In a clinical trial, it frequently occurs that the most credible outcome to evaluate the effectiveness of a new therapy (the true endpoint) is difficult to measure. In such a situation, it can be an effective strategy to replace the true endpoint by a (bio)marker that is easier to measure and that allows for a prediction of the treatment effect on the true endpoint (a surrogate endpoint). The package Surrogate allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks. Part of this software has been developed using funding provided from the European Union's Seventh Framework Programme for research, technological development and demonstration (Grant Agreement no 602552), the Special Research Fund (BOF) of Hasselt University (BOF-number: BOF2OCPO3), GlaxoSmithKline Biologicals, Baekeland Mandaat (HBC.2022.0145), and Johnson & Johnson Innovative Medicine.

Drafting an epidemiological report in Microsoft Word format for a given disease, similar to the Annual Epidemiological Reports published by the European Centre for Disease Prevention and Control. Through standalone functions, it is specifically designed to generate each disease specific output presented in these reports and includes: - Table with the distribution of cases by Member State over the last five years; - Seasonality plot with the distribution of cases at the European Union / European Economic Area level, by month, over the past five years; - Trend plot with the trend and number of cases at the European Union / European Economic Area level, by month, over the past five years; - Age and gender bar graph with the distribution of cases at the European Union / European Economic Area level. Two types of datasets can be used: - The default dataset of dengue 2015-2019 data; - Any dataset specified as described in the vignette.

Allows calculating global scores for characteristics of visual stimuli as assessed by human raters. Stimuli are presented as sequence of pairwise comparisons ('contests'), during each of which a rater expresses preference for one stimulus over the other (forced choice). The algorithm for calculating global scores is based on Elo rating, which updates individual scores after each single pairwise contest. Elo rating is widely used to rank chess players according to their performance. Its core feature is that dyadic contests with expected outcomes lead to smaller changes of participants scores than outcomes that were unexpected. As such, Elo rating is an efficient tool to rate individual stimuli when a large number of such stimuli are paired against each other in the context of experiments where the goal is to rank stimuli according to some characteristic of interest. Clark et al (2018) <doi:10.1371/journal.pone.0190393> provide details.

Most common exact, asymptotic and resample based tests are provided for testing the homogeneity of variances of k normal distributions under normality. These tests are Barlett, Bhandary & Dai, Brown & Forsythe, Chang et al., Gokpinar & Gokpinar, Levene, Liu and Xu, Gokpinar. Also, a data generation function from multiple normal distribution is provided using any multiple normal parameters. Bartlett, M. S. (1937) <doi:10.1098/rspa.1937.0109> Bhandary, M., & Dai, H. (2008) <doi:10.1080/03610910802431011> Brown, M. B., & Forsythe, A. B. (1974).<doi:10.1080/01621459.1974.10482955> Chang, C. H., Pal, N., & Lin, J. J. (2017) <doi:10.1080/03610918.2016.1202277> Gokpinar E. & Gokpinar F. (2017) <doi:10.1080/03610918.2014.955110> Liu, X., & Xu, X. (2010) <doi:10.1016/j.spl.2010.05.017> Levene, H. (1960) <https://cir.nii.ac.jp/crid/1573950400526848896> Gökpınar, E. (2020) <doi:10.1080/03610918.2020.1800037>.

Simply and efficiently simulates (i) variants from reference genomes and (ii) reads from both Illumina <https://www.illumina.com/> and Pacific Biosciences (PacBio) <https://www.pacb.com/> platforms. It can either read reference genomes from FASTA files or simulate new ones. Genomic variants can be simulated using summary statistics, phylogenies, Variant Call Format (VCF) files, and coalescent simulationsâ the latter of which can include selection, recombination, and demographic fluctuations. jackalope can simulate single, paired-end, or mate-pair Illumina reads, as well as PacBio reads. These simulations include sequencing errors, mapping qualities, multiplexing, and optical/polymerase chain reaction (PCR) duplicates. Simulating Illumina sequencing is based on ART by Huang et al. (2012) <doi:10.1093/bioinformatics/btr708>. PacBio sequencing simulation is based on SimLoRD by Stöcker et al. (2016) <doi:10.1093/bioinformatics/btw286>. All outputs can be written to standard file formats.

The inference in multi-state models is traditionally performed under a Markov assumption that claims that past and future of the process are independent given the present state. In this package, we consider tests of the Markov assumption that are applicable to general multi-state models. Three approaches using existing methodology are considered: a simple method based on including covariates depending on the history in Cox models for the transition intensities; methods based on measuring the discrepancy of the non-Markov estimators of the transition probabilities to the Markov Aalen-Johansen estimators; and, finally, methods that were developed by considering summaries from families of log-rank statistics where patients are grouped by the state occupied of the process at a particular time point (see Soutinho G, Meira-Machado L (2021) <doi:10.1007/s00180-021-01139-7> and Titman AC, Putter H (2020) <doi:10.1093/biostatistics/kxaa030>).

