This toolkit implements a numerical solution algorithm to invert a quality of life measure from observed data. Unlike the traditional Rosen-Roback measure, this measure accounts for mobility frictionsâ generated by idiosyncratic tastes and local ties â and trade frictions â generated by trade costs and non-tradable services, thereby reducing non-classical measurement error. The QoL measure is based on Ahlfeldt, Bald, Roth, Seidel (2024) <https://econpapers.repec.org/RePEc:boc:bocode:s459382> "Measuring Quality of Life under Spatial Frictions". When using this programme or the toolkit in your work, please cite the paper.

Concept drift refers to the change in the data distribution or in the relationships between variables over time. drifter calculates distances between variable distributions or variable relations and identifies both types of drift. Key functions are: calculate_covariate_drift() checks distance between corresponding variables in two datasets, calculate_residuals_drift() checks distance between residual distributions for two models, calculate_model_drift() checks distance between partial dependency profiles for two models, check_drift() executes all checks against drift. drifter is a part of the DrWhy.AI universe (Biecek 2018) <arXiv:1806.08915>.

Descriptive analysis is essential for publishing medical articles. This package provides an easy way to conduct the descriptive analysis. 1. Both numeric and factor variables can be handled. For numeric variables, normality test will be applied to choose the parametric and nonparametric test. 2. Both two or more groups can be handled. For groups more than two, the post hoc test will be applied, Tukey for the numeric variables and FDR for the factor variables. 3. T test, ANOVA or Fisher test can be forced to apply. 4. Mean and standard deviation can be forced to display.

Developed by CDC/ATSDR (Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry), Social Vulnerability Index (SVI) serves as a tool to assess the resilience of communities by taking into account socioeconomic and demographic factors. Provided with year(s), region(s) and a geographic level of interest, findSVI retrieves required variables from US census data and calculates SVI for communities in the specified area based on CDC/ATSDR SVI documentation. Reference for the calculation methods: Flanagan BE, Gregory EW, Hallisey EJ, Heitgerd JL, Lewis B (2011) <doi:10.2202/1547-7355.1792>.

Guild AI is an open-source tool for managing machine learning experiments. It's for scientists, engineers, and researchers who want to run scripts, compare results, measure progress, and automate machine learning workflow. Guild AI is a light weight, external tool that runs locally. It works with any framework, doesn't require any changes to your code, or access to any web services. Users can easily record experiment metadata, track model changes, manage experiment artifacts, tune hyperparameters, and share results. Guild AI combines features from Git', SQLite', and Make to provide a lab notebook for machine learning.

Nonparametric tests for clustered data in pre-post intervention design documented in Cui and Harrar (2021) <doi:10.1002/bimj.201900310> and Harrar and Cui (2022) <doi:10.1016/j.jspi.2022.05.009>. Other than the main test results mentioned in the reference paper, this package also provides a function to calculate the sample size allocations for the input long format data set, and also a function for adjusted/unadjusted confidence intervals calculations. There are also functions to visualize the distribution of data across different intervention groups over time, and also the adjusted/unadjusted confidence intervals.

Snow water equivalent is modeled with the process based models delta.snow and HS2SWE and empirical regression, which use relationships between density and diverse at-site parameters. The methods are described in Winkler et al. (2021) <doi:10.5194/hess-25-1165-2021>, Magnusson et al. (2025) <doi:10.1016/j.coldregions.2025.104435>, Guyennon et al. (2019) <doi:10.1016/j.coldregions.2019.102859>, Pistocchi (2016) <doi:10.1016/j.ejrh.2016.03.004>, Jonas et al. (2009) <doi:10.1016/j.jhydrol.2009.09.021> and Sturm et al. (2010) <doi:10.1175/2010JHM1202.1>.

An add-on to the party package, with a faster implementation of the partial-conditional permutation importance for random forests. The standard permutation importance is implemented exactly the same as in the party package. The conditional permutation importance can be computed faster, with an option to be backward compatible to the party implementation. The package is compatible with random forests fit using the party and the randomForest package. The methods are described in Strobl et al. (2007) <doi:10.1186/1471-2105-8-25> and Debeer and Strobl (2020) <doi:10.1186/s12859-020-03622-2>.

