This is a simple addin to RStudio that finds all TODO', FIX ME', CHANGED etc. comments in your project and shows them as a markers list.

We provide a tidy grammar of population genetics, facilitating the manipulation and analysis of data on biallelic single nucleotide polymorphisms (SNPs). tidypopgen scales to very large genetic datasets by storing genotypes on disk, and performing operations on them in chunks, without ever loading all data in memory. The full functionalities of the package are described in Carter et al. (2025) <doi:10.1111/2041-210x.70204>.

An interface to the mclust package to easily carry out latent profile analysis ("LPA"). Provides functionality to estimate commonly-specified models. Follows a tidy approach, in that output is in the form of a data frame that can subsequently be computed on. Also has functions to interface to the commercial MPlus software via the MplusAutomation package.

This package provides tools for computing various vector summaries of persistence diagrams studied in Topological Data Analysis. For improved computational efficiency, all code for the vector summaries is written in C++ using the Rcpp and RcppArmadillo packages.

This package provides tools for translating environmental change into organismal response. Microclimate models to vertically scale weather station data to organismal heights. The biophysical modeling tools include both general models for heat flows and specific models to predict body temperatures for a variety of ectothermic taxa. Additional functions model and temporally partition air and soil temperatures and solar radiation. Utility functions estimate the organismal and environmental parameters needed for biophysical ecology. TrenchR focuses on relatively simple and modular functions so users can create transparent and flexible biophysical models. Many functions are derived from Gates (1980) <doi:10.1007/978-1-4612-6024-0> and Campbell and Norman (1988) <isbn:9780387949376>.

This package provides functions for the selection of thresholds for use in extreme value models, based mainly on the methodology in Northrop, Attalides and Jonathan (2017) <doi:10.1111/rssc.12159>. It also performs predictive inferences about future extreme values, based either on a single threshold or on a weighted average of inferences from multiple thresholds, using the revdbayes package <https://cran.r-project.org/package=revdbayes>. At the moment only the case where the data can be treated as independent identically distributed observations is considered.

This package provides a teal_data class as a unified data model for teal applications focusing on reproducibility and relational data.

Access and manipulate spatial tracking data, with straightforward coercion from and to other formats. Filter for speed and create time spent maps from tracking data. There are coercion methods to convert between trip and ltraj from adehabitatLT', and between trip and psp and ppp from spatstat'. Trip objects can be created from raw or grouped data frames, and from types in the sp', sf', amt', trackeR', mousetrap', and other packages, Sumner, MD (2011) <https://figshare.utas.edu.au/articles/thesis/The_tag_location_problem/23209538>.

This package provides a user-friendly R data package that is intended to make Turkish higher education statistics more accessible.

Consolidates and calculates different sets of time-series features from multiple R and Python packages including Rcatch22 Henderson, T. (2021) <doi:10.5281/zenodo.5546815>, feasts O'Hara-Wild, M., Hyndman, R., and Wang, E. (2021) <https://CRAN.R-project.org/package=feasts>, tsfeatures Hyndman, R., Kang, Y., Montero-Manso, P., Talagala, T., Wang, E., Yang, Y., and O'Hara-Wild, M. (2020) <https://CRAN.R-project.org/package=tsfeatures>, tsfresh Christ, M., Braun, N., Neuffer, J., and Kempa-Liehr A.W. (2018) <doi:10.1016/j.neucom.2018.03.067>, TSFEL Barandas, M., et al. (2020) <doi:10.1016/j.softx.2020.100456>, and Kats Facebook Infrastructure Data Science (2021) <https://facebookresearch.github.io/Kats/>.

Transport theory has seen much success in many fields of statistics and machine learning. We provide a variety of algorithms to compute Wasserstein distance, barycenter, and others. See Peyré and Cuturi (2019) <doi:10.1561/2200000073> for the general exposition to the study of computational optimal transport.

Method to estimate the effect of the trend in predictor variables on the observed trend of the response variable using mixed models with temporal autocorrelation. See Fernández-Martà nez et al. (2017 and 2019) <doi:10.1038/s41598-017-08755-8> <doi:10.1038/s41558-018-0367-7>.

To provide a high dimensional grouped variable selection approach for detection of whole-genome SNP effects and SNP-SNP interactions, as described in Fang et al. (2017, under review).

Make it easy to deal with multiple cross-tables in data exploration, by creating them, manipulating them, and adding color helpers to highlight important informations (differences from totals, comparisons between lines or columns, contributions to variance, confidence intervals, odds ratios, etc.). All functions are pipe-friendly and render data frames which can be easily manipulated. In the same time, time-taking operations are done with data.table to go faster with big dataframes. Tables can be exported with formats and colors to Excel', plot and html.

