Jalali calendar, or solar Hijri, is calendar of Iran and Afghanistan (<https://en.wikipedia.org/wiki/Solar_Hijri_calendar>). This package is designed to working with Jalali date. For this purpose, It defines JalaliDate class that is similar to Date class.

Autoencoding Random Forests ('RFAE') provide a method to autoencode mixed-type tabular data using Random Forests ('RF'), which involves projecting the data to a latent feature space of user-chosen dimensionality (usually a lower dimension), and then decoding the latent representations back into the input space. The encoding stage is useful for feature engineering and data visualisation tasks, akin to how principal component analysis ('PCA') is used, and the decoding stage is useful for compression and denoising tasks. At its core, RFAE is a post-processing pipeline on a trained random forest model. This means that it can accept any trained RF of ranger object type: RF', URF or ARF'. Because of this, it inherits Random Forests robust performance and capacity to seamlessly handle mixed-type tabular data. For more details, see Vu et al. (2025) <doi:10.48550/arXiv.2505.21441>.

This package provides functions to estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker.

Mixed Treatment Comparison is a methodology to compare directly and/or indirectly health strategies (drugs, treatments, devices). This package provides an Rcmdr plugin to perform Mixed Treatment Comparison for binary outcome using BUGS code from Bristol University (Lu and Ades).

Ranked set sampling (RSS) is introduced as an advanced method for data collection which is substantial for the statistical and methodological analysis in scientific studies by McIntyre (1952) (reprinted in 2005) <doi:10.1198/000313005X54180>. This package introduces the first package that implements the RSS and its modified versions for sampling. With RSSampling', the researchers can sample with basic RSS and the modified versions, namely, Median RSS, Extreme RSS, Percentile RSS, Balanced groups RSS, Double RSS, L-RSS, Truncation-based RSS, Robust extreme RSS. The RSSampling also allows imperfect ranking using an auxiliary variable (concomitant) which is widely used in the real life applications. Applicants can also use this package for parametric and nonparametric inference such as mean, median and variance estimation, regression analysis and some distribution-free tests where the the samples are obtained via basic RSS.

Download and import agricultural data from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) <https://www.agriculture.gov.au/abares> and Australian Bureau of Statistics (ABS) <https://www.abs.gov.au>. Data types serviced include spreadsheets, comma separated value (CSV) files, geospatial data including shape files and geotiffs covering topics including broadacre crops, livestock, soil data, commodities and more. Unifies field names and formats for data interoperability making analysis easier by standardising names between data formats. Also simplifies importing geospatial data as well as correcting issues in the geospatial data upon import.

This package implements safe policy learning under regression discontinuity designs with multiple cutoffs, based on Zhang et al. (2022) <doi:10.48550/arXiv.2208.13323>. The learned cutoffs are guaranteed to perform no worse than the existing cutoffs in terms of overall outcomes. The rdlearn package also includes features for visualizing the learned cutoffs relative to the baseline and conducting sensitivity analyses.

This package provides access to global river gauge data from a variety of national-level river agencies. The package interfaces with the national-level agency websites to provide access to river gauge locations, river discharge, and river stage. Currently, the package is available for the following countries: Australia, Brazil, Canada, Chile, France, Japan, South Africa, the United Kingdom, and the United States.

Focused on linear, quadratic and cubic regression models, it has a function for calculating the models, obtaining a list with their parameters, and a function for making the graphs for the respective models.

This package provides functions to construct efficient row-column designs for 3-level factorial experiments in 3 rows. The designs ensure the estimation of all main effects (full efficiency) and two factor interactions in minimum replications. For more details, see Dey, A. and Mukerjee, R. (2012) <doi:10.1016/j.spl.2012.06.014> and Dash, S., Parsad, R., and Gupta, V. K. (2013) <doi:10.1007/s40003-013-0059-5>.

This package provides an easy way to report the results of regression analysis, including: 1. Proportional hazards regression from function coxph of package survival'; 2. Conditional logistic regression from function clogit of package survival'; 3. Ordered logistic regression from function polr of package MASS'; 4. Binary logistic regression from function glm of package stats'; 5. Linear regression from function lm of package stats'; 6. Risk regression model for survival analysis with competing risks from function FGR of package riskRegression'; 7. Multilevel model from function lme of package nlme'.

Adds menu items for case 2 (profile case) best-worst scaling (BWS2) to the R Commander. BWS2 is a question-based survey method that constructs profiles (combinations of attribute levels) using an orthogonal array, asks respondents to select the best and worst levels in each profile, and measures preferences for attribute levels by analyzing the responses. For details, see Aizaki and Fogarty (2019) <doi:10.1016/j.jocm.2019.100171>.

This package provides a simple set of wrappers to easily use RDCOMClient for generating Microsoft PowerPoint presentations. Warning:this package is soon to be archived from CRAN.

