allowing to determine the concentration in the sample in CFU per mL or in number of copies per mL provided to qPCR results after with or without PMA treatment. This package is simply to use because no knowledge in R commands is necessary. A graphic represents the standard curve, and a table containing the result for each sample is created.

This package provides a set of functions to estimate interactions flexibly in the face of possibly many controls. Implements the procedures described in Blackwell and Olson (2022) <doi:10.1017/pan.2021.19>.

This package provides a function to calculate infinite-jackknife-based standard errors for fixed effects parameters in brms models, handling both clustered and independent data. References: Ji et al. (2024) <doi:10.48550/arXiv.2407.09772>; Giordano et al. (2024) <doi:10.48550/arXiv.2305.06466>.

Analyzes raw abundance data from a cellular thermal shift experiment and calculates melt temperatures and melt shifts for each protein in the experiment. McCracken (2022) <doi:10.1101/2022.12.30.522131>.

Collect marketing data from Instagram Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

ISA is a metadata framework to manage an increasingly diverse set of life science, environmental and biomedical experiments. In isatabr methods for reading, modifying and writing of files in the ISA-Tab format are implemented. It also contains methods for processing assay data.

Single Layer Feed-forward Neural networks (SLFNs) have many applications in various fields of statistical modelling, especially for time-series forecasting. However, there are some major disadvantages of training such networks via the widely accepted gradient-based backpropagation algorithm, such as convergence to local minima, dependencies on learning rate and large training time. These concerns were addressed by Huang et al. (2006) <doi:10.1016/j.neucom.2005.12.126>, wherein they introduced the Extreme Learning Machine (ELM), an extremely fast learning algorithm for SLFNs which randomly chooses the weights connecting input and hidden nodes and analytically determines the output weights of SLFNs. It shows good generalized performance, but is still subject to a high degree of randomness. To mitigate this issue, this package uses a dimensionality reduction technique given in Hyvarinen (1999) <doi:10.1109/72.761722>, namely, the Independent Component Analysis (ICA) to determine the input-hidden connections and thus, remove any sort of randomness from the algorithm. This leads to a robust, fast and stable ELM model. Using functions within this package, the proposed model can also be compared with an existing alternative based on the Principal Component Analysis (PCA) algorithm given by Pearson (1901) <doi:10.1080/14786440109462720>, i.e., the PCA based ELM model given by Castano et al. (2013) <doi:10.1007/s11063-012-9253-x>, from which the implemented ICA based algorithm is greatly inspired.

This package provides a collection of datasets containing a variety of in vitro toxicokinetic measurements including -- but not limited to -- chemical fraction unbound in the presence of plasma (f_up), intrinsic hepatic clearance (Clint, uL/min/million hepatocytes), and membrane permeability for oral absorption (Caco2). The datasets provided by the package were processed and analyzed with the companion invitroTKstats package.

This package provides functions to access data from public RESTful APIs including World Bank API', and REST Countries API', retrieving real-time or historical data related to India, such as economic indicators, and international demographic and geopolitical indicators. Additionally, the package includes one of the largest curated collections of open datasets focused on India, covering topics such as population, economy, weather, politics, health, biodiversity, sports, agriculture, cybercrime, infrastructure, and more. The package supports reproducible research and teaching by integrating reliable international APIs and structured datasets from public, academic, and government sources. For more information on the APIs, see: World Bank API <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>, REST Countries API <https://restcountries.com/>.

This package provides datasets and functions for the class "Modelling and Data Analysis for Pharmaceutical Sciences". The datasets can be used to present various methods of data analysis and statistical modeling. Functions for data visualization are also implemented.

This package provides methods for estimating causal effects in the presence of interference described in B. Saul and M. Hugdens (2017) <doi:10.18637/jss.v082.i02>. Currently it implements the inverse-probability weighted (IPW) estimators proposed by E.J. Tchetgen Tchetgen and T.J. Vanderweele (2012) <doi:10.1177/0962280210386779>.

Semiparametric regression models on the cumulative incidence function for interval-censored competing risks data as described in Bakoyannis, Yu, & Yiannoutsos (2017) /doi10.1002/sim.7350 and the models with missing event types as described in Park, Bakoyannis, Zhang, & Yiannoutsos (2021) \doi10.1093/biostatistics/kxaa052. The proportional subdistribution hazards model (Fine-Gray model), the proportional odds model, and other models that belong to the class of semiparametric generalized odds rate transformation models.

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 R package implements methods for estimation and inference under Incomplete Block Designs and Balanced Incomplete Block Designs within a design-based finite-population framework. Based on Koo and Pashley (2024) <arXiv:2405.19312>, it includes block-level estimators and extends to unit-level effects using Horvitz-Thompson and Hájek estimators. The package also provides asymptotic confidence intervals to support valid statistical inference.

This package implements continuous-time hidden Markov models (HMMs) to infer identity-by-descent (IBD) segments shared by two individuals from their single-nucleotide polymorphism (SNP) genotypes. Provides posterior probabilities at each marker (forward-backward algorithm), prediction of IBD segments (Viterbi algorithm), and functions for visualising results. Supports both autosomal data and X-chromosomal data.

Generates a Graphviz graph of the most significant 3-way interaction gains (i.e. conditional information gains) based on a provided discrete data frame. Various output formats are supported ('Graphviz', SVG, PNG, PDF, PS). For references, see the webpage of Aleks Jakulin <http://stat.columbia.edu/~jakulin/Int/>.