Build customized transfer function and ARIMA models with multiple operators and parameter restrictions. Provides tools for model identification, estimation using exact or conditional maximum likelihood, diagnostic checking, automatic outlier detection, calendar effects, forecasting, and seasonal adjustment. The new version also supports unobserved component ARIMA model specification and estimation for structural time series analysis.

This package implements a decomposition of the two-way fixed effects instrumental variable estimator into all possible Wald difference-in-differences estimators. Provides functions to summarize the contribution of different cohort comparisons to the overall two-way fixed effects instrumental variable estimate, with or without controls. The method is described in Miyaji (2024) <doi:10.48550/arXiv.2405.16467>.

An implementation of tidy speaker vowel normalization. This includes generic functions for defining new normalization methods for points, formant tracks, and Discrete Cosine Transform coefficients, as well as convenience functions implementing established normalization methods. References for the implemented methods are: Johnson, Keith (2020) <doi:10.5334/labphon.196> Lobanov, Boris (1971) <doi:10.1121/1.1912396> Nearey, Terrance M. (1978) <https://sites.ualberta.ca/~tnearey/Nearey1978_compressed.pdf> Syrdal, Ann K., and Gopal, H. S. (1986) <doi:10.1121/1.393381> Watt, Dominic, and Fabricius, Anne (2002) <https://www.latl.leeds.ac.uk/article/evaluation-of-a-technique-for-improving-the-mapping-of-multiple-speakers-vowel-spaces-in-the-f1-f2-plane/>.

Computes the solution path of the Terminating-LARS (T-LARS) algorithm. The T-LARS algorithm is a major building block of the T-Rex selector (see R package TRexSelector'). The package is based on the papers Machkour, Muma, and Palomar (2022) <arXiv:2110.06048>, Efron, Hastie, Johnstone, and Tibshirani (2004) <doi:10.1214/009053604000000067>, and Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>.

Evaluate inline or chunks of R code in template files and replace with their output modifying the resulting template.

Custom template and output formats for use with rmarkdown. Produce Edward Tufte-style handouts in html formats with full support for rmarkdown features.

This package provides a robust implementation of Topolow algorithm. It embeds objects into a low-dimensional Euclidean space from a matrix of pairwise dissimilarities, even when the data do not satisfy metric or Euclidean axioms. The package is particularly well-suited for sparse, incomplete, and censored (thresholded) datasets such as antigenic relationships. The core is a physics-inspired, gradient-free optimization framework that models objects as particles in a physical system, where observed dissimilarities define spring rest lengths and unobserved pairs exert repulsive forces. The package also provides functions specific to antigenic mapping to transform cross-reactivity and binding affinity measurements into accurate spatial representations in a phenotype space. Key features include: * Robust Embedding from Sparse Data: Effectively creates complete and consistent maps (in optimal dimensions) even with high proportions of missing data (e.g., >95%). * Physics-Inspired Optimization: Models objects (e.g., antigens, landmarks) as particles connected by springs (for measured dissimilarities) and subject to repulsive forces (for missing dissimilarities), and simulates the physical system using laws of mechanics, reducing the need for complex gradient computations. * Automatic Dimensionality Detection: Employs a likelihood-based approach to determine the optimal number of dimensions for the embedding/map, avoiding distortions common in methods with fixed low dimensions. * Noise and Bias Reduction: Naturally mitigates experimental noise and bias through its network-based, error-dampening mechanism. * Antigenic Velocity Calculation (for antigenic data): Introduces and quantifies "antigenic velocity," a vector that describes the rate and direction of antigenic drift for each pathogen isolate. This can help identify cluster transitions and potential lineage replacements. * Broad Applicability: Analyzes data from various objects that their dissimilarity may be of interest, ranging from complex biological measurements such as continuous and relational phenotypes, antibody-antigen interactions, and protein folding to abstract concepts, such as customer perception of different brands. Methods are described in the context of bioinformatics applications in Arhami and Rohani (2025a) <doi:10.1093/bioinformatics/btaf372>, and mathematical proofs and Euclidean embedding details are in Arhami and Rohani (2025b) <doi:10.48550/arXiv.2508.01733>.

Description: Implementation of topological data analysis methods based on graph-theoretic approaches for discovering topological structures in data. The core algorithm constructs topological spaces from graphs following Nada et al. (2018) <doi:10.1002/mma.4726> "New types of topological structures via graphs".

Cluster data without specifying the number of clusters using the Table Invitation Prior (TIP) introduced in the paper "Clustering Gene Expression Using the Table Invitation Prior" by Charles W. Harrison, Qing He, and Hsin-Hsiung Huang (2022) <doi:10.3390/genes13112036>. TIP is a Bayesian prior that uses pairwise distance and similarity information to cluster vectors, matrices, or tensors.