This package provides tools for extracting word and phrase frequencies from the Child Language Data Exchange System (CHILDES) database via the childesr API. Supports type-level word counts, token-mode searches with simple wildcard patterns and part-of-speech filters, optional stemming, and Zipf-scaled frequencies. Provides normalization per number of tokens or utterances, speaker-role breakdowns, dataset summaries, and export to Excel workbooks for reproducible child language research. The CHILDES database is maintained at <https://talkbank.org/childes/>.

This package provides a comprehensive API for colour conversion between popular colour spaces ('RGB', HSL', OKLab', OKLch', hex', and named colours) along with clean, modern CSS Color Level 4 syntax output. Integrates seamlessly into Shiny and Quarto workflows. Includes nearest colour name lookup powered by a curated database of over 30,000 colour names. OKLab'/'OKLCh colour spaces are described in Ottosson (2020) <https://bottosson.github.io/posts/oklab/>. CSS Color Level 4 syntax follows the W3C specification <https://www.w3.org/TR/css-color-4/>.

This package provides a collection of functions that make it easier to understand crime (or other) data, and assist others in understanding it. The package helps you read data from various sources, clean it, fix column names, and graph the data.

Computes marginal conformal p-values using conformal prediction in binary classification tasks. Conformal prediction is a framework that augments machine learning algorithms with a measure of uncertainty, in the form of prediction regions that attain a user-specified level of confidence. This package specifically focuses on providing conformal p-values that can be used to assess the confidence of the classification predictions. For more details, see Tyagi and Guo (2023) <https://proceedings.mlr.press/v204/tyagi23a.html>.

Finds the most likely originating tissue(s) and developmental stage(s) of tissue-specific RNA sequencing data. The package identifies both pure transcriptomes and mixtures of transcriptomes. The most likely identity is found through comparisons of the sequencing data with high-throughput in situ hybridisation patterns. Typical uses are the identification of cancer cell origins, validation of cell culture strain identities, validation of single-cell transcriptomes, and validation of identity and purity of flow-sorting and dissection sequencing products.

Users can declare causal models over binary nodes, update beliefs about causal types given data, and calculate arbitrary queries. Updating is implemented in stan'. See Humphreys and Jacobs, 2023, Integrated Inferences (<DOI: 10.1017/9781316718636>) and Pearl, 2009 Causality (<DOI:10.1017/CBO9780511803161>).

Supports analysis of trends in climate change, ecological and crop modelling.

Identification and visualization of groups of closely spaced mutations in the DNA sequence of cancer genome. The extremely mutated zones are searched in the symmetric dissimilarity matrix using the anti-Robinson matrix properties. Different data sets are obtained to describe and plot the clustered mutations information.

This package implements cointegration/co-trending rank selection algorithm in Guo and Shintani (2013) "Consistent co-trending rank selection when both stochastic and nonlinear deterministic trends are present". The Econometrics Journal 16: 473-483 <doi:10.1111/j.1368-423X.2012.00392.x>. Numbered examples correspond to Feb 2011 preprint <http://www.fas.nus.edu.sg/ecs/events/seminar/seminar-papers/05Apr11.pdf>.

Utilize the shiny interface to generate Goodness of Fit (GOF) plots and tables for Non-Linear Mixed Effects (NLME / NONMEM) pharmacometric models. From the interface, users can customize model diagnostics and generate the underlying R code to reproduce the diagnostic plots and tables outside of the shiny session. Model diagnostics can be included in a rmarkdown document and rendered to desired output format.

This package provides a tool to easily run and visualise supervised and unsupervised state of the art customer segmentation. It is built like a pipeline covering the 3 main steps in a segmentation project: pre-processing, modelling, and plotting. Users can either run the pipeline as a whole, or choose to run any one of the three individual steps. It is equipped with a supervised option (tree optimisation) and an unsupervised option (k-clustering) as default models.

This package provides a function that performs the adaptive mean shift algorithm for individual tree crown delineation in 3D point clouds as proposed by Ferraz et al. (2016) <doi:10.1016/j.rse.2016.05.028>, as well as supporting functions.

Automatic specification and estimation of reserve demand curves for central bank operations. The package can help to choose the best demand curve and identify additional explanatory variables. Various plot and predict options are included. For more details, see Chen et al. (2023) <https://www.imf.org/en/Publications/WP/Issues/2023/09/01/Modeling-the-Reserve-Demand-to-Facilitate-Central-Bank-Operations-538754>.