This package performs various statistical transformations; Box-Cox and Log (Box and Cox, 1964) <doi:10.1111/j.2517-6161.1964.tb00553.x>, Glog (Durbin et al., 2002) <doi:10.1093/bioinformatics/18.suppl_1.S105>, Neglog (Whittaker et al., 2005) <doi:10.1111/j.1467-9876.2005.00520.x>, Reciprocal (Tukey, 1957), Log Shift (Feng et al., 2016) <doi:10.1002/sta4.104>, Bickel-Docksum (Bickel and Doksum, 1981) <doi:10.1080/01621459.1981.10477649>, Yeo-Johnson (Yeo and Johnson, 2000) <doi:10.1093/biomet/87.4.954>, Square Root (Medina et al., 2019), Manly (Manly, 1976) <doi:10.2307/2988129>, Modulus (John and Draper, 1980) <doi:10.2307/2986305>, Dual (Yang, 2006) <doi:10.1016/j.econlet.2006.01.011>, Gpower (Kelmansky et al., 2013) <doi:10.1515/sagmb-2012-0030>. It also performs graphical approaches, assesses the success of the transformation via tests and plots.

This package provides several methods for generating density functions based on binned data. Methods include step function, recursive subdivision, and optimized spline. Data are assumed to be nonnegative, the top bin is assumed to have no upper bound, but the bin widths need be equal. All PDF smoothing methods maintain the areas specified by the binned data. (Equivalently, all CDF smoothing methods interpolate the points specified by the binned data.) In practice, an estimate for the mean of the distribution should be supplied as an optional argument. Doing so greatly improves the reliability of statistics computed from the smoothed density functions. Includes methods for estimating the Gini coefficient, the Theil index, percentiles, and random deviates from a smoothed distribution. Among the three methods, the optimized spline (splinebins) is recommended for most purposes. The percentile and random-draw methods should be regarded as experimental, and these methods only support splinebins.

This package provides a bottom up model to estimate the emission levels of public transport systems based on General Transit Feed Specification (GTFS) data. The package requires two main inputs: i) Public transport data in the GTFS standard format; and ii) Some basic information on fleet characteristics such as fleet age, technology, fuel and Euro stage. As it stands, the package estimates several pollutants at high spatial and temporal resolutions. Pollution levels can be calculated for specific transport routes, trips, time of the day or for the transport system as a whole. The output with emission estimates can be extracted in different formats, supporting analysis on how emission levels vary across space, time and by fleet characteristics. A full description of the methods used in the gtfs2emis model is presented in Vieira, J. P. B.; Pereira, R. H. M.; Andrade, P. R. (2022) <doi:10.31219/osf.io/8m2cy>.

Simulation of the random evolution of heterogeneous populations using stochastic Individual-Based Models (IBMs) <doi:10.48550/arXiv.2303.06183>. The package enables users to simulate population evolution, in which individuals are characterized by their age and some characteristics, and the population is modified by different types of events, including births/arrivals, death/exit events, or changes of characteristics. The frequency at which an event can occur to an individual can depend on their age and characteristics, but also on the characteristics of other individuals (interactions). Such models have a wide range of applications. For instance, IBMs can be used for simulating the evolution of a heterogeneous insurance portfolio with selection or for validating mortality forecasts. This package overcomes the limitations of time-consuming IBMs simulations by implementing new efficient algorithms based on thinning methods, which are compiled using the Rcpp package while providing a user-friendly interface.

This package provides interactive, configurable and graphics visualization of the chromosome regions of any living organism allowing users to map chromosome elements (like genes, SNPs etc.) on the chromosome plot. It introduces a special plot viz. the "chromosome heatmap" that, in addition to mapping elements, can visualize the data associated with chromosome elements (like gene expression) in the form of heat colors. Users can investigate the detailed information about the mappings (like gene names or total genes mapped on a location) or can view the magnified single or double stranded view of the chromosome at a location showing each mapped element in sequential order. The package provide multiple features like visualizing multiple sets, chromosome heat-maps, group annotations, adding hyperlinks, and labelling. The plots can be saved as HTML documents that can be customized and shared easily. In addition, you can include them in R Markdown or in R Shiny applications.

Similarity of dissolution profiles is assessed using the similarity factor f2 according to the EMA guideline (European Medicines Agency 2010) "On the investigation of bioequivalence". Dissolution profiles are regarded as similar if the f2 value is between 50 and 100. For the applicability of the similarity factor f2, the variability between profiles needs to be within certain limits. Often, this constraint is violated. One possibility in this situation is to resample the measured profiles in order to obtain a bootstrap estimate of f2 (Shah et al. (1998) <doi:10.1023/A:1011976615750>). Other alternatives are the model-independent non-parametric multivariate confidence region (MCR) procedure (Tsong et al. (1996) <doi:10.1177/009286159603000427>) or the T2-test for equivalence procedure (Hoffelder (2016) <https://www.ecv.de/suse_item.php?suseId=Z|pi|8430>). Functions for estimation of f1, f2, bootstrap f2, MCR / T2-test for equivalence procedure are implemented.

Allows ATA (Automatic Time series analysis using the Ata method) models from the ATAforecasting package to be used in a tidy workflow with the modeling interface of fabletools'. This extends ATAforecasting to provide enhanced model specification and management, performance evaluation methods, and model combination tools. The Ata method (Yapar et al. (2019) <doi:10.15672/hujms.461032>), an alternative to exponential smoothing