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.

This package provides a helper function, to bulk read SQL code from separate files and load it into an R list, where the list elements contain the individual statements and queries as strings. This works by annotating the SQL code with a name comment, which also will be the name of the list element.

Flexible nonlinear extension of spatial autoregressive (SAR), spatial error (SEM), and spatial autoregressive with autoregressive disturbances (SARAR) models with multiple regression engines (generalized additive models ('mgcv'), gradient boosting ('mboost'), multivariate adaptive regression splines ('earth'), and xgboost') and two families of spatial-parameter estimators: maximum likelihood and the determinant-free Closed-Form Estimator of Smirnov (2020) <doi:10.1111/gean.12268>. See Geniaux G. (2026). "Flexible nonlinear spatial autoregressive models: a gradient boosting approach with closed-form estimation." Presented at Spatial Econometrics World Congress (SEA/SEW 2026, Paris), unpublished.

The goal of safejoin is to guarantee that when performing joins extra rows are not added to your data. safejoin provides a wrapper around dplyr::left_join that will raise an error when extra rows are unexpectedly added to your data. This can be useful when working with data where you expect there to be a many to one relationship but you are not certain the relationship holds.

Proposes application of spectral analysis and jack-knife resampling for multivariate sequence forecasting. The application allows for a fast random search in a compact space of hyper-parameters composed by Sequence Length and Jack-Knife Leave-N-Out.

English is the native language for only 5% of the World population. Also, only 17% of us can understand this text. Moreover, the Latin alphabet is the main one for merely 36% of the total. The early computer era, now a very long time ago, was dominated by the US. Due to the proliferation of the internet, smartphones, social media, and other technologies and communication platforms, this is no longer the case. This package replaces base R string functions (such as grep(), tolower(), sprintf(), and strptime()) with ones that fully support the Unicode standards related to natural language and date-time processing. It also fixes some long-standing inconsistencies, and introduces some new, useful features. Thanks to ICU (International Components for Unicode) and stringi', they are fast, reliable, and portable across different platforms.

Facilitate phonetic transliteration between different languages. With support for both Hindi and English, this package provides a way to convert text between Hindi and English dataset. Whether you're working with multilingual data or need to convert dataset for analysis or presentation purposes, it offers a simple and efficient solution and harness the power of phonetic transliteration in your projects with this versatile package.

These are my collection of R Markdown templates, mostly for compilation to PDF. These are useful for all things academic and professional, if you are using R Markdown for things like your CV or your articles and manuscripts.

Estimate the parameters of multivariate endogenous switching and sample selection models using methods described in Newey (2009) <doi:10.1111/j.1368-423X.2008.00263.x>, E. Kossova, B. Potanin (2018) <https://ideas.repec.org/a/ris/apltrx/0346.html>, E. Kossova, L. Kupriianova, B. Potanin (2020) <https://ideas.repec.org/a/ris/apltrx/0391.html> and E. Kossova, B. Potanin (2022) <https://ideas.repec.org/a/ris/apltrx/0455.html>.

Analyzes shooting data with respect to group shape, precision, and accuracy. This includes graphical methods, descriptive statistics, and inference tests using standard, but also non-parametric and robust statistical methods. Implements distributions for radial error in bivariate normal variables. Works with files exported by OnTarget PC/TDS', Silver Mountain e-target, ShotMarker e-target, SIUS e-target, or Taran', as well as with custom data files in text format. Supports inference from range statistics such as extreme spread. Includes a set of web-based graphical user interfaces.

Secure handling of API keys can be difficult. This package provides secure convenience functions for entering / handling API keys and opening connections via inversion of control on those keys. Works seamlessly between production and developer environments.

Used for creating swimmers plots with functions to customize the bars, add points, add lines, add text, and add arrows.

Based on the structure of the SPSS version of the Swiss Household Panel (SHP) data, provides a function seqFromWaves() that seeks the data of variables specified by the user in each of the wave files and collects them as sequences. The function also matches the sequences with variables from other files such as the master files of persons (MP) and households (MH), and social origins (SO). It can also match with activity calendar data (CA).

Spatial versions of Regression Discontinuity Designs (RDDs) are becoming increasingly popular as tools for causal inference. However, conducting state-of-the-art analyses often involves tedious and time-consuming steps. This package offers comprehensive functionalities for executing all required spatial and econometric tasks in a streamlined manner. Moreover, it equips researchers with tools for performing essential placebo and balancing checks comprehensively. The fact that researchers do not have to rely on APIs of external GIS software ensures replicability and raises the standard for spatial RDDs.

Univariate time series forecasting with STL decomposition based Extreme Learning Machine hybrid model. For method details see Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.

Generate the optimal Latin Hypercube Designs (LHDs) for computer experiments with quantitative factors and the optimal Sliced Latin Hypercube Designs (SLHDs) for computer experiments with both quantitative and qualitative factors. Details of the algorithm can be found in Ba, S., Brenneman, W. A. and Myers, W. R. (2015), "Optimal Sliced Latin Hypercube Designs," Technometrics. Important function in this package is "maximinSLHD".

Hierarchical models for the analysis of species-area relationships (SARs) by combining several data sets and covariates; with a global data set combining individual SAR studies; as described in Solymos and Lele (2012) <doi:10.1111/j.1466-8238.2011.00655.x>.

An MCMC algorithm for simultaneous feature selection and classification, and visualization of the selected features and feature interactions. An implementation of SBFC by Krakovna, Du and Liu (2015), <arXiv:1506.02371>.

This package implements the "Residual (Sur)Realism" algorithm described by Stefanski (2007) <doi:10.1198/000313007X190079> to generate datasets that reveal hidden images or messages in their residual plots. It offers both predefined datasets and tools to embed custom text or images into residual structures. Allowing users to create intriguing visual demonstrations for teaching model diagnostics.

This package provides methods to fit robust alternatives to commonly used models used in Small Area Estimation. The methods here used are based on best linear unbiased predictions and linear mixed models. At this time available models include area level models incorporating spatial and temporal correlation in the random effects.

Set of tools aimed at wrapping some of the functionalities of the packages tools, utils and codetools into a nicer format so that an IDE can use them.

Perform variable selection for the spatial Poisson regression model under the adaptive elastic net penalty. Spatial count data with covariates is the input. We use a spatial Poisson regression model to link the spatial counts and covariates. For maximization of the likelihood under adaptive elastic net penalty, we implemented the penalized quasi-likelihood (PQL) and the approximate penalized loglikelihood (APL) methods. The proposed methods can automatically select important covariates, while adjusting for possible spatial correlations among the responses. More details are available in Xie et al. (2018, <arXiv:1809.06418>). The package also contains the Lyme disease dataset, which consists of the disease case data from 2006 to 2011, and demographic data and land cover data in Virginia. The Lyme disease case data were collected by the Virginia Department of Health. The demographic data (e.g., population density, median income, and average age) are from the 2010 census. Land cover data were obtained from the Multi-Resolution Land Cover Consortium for 2006.

This package provides tools for analysing the agreement of two or more rankings of the same items. Examples are importance rankings of predictor variables and risk predictions of subjects. Benchmarks for agreement are computed based on random permutation and bootstrap. See Ekstrøm CT, Gerds TA, Jensen, AK (2018). "Sequential rank agreement methods for comparison of ranked lists." _Biostatistics_, *20*(4), 582-598 <doi:10.1093/biostatistics/kxy017> for more information.

Smooth a sequence of terra rasters using various algorithms (currently moving average, weighted moving average, and exponential smoothing). Also includes wrappers to smooth a vector time-series using these same algorithms. All smoothers use Rcpp implementations for performance.