Builds, evaluates and validates a nomogram with survey data and right-censored outcomes. As described in Capanu (2015) <doi:10.18637/jss.v064.c01>, the package contains functions to create the nomogram, validate it using bootstrap, as well as produce the calibration plots.

Visualization and analysis of spatially resolved transcriptomics data. The spatialGE R package provides methods for visualizing and analyzing spatially resolved transcriptomics data, such as 10X Visium, CosMx, or csv/tsv gene expression matrices. It includes tools for spatial interpolation, autocorrelation analysis, tissue domain detection, gene set enrichment, and differential expression analysis using spatial mixed models.

This package provides a covariance estimator for multivariate normal data that is sparse and positive definite. Implements the majorize-minimize algorithm described in Bien, J., and Tibshirani, R. (2011), "Sparse Estimation of a Covariance Matrix," Biometrika. 98(4). 807--820.

This package provides tools for manipulating sound files for bioacoustic analysis, and preparing analyses these for publication. The package validates that values are physically possible wherever feasible.

This package implements a sequential imputation framework using Bayesian Mixed-Effects Trees ('SBMTrees') for handling missing data in longitudinal studies. The package supports a variety of models, including non-linear relationships and non-normal random effects and residuals, leveraging Dirichlet Process priors for increased flexibility. Key features include handling Missing at Random (MAR) longitudinal data, imputation of both covariates and outcomes, and generating posterior predictive samples for further analysis. The methodology is designed for applications in epidemiology, biostatistics, and other fields requiring robust handling of missing data in longitudinal settings.

This package contains functions that help to determine event boundaries in event segmentation experiments by bootstrapping a critical segmentation magnitude under the null hypothesis that all key presses were randomly distributed across the experiment. Segmentation magnitude is defined as the sum of Gaussians centered at the times of the segmentation key presses performed by the participants. Within a participant, the maximum of the overlaid Gaussians is used to prevent an excessive influence of a single participant on the overall outcome (e.g. if a participant is pressing the key multiple times in succession). Further functions are included, such as plotting the results.

Identifies individuals in a social network who should be the intervention subjects for a network intervention in which you have a group of targets, a group of avoiders, and a group that is neither.

This package provides routines for scoring behavioral questionnaires. Includes scoring procedures for the International Physical Activity Questionnaire (IPAQ) <http://www.ipaq.ki.se>. Compares physical functional performance to the age- and gender-specific normal ranges.

Model age schedules of mortality, nqx, suitable for a life table. This package implements the SVD-Comp mortality model indexed by either child or child/adult mortality. Given input value(s) of either 5q0 or (5q0, 45q15), the qx() function generates single-year 1qx or 5-year 5qx conditional age-specific probabilities of dying. See Clark (2016) <doi:10.48550/arXiv.1612.01408> and Clark (2019) <doi:10.1007/s13524-019-00785-3>.

This data-driven phylogenetic comparative method fits stabilizing selection models to continuous trait data, building on the ouch methodology of Butler and King (2004) <doi:10.1086/426002>. The main functions fit a series of Hansen models using stepwise AIC, then identify cases of convergent evolution where multiple lineages have shifted to the same adaptive peak. For more information see Ingram and Mahler (2013) <doi:10.1111/2041-210X.12034>.

This package provides functions to perform the Sequential Probability Ratio Test (SPRT) for hypothesis testing in Binomial, Poisson and Normal distributions. The package allows users to specify Type I and Type II error probabilities, decision thresholds, and compare null and alternative hypotheses sequentially as data accumulate. It includes visualization tools for plotting the likelihood ratio path and decision boundaries, making it easier to interpret results. The methods are based on Wald (1945) <doi:10.1214/aoms/1177731118>, who introduced the SPRT as one of the earliest and most powerful sequential analysis techniques. This package is useful in quality control, clinical trials, and other applications requiring early decision-making.The term SPRT is an abbreviation and used intentionally.

Non-parametric test, originally proposed by Stute (1997) <https://www.jstor.org/stable/2242560>, that the expectation of a dependent variable Y given an independent variable D is linear in D.

Sejong(http://www.sejong.or.kr/) corpus and Hannanum(http://semanticweb.kaist.ac.kr/home/index.php/HanNanum) dictionaries for KoNLP.