Regression analysis using common models in seed temperature studies, such as the Gaussian model (Martins, JF, Barroso, AAM, & Alves, PLCA (2017) <doi:10.1590/s0100-83582017350100039>), quadratic (Nunes, AL, Sossmeier, S, Gotz, AP, & Bispo, NB (2018) <doi: 10.17265/2161-6264/2018.06.002>) and others with potential for use, such as those implemented in the drc package (Ritz, C, Baty, F, Streibig, JC, & Gerhard, D (2015). <doi:10.1371/journal.pone.0146021>), in the estimation of the ideal and cardinal temperature for the occurrence of plant seed germination. The functions return graphs with the equations automatically.

Two versions of sample variance plots, Sv-plot1 and Sv-plot2, will be provided illustrating the squared deviations from sample variance. Besides indicating the contribution of squared deviations for the sample variability, these plots are capable of detecting characteristics of the distribution such as symmetry, skewness and outliers. A remarkable graphical method based on Sv-plot2 can determine the decision on testing hypotheses over one or two population means. In sum, Sv-plots will be appealing visualization tools. Complete description of this methodology can be found in the article, Wijesuriya (2020) <doi:10.1080/03610918.2020.1851716>.

This package provides methods for anonymisation of spatial datasets while preserving spatial structure and relationships. Original coordinates or raster geometries are transformed using randomized or predefined vertical shifts, horizontal shifts, and rotations. Compatible with point-based data in matrix', data.frame', or sf formats, as well as terra raster objects. Supports reversible anonymisation workflows, hash-based validation, shapefile export, and consistent tangling across related datasets using stored transformation sequences. Approach informed by the De-Identification Decision Making Framework (CM Oâ Keefe, S Otorepec, M Elliot, E Mackey, and K Oâ Hara 2017) <doi:10.4225/08/59c169433efd4>.

Structural equation modeling (SEM) has a long history of representing models graphically as path diagrams. The semPlot package for R fills the gap between advanced, but time-consuming, graphical software and the limited graphics produced automatically by SEM software. In addition, semPlot offers more functionality than drawing path diagrams: it can act as a common ground for importing SEM results into R. Any result usable as input to semPlot can also be represented in any of the three popular SEM frame-works, as well as translated to input syntax for the R packages sem and lavaan.

This package performs simulation-based inference as an alternative to the delta method for obtaining valid confidence intervals and p-values for regression post-estimation quantities, such as average marginal effects and predictions at representative values. This framework for simulation-based inference is especially useful when the resulting quantity is not normally distributed and the delta method approximation fails. The methodology is described in King, Tomz, and Wittenberg (2000) <doi:10.2307/2669316>. clarify is meant to replace some of the functionality of the archived package Zelig'; see the vignette "Translating Zelig to clarify" for replicating this functionality.

This package provides tools for exploration of R package dependencies. The main deepdep() function allows to acquire deep dependencies of any package and plot them in an elegant way. It also adds some popularity measures for the packages e.g. in the form of download count through the cranlogs package. Uses the CRAN metadata database <http://crandb.r-pkg.org> and Bioconductor metadata <https://bioconductor.org>. Other data acquire functions are: get_dependencies(), get_downloads() and get_description(). The deepdep_shiny() function runs shiny application that helps to produce a nice deepdep plot.

This package provides R-implementation of Decision forest algorithm, which combines the predictions of multiple independent decision tree models for a consensus decision. In particular, Decision Forest is a novel pattern-recognition method which can be used to analyze: (1) DNA microarray data; (2) Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) data; and (3) Structure-Activity Relation (SAR) data. In this package, three fundamental functions are provided, as (1)DF_train, (2)DF_pred, and (3)DF_CV. run Dforest() to see more instructions. Weida Tong (2003) <doi:10.1021/ci020058s>.

Analysis of temporal changes (i.e. dynamics) of ecological entities, defined as trajectories on a chosen multivariate space, by providing a set of trajectory metrics and visual representations [De Caceres et al. (2019) <doi:10.1002/ecm.1350>; and Sturbois et al. (2021) <doi:10.1016/j.ecolmodel.2020.109400>]. Includes functions to estimate metrics for individual trajectories (length, directionality, angles, ...) as well as metrics to relate pairs of trajectories (dissimilarity and convergence). Functions are also provided to estimate the ecological quality of ecosystem with respect to reference conditions [Sturbois et al. (2023) <doi:10.1002/ecs2.4726>].