This header-only library provides modern, portable C++ wrappers for SIMD intrinsics and parallelized, optimized math implementations (SSE, AVX, NEON, AVX512). By placing this library in this package, we offer an efficient distribution system for Xsimd <https://github.com/xtensor-stack/xsimd> for R packages using CRAN.

This package provides a very lightweight package that writes out log messages in an opinionated way. Simpler and lighter than other logging packages, rlog provides a compact feature set that focuses on getting the job done in a Unix-like way.

This package provides a pure R implementation of the median cut algorithm. Extracts the dominant colors from an image, and turns them into a scale for use in plots or for fun!

Connect, query, and operate on information available from the Open Source Vulnerability database <https://osv.dev/>. Although CRAN has vulnerabilities listed, these are few compared to projects such as PyPI'. With tighter integration between R and Python', having an R specific package to access details about vulnerabilities from various sources is a worthwhile enterprise.

Communications simulation package supporting forward error correction.

This package provides a native R implementation for encoding and decoding sixel graphics (<https://vt100.net/docs/vt3xx-gp/chapter14.html>), and a dedicated sixel graphics device that allows plots to be rendered directly within compatible terminal emulators.

Computes confidence intervals for binomial or Poisson rates and their differences or ratios. Including the rate (or risk) difference ('RD') or rate ratio (or relative risk, RR') for binomial proportions or Poisson rates, and odds ratio ('OR', binomial only). Also confidence intervals for RD, RR or OR for paired binomial data, and estimation of a proportion from clustered binomial data. Includes skewness-corrected asymptotic score ('SCAS') methods, which have been developed in Laud (2017) <doi:10.1002/pst.1813> from Miettinen and Nurminen (1985) <doi:10.1002/sim.4780040211> and Gart and Nam (1988) <doi:10.2307/2531848>, and in Laud (2025, under review) for paired proportions. The same score produces hypothesis tests that are improved versions of the non-inferiority test for binomial RD and RR by Farrington and Manning (1990) <doi:10.1002/sim.4780091208>, or a generalisation of the McNemar test for paired data. The package also includes MOVER methods (Method Of Variance Estimates Recovery) for all contrasts, derived from the Newcombe method but with options to use equal-tailed intervals in place of the Wilson score method, and generalised for Bayesian applications incorporating prior information. So-called exact methods for strictly conservative coverage are approximated using continuity adjustments, and the amount of adjustment can be selected to avoid over-conservative coverage. Also includes methods for stratified calculations (e.g. meta-analysis), either with fixed effect assumption (matching the CMH test) or incorporating stratum heterogeneity.

This package provides and extends the Fuzzy Coco algorithm by wrapping the FuzzyCoCo C++ Library, cf <https://github.com/Lonza-RND-Data-Science/fuzzycoco>. Fuzzy Coco constructs systems that predict the outcome of a human decision-making process while providing an understandable explanation of a possible reasoning leading to it. The constructed fuzzy systems are composed of rules and linguistic variables. This package provides a S3 classic interface (fit_xy()/fit()/predict()/evaluate()) and a tidymodels'/'parsnip interface, a custom engine with custom iteration stop criterion and progress bar support as well as a systematic implementation that do not rely on genetic programming but rather explore all possible combinations.

This package provides access to the Ravelry API <https://www.ravelry.com/groups/ravelry-api>. An R wrapper for pulling data from Ravelry.com', an organizational tool for crocheters, knitters, spinners, and weavers. You can retrieve pattern, yarn, author, and shop information by search or by a given id.

Sequential permutation testing for statistical significance of predictors in random forests and other prediction methods. The main function of the package is rfvimptest(), which allows to test for the statistical significance of predictors in random forests using different (sequential) permutation test strategies [1]. The advantage of sequential over conventional permutation tests is that they are computationally considerably less intensive, as the sequential procedure is stopped as soon as there is sufficient evidence for either the null or the alternative hypothesis. Reference: [1] Hapfelmeier, A., Hornung, R. & Haller, B. (2023) Efficient permutation testing of variable importance measures by the example of random forests. Computational Statistics & Data Analysis 181:107689, <doi:10.1016/j.csda.2022.107689>.

Generate causally-simulated data to serve as ground truth for evaluating methods in causal discovery and effect estimation. The package provides tools to assist in defining functions based on specified edges, and conversely, defining edges based on functions. It enables the generation of data according to these predefined functions and causal structures. This is particularly useful for researchers in fields such as artificial intelligence, statistics, biology, medicine, epidemiology, economics, and social sciences, who are developing a general or a domain-specific methods to discover causal structures and estimate causal effects. Data simulation adheres to principles of structural causal modeling. Detailed methodologies and examples are documented in our vignette, available at <https://htmlpreview.github.io/?https://github.com/herdiantrisufriyana/rcausim/blob/master/doc/causal_simulation_exemplar.html>.