This package provides tools for analyzing tail dependence in any sample or in particular theoretical models. The package uses only theoretical and non parametric methods, without inference. The primary goals of the package are to provide: (a)symmetric multivariate extreme value models in any dimension; theoretical and empirical indices to order tail dependence; theoretical and empirical graphical methods to visualize tail dependence.

Test and estimates of location, tests of independence, tests of sphericity and several estimates of shape all based on spatial signs, symmetrized signs, ranks and signed ranks. For details, see Oja and Randles (2004) <doi:10.1214/088342304000000558> and Oja (2010) <doi:10.1007/978-1-4419-0468-3>.

Extends the functionality of the package Synth as detailed in Abadie, Diamond, and Hainmueller (2011) <doi:10.18637/jss.v042.i13>. Includes generating and plotting placebos, post/pre-MSPE (Mean Squared Prediction Error) significance tests and plots, and calculating average treatment effects for multiple treated units.

Cleans and formats language transcripts guided by a series of transformation options (e.g., lemmatize words, omit stopwords, split strings across rows). SemanticDistance computes two distinct metrics of cosine semantic distance (experiential and embedding). These values reflect pairwise cosine distance between different elements or chunks of a language sample. SemanticDistance can process monologues (e.g., stories, ordered text), dialogues (e.g., conversation transcripts), word pairs arrayed in columns, and unordered word lists. Users specify options for how they wish to chunk distance calculations. These options include: rolling ngram-to-word distance (window of n-words to each new word), ngram-to-ngram distance (2-word chunk to the next 2-word chunk), pairwise distance between words arrayed in columns, matrix comparisons (i.e., all possible pairwise distances between words in an unordered list), turn-by-turn distance (talker to talker in a dialogue transcript). SemanticDistance includes visualization options for analyzing distances as time series data and simple semantic network dynamics (e.g., clustering, undirected graph network).

Streamlines geographic data transformation, storage and publication, simplifying data preparation and enhancing interoperability across formats and platforms.

Calculates sample size for various scenarios, such as sample size to estimate population proportion with stated absolute or relative precision, testing a single proportion with a reference value, to estimate the population mean with stated absolute or relative precision, testing single mean with a reference value and sample size for comparing two unpaired or independent means, comparing two paired means, the sample size For case control studies, estimating the odds ratio with stated precision, testing the odds ratio with a reference value, estimating relative risk with stated precision, testing relative risk with a reference value, testing a correlation coefficient with a specified value, etc. <https://www.academia.edu/39511442/Adequacy_of_Sample_Size_in_Health_Studies#:~:text=Determining%20the%20sample%20size%20for,may%20yield%20statistically%20inconclusive%20results.>.

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.

When comparing single cases to control populations and no parameters are known researchers and clinicians must estimate these with a control sample. This is often done when testing a case's abnormality on some variable or testing abnormality of the discrepancy between two variables. Appropriate frequentist and Bayesian methods for doing this are here implemented, including tests allowing for the inclusion of covariates. These have been developed first and foremost by John Crawford and Paul Garthwaite, e.g. in Crawford and Howell (1998) <doi:10.1076/clin.12.4.482.7241>, Crawford and Garthwaite (2005) <doi:10.1037/0894-4105.19.3.318>, Crawford and Garthwaite (2007) <doi:10.1080/02643290701290146> and Crawford, Garthwaite and Ryan (2011) <doi:10.1016/j.cortex.2011.02.017>. The package is also equipped with power calculators for each method.

This package provides a pipeline to perform small area estimation and prevalence mapping of binary indicators using health and demographic survey data, described in Fuglstad et al. (2022) <doi:10.48550/arXiv.2110.09576> and Wakefield et al. (2020) <doi:10.1111/insr.12400>.

Fast, lightweight toolkit for data splitting. Data sets can be partitioned into disjoint groups (e.g. into training, validation, and test) or into (repeated) k-folds for subsequent cross-validation. Besides basic splits, the package supports stratified, grouped as well as blocked splitting. Furthermore, cross-validation folds for time series data can be created. See e.g. Hastie et al. (2001) <doi:10.1007/978-0-387-84858-7> for the basic background on data partitioning and cross-validation.

Explore and analyse the genealogy of textual or musical traditions, from their variants, with various stemmatological methods, mainly the disagreement-based algorithms suggested by Camps and Cafiero (2015) <doi:10.1484/M.LECTIO-EB.5.102565>.