Storing very large data objects on a local drive, while still making it possible to manipulate the data in an efficient manner.

The goal of automatedRecLin is to perform record linkage (also known as entity resolution) in unsupervised or supervised settings. It compares pairs of records from two datasets using selected comparison functions to estimate the probability or density ratio between matched and non-matched records. Based on these estimates, it predicts a set of matches that maximizes entropy. For details see: Lee et al. (2022) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2022001/article/00007-eng.htm>, Vo et al. (2023) <https://ideas.repec.org/a/eee/csdana/v179y2023ics0167947322002365.html>, Sugiyama et al. (2008) <doi:10.1007/s10463-008-0197-x>.

Automatically selects and runs the most appropriate statistical test for your data, returning clear, easy-to-read results. Ideal for all experience levels.

Actuarial reports are prepared for the last day of a specific period, such as a month, a quarter or a year. Actuarial models assume that certain events happen at the beginning or end of periods. The package contains functions to easily refer to the first or last (working) day within a specific period relative to a base date to facilitate actuarial reporting and to compare results.

This package provides methods to analyse spatial units in archaeology from the refitting relationships between fragments of objects scattered in these units (e.g. stratigraphic layers). Graphs are used to model archaeological observations. The package is mainly based on the igraph package for graph analysis. Functions can: 1) create, manipulate, visualise, and simulate fragmentation graphs, 2) measure the cohesion and admixture of archaeological spatial units, and 3) characterise the topology of a specific set of refitting relationships. A series of published empirical datasets is included. Documentation about archeofrag is provided by a vignette and by the accompanying scientific papers: Plutniak (2021, Journal of Archaeological Science, <doi:10.1016/j.jas.2021.105501>) and Plutniak (2022, Journal of Open Source Software, <doi:10.21105/joss.04335>). This package is complemented by the archeofrag.gui R package, a companion GUI application available at <https://analytics.huma-num.fr/Sebastien.Plutniak/archeofrag/>.

RStudio allows to show and navigate for the outline of a R Markdown file, but not for R Markdown projects with multiple files. For this reason, I have developed several RStudio addins capable of show project outline. Each addin is specialized in showing projects of different types: R Markdown project, bookdown package project and LaTeX project. There is a configuration file that allows you to customize additional searches.

This package provides a number of functions to create and analyze factorial plans according to the Design of Experiments (DoE) approach, with the addition of some utility function to perform some statistical analyses. DoE approach follows the approach in "Design and Analysis of Experiments" by Douglas C. Montgomery (2019, ISBN:978-1-119-49244-3). The package also provides utilities used in the course "Analysis of Data and Statistics" at the University of Trento, Italy.

This package provides a function to calculate the concentration of un-ionized ammonia in the total ammonia in aqueous solution using the pH and temperature values.

Obtain network structures from animal GPS telemetry observations and statistically analyse them to assess their adequacy for social network analysis. Methods include pre-network data permutations, bootstrapping techniques to obtain confidence intervals for global and node-level network metrics, and correlation and regression analysis of the local network metrics.

This package provides a toolkit to predict antimicrobial peptides from protein sequences on a genome-wide scale. It incorporates two support vector machine models ("precursor" and "mature") trained on publicly available antimicrobial peptide data using calculated physico-chemical and compositional sequence properties described in Meher et al. (2017) <doi:10.1038/srep42362>. In order to support genome-wide analyses, these models are designed to accept any type of protein as input and calculation of compositional properties has been optimised for high-throughput use. For best results it is important to select the model that accurately represents your sequence type: for full length proteins, it is recommended to use the default "precursor" model. The alternative, "mature", model is best suited for mature peptide sequences that represent the final antimicrobial peptide sequence after post-translational processing. For details see Fingerhut et al. (2020) <doi:10.1093/bioinformatics/btaa653>. The ampir package is also available via a Shiny based GUI at <https://ampir.marine-omics.net/>.

An unofficial companion to "Applied Logistic Regression" by D.W. Hosmer, S. Lemeshow and R.X. Sturdivant (3rd ed., 2013) containing the dataset used in the book.

Connect to the Adobe Analytics API v2.0 <https://github.com/AdobeDocs/analytics-2.0-apis> which powers Analysis Workspace'. The package was developed with the analyst in mind, and it will continue to be developed with the guiding principles of iterative, repeatable, timely analysis.

Fit, compare, and visualise linear and nonlinear regression models tailored to field-trial and dose-response agricultural data. Provides S3 classes for mixed-effects models (via lme4'), nonlinear growth curves (logistic, Gompertz', asymptotic, linear-plateau, quadratic), and four/five-parameter log-logistic dose-response models (via drc'). Includes automated starting-value heuristics, goodness-of-fit statistics, residual diagnostics, and ggplot2'-based visualisation. Methods are based on Bates and Watts (1988, ISBN:9780471816430), Ritz and others (2015) <doi:10.1371/journal.pone.0146021>, and Bates and others (2015) <doi:10.18637/jss.v067.i01>.