FASTQC is the most widely used tool for evaluating the quality of high throughput sequencing data. It produces, for each sample, an html report and a compressed file containing the raw data. If you have hundreds of samples, you are not going to open up each HTML page. You need some way of looking at these data in aggregate. fastqcr Provides helper functions to easily parse, aggregate and analyze FastQC reports for large numbers of samples. It provides a convenient solution for building a Multi-QC report, as well as, a one-sample report with result interpretations.

Manages a file system cache. Regular files can be moved or copied to the cache folder. Sub-folders can be created in order to organize the files. Files can be located inside the cache using a glob function. Text contents can be easily stored in and retrieved from the cache using dedicated functions. It can be used for an application or a package, as a global cache, or as a per-user cache, in which case the standard OS user cache folder will be used (e.g.: on Linux $HOME/.cache/R/my_app_or_pkg_cache_folder).

This package provides a framework to assist creation of marine ecosystem models, generating either R or C++ code which can then be optimised using the TMB package and standard R tools. Principally designed to reproduce gadget2 models in TMB', but can be extended beyond gadget2's capabilities. Kasper Kristensen, Anders Nielsen, Casper W. Berg, Hans Skaug, Bradley M. Bell (2016) <doi:10.18637/jss.v070.i05> "TMB: Automatic Differentiation and Laplace Approximation.". Begley, J., & Howell, D. (2004) <https://core.ac.uk/download/pdf/225936648.pdf> "An overview of Gadget, the globally applicable area-disaggregated general ecosystem toolbox. ICES.".

This package provides tools to build and work with bilateral generalized-mean price indexes (and by extension quantity indexes), and indexes composed of generalized-mean indexes (e.g., superlative quadratic-mean indexes, GEKS). Covers the core mathematical machinery for making bilateral price indexes, computing price relatives, detecting outliers, and decomposing indexes, with wrappers for all common (and many uncommon) index-number formulas. Implements and extends many of the methods in Balk (2008, <doi:10.1017/CBO9780511720758>), von der Lippe (2007, <doi:10.3726/978-3-653-01120-3>), and the CPI manual (2020, <doi:10.5089/9781484354841.069>).

This package contains implementations of integrative survival analysis routines, including regular Cox cure rate model proposed by Kuk and Chen (1992) <doi:10.1093/biomet/79.3.531> via an EM algorithm proposed by Sy and Taylor (2000) <doi:10.1111/j.0006-341X.2000.00227.x>, regularized Cox cure rate model with elastic net penalty following Masud et al. (2018) <doi:10.1177/0962280216677748>, and Zou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>, and weighted concordance index for cure models proposed by Asano and Hirakawa (2017) <doi:10.1080/10543406.2017.1293082>.

Following the common types of measures of uncertainty for parameter estimation, two measures of uncertainty were proposed for model selection, see Liu, Li and Jiang (2020) <doi:10.1007/s11749-020-00737-9>. The first measure is a kind of model confidence set that relates to the variation of model selection, called Mac. The second measure focuses on error of model selection, called LogP. They are all computed via bootstrapping. This package provides functions to compute these two measures. Furthermore, a similar model confidence set adapted from Bayesian Model Averaging can also be computed using this package.

This package provides a collection of functions for the analysis of archaeological mortality data (on the topic see e.g. Chamberlain 2006 <https://books.google.de/books?id=nG5FoO_becAC&lpg=PA27&ots=LG0b_xrx6O&dq=life%20table%20archaeology&pg=PA27#v=onepage&q&f=false>). It takes demographic data in different formats and displays the result in a standard life table as well as plots the relevant indices (percentage of deaths, survivorship, probability of death, life expectancy, percentage of population). It also checks for possible biases in the age structure and applies corrections to life tables.

This package provides a set of tools for likelihood-based estimation, model selection and testing of two- and three-range shift and migration models for animal movement data as described in Gurarie et al. (2017) <doi: 10.1111/1365-2656.12674>. Provided movement data (X, Y and Time), including irregularly sampled data, functions estimate the time, duration and location of one or two range shifts, as well as the ranging area and auto-correlation structure of the movment. Tests assess, for example, whether the shift was "significant", and whether a two-shift migration was a true return migration.