This package contains 3 maps. 1) US States 2) US Counties 3) Countries of the world.

It helps in development of a principal component analysis based composite index by assigning weights to variables and combining the weighted variables. For method details see Sendhil, R., Jha, A., Kumar, A. and Singh, S. (2018). <doi:10.1016/j.ecolind.2018.02.053>, and Wu, T. (2021). <doi:10.1016/j.ecolind.2021.108006>.

Encrypts and decrypts strings using either the Caesar cipher or a pseudorandom number generation (using set.seed()) method.

Intended to analyse recordings from multiple microphones (e.g., backpack microphones in captive setting). It allows users to align recordings even if there is non-linear drift of several minutes between them. A call detection and assignment pipeline can be used to find vocalisations and assign them to the vocalising individuals (even if the vocalisation is picked up on multiple microphones). The tracing and measurement functions allow for detailed analysis of the vocalisations and filtering of noise. Finally, the package includes a function to run spectrographic cross correlation, which can be used to compare vocalisations. It also includes multiple other functions related to analysis of vocal behaviour.

Correcting area under ROC (AUC) for measurement error based on probit-shift model.

Calculate confidence and consistency that measure the goodness-of-fit and transferability of predictive/potential distribution models (including species distribution models) as described by Somodi & Bede-Fazekas et al. (2024) <doi:10.1016/j.ecolmodel.2024.110667>.

This package provides a tool for transforming coordinates in a color space to common color names using data from the Royal Horticultural Society and the International Union for the Protection of New Varieties of Plants.

CLUster Evaluation (CLUE) is a computational method for identifying optimal number of clusters in a given time-course dataset clustered by cmeans or kmeans algorithms and subsequently identify key kinases or pathways from each cluster. Its implementation in R is called ClueR. See README on <https://github.com/PYangLab/ClueR> for more details. P Yang et al. (2015) <doi:10.1371/journal.pcbi.1004403>.

This package provides methods for powering cluster-randomized trials with two continuous co-primary outcomes using five key design techniques. Includes functions for calculating required sample size and statistical power. For more details on methodology, see Owen et al. (2025) <doi:10.1002/sim.70015>, Yang et al. (2022) <doi:10.1111/biom.13692>, Pocock et al. (1987) <doi:10.2307/2531989>, Vickerstaff et al. (2019) <doi:10.1186/s12874-019-0754-4>, and Li et al. (2020) <doi:10.1111/biom.13212>.

Comprehensive data analysis software, and the name "cg" stands for "compare groups." Its genesis and evolution are driven by common needs to compare administrations, conditions, etc. in medicine research and development. The current version provides comparisons of unpaired samples, i.e. a linear model with one factor of at least two levels. It also provides comparisons of two paired samples. Good data graphs, modern statistical methods, and useful displays of results are emphasized.

Estimation, based on conditional maximum likelihood, of the quadratic exponential model proposed by Bartolucci, F. & Nigro, V. (2010, Econometrica) <DOI:10.3982/ECTA7531> and of a simplified and a modified version of this model. The quadratic exponential model is suitable for the analysis of binary longitudinal data when state dependence (further to the effect of the covariates and a time-fixed individual intercept) has to be taken into account. Therefore, this is an alternative to the dynamic logit model having the advantage of easily allowing conditional inference in order to eliminate the individual intercepts and then getting consistent estimates of the parameters of main interest (for the covariates and the lagged response). The simplified version of this model does not distinguish, as the original model does, between the last time occasion and the previous occasions. The modified version formulates in a different way the interaction terms and it may be used to test in a easy way state dependence as shown in Bartolucci, F., Nigro, V. & Pigini, C. (2018, Econometric Reviews) <DOI:10.1080/07474938.2015.1060039>. The package also includes estimation of the dynamic logit model by a pseudo conditional estimator based on the quadratic exponential model, as proposed by Bartolucci, F. & Nigro, V. (2012, Journal of Econometrics) <DOI:10.1016/j.jeconom.2012.03.004>. For large time dimensions of the panel, the computation of the proposed models involves a recursive function from Krailo M. D., & Pike M. C. (1984, Journal of the Royal Statistical Society. Series C (Applied Statistics)) and Bartolucci F., Valentini, F. & Pigini C. (2021, Computational Economics <DOI:10.1007/s10614-021-10218-2>.