This package provides tools for constructing a matched design with multiple comparison groups. Further specifications of refined covariate balance restriction and exact match on covariate can be imposed. Matches are approximately optimal in the sense that the cost of the solution is at most twice the optimal cost, Crama and Spieksma (1992) <doi:10.1016/0377-2217(92)90078-N>, Karmakar, Small and Rosenbaum (2019) <doi:10.1080/10618600.2019.1584900>.

Display air quality model output and monitoring data using scatterplots, grids, and legends.

Utilities to parse authors fields from DESCRIPTION files and general purpose functions to deduplicate names in database, beyond the specific case of R package authors.

Flexible parametric Accelerated Hazards (AH) regression models in overall and relative survival frameworks with 13 distinct Baseline Distributions. The AH Model can also be applied to lifetime data with crossed survival curves. Any user-defined parametric distribution can be fitted, given at least an R function defining the cumulative hazard and hazard rate functions. See Chen and Wang (2000) <doi:10.1080/01621459.2000.10474236>, and Lee (2015) <doi:10.1007/s10985-015-9349-5> for more details.

This package provides an efficient suite of R tools for scorecard modeling, analysis, and visualization. Including equal frequency binning, equidistant binning, K-means binning, chi-square binning, decision tree binning, data screening, manual parameter modeling, fully automatic generation of scorecards, etc. This package is designed to make scorecard development easier and faster. References include: 1. <http://shichen.name/posts/>. 2. Dong-feng Li(Peking University),Class PPT. 3. <https://zhuanlan.zhihu.com/p/389710022>. 4. <https://www.zhangshengrong.com/p/281oqR9JNw/>.

An interface to Azure Data Explorer', also known as Kusto', a fast, distributed data exploration service from Microsoft: <https://azure.microsoft.com/en-us/products/data-explorer/>. Includes DBI and dplyr interfaces, with the latter modelled after the dbplyr package, whereby queries are translated from R into the native KQL query language and executed lazily. On the admin side, the package extends the object framework provided by AzureRMR to support creation and deletion of databases, and management of database principals. Part of the AzureR family of packages.

Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss reserving models specialise in capturing different aspects of loss data. This is recognised in practice in the sense that results from different models are often considered, and sometimes combined. For instance, actuaries may take a weighted average of the prediction outcomes from various loss reserving models, often based on subjective assessments. This package allows for the use of a systematic framework to objectively combine (i.e. ensemble) multiple stochastic loss reserving models such that the strengths offered by different models can be utilised effectively. Our framework is developed in Avanzi et al. (2023). Firstly, our criteria model combination considers the full distributional properties of the ensemble and not just the central estimate - which is of particular importance in the reserving context. Secondly, our framework is that it is tailored for the features inherent to reserving data. These include, for instance, accident, development, calendar, and claim maturity effects. Crucially, the relative importance and scarcity of data across accident periods renders the problem distinct from the traditional ensemble techniques in statistical learning. Our framework is illustrated with a complex synthetic dataset. In the results, the optimised ensemble outperforms both (i) traditional model selection strategies, and (ii) an equally weighted ensemble. In particular, the improvement occurs not only with central estimates but also relevant quantiles, such as the 75th percentile of reserves (typically of interest to both insurers and regulators). Reference: Avanzi B, Li Y, Wong B, Xian A (2023) "Ensemble distributional forecasting for insurance loss reserving" <doi:10.48550/arXiv.2206.08541>.

In panel data settings, specifies set of candidate models, fits them to data from pre-treatment validation periods, and selects model as average over candidate models, weighting each by posterior probability of being most robust given its differential average prediction errors in pre-treatment validation periods. Subsequent estimation and inference of causal effect's bounds accounts for both model and sampling uncertainty, and calculates the robustness changepoint value at which bounds go from excluding to including 0. The package also includes a range of diagnostic plots, such as those illustrating models differential average prediction errors and the posterior distribution of which model is most robust.

Construct time series for Germany's municipalities (Gemeinden) and districts (Kreise) using a annual crosswalk constructed by the Federal Office for Building and Regional Planning (BBSR).

Adaptive wavelet lifting transforms for signal denoising using optimal local neighbourhood regression, from Nunes et al. (2006) <doi:10.1007/s11222-006-6560-y>.

This package provides functions for interacting directly with the ALTADATA API. With this R package, developers can build applications around the ALTADATA API without having to deal with accessing and managing requests and responses. ALTADATA is a curated data marketplace for more information go to <https://www.altadata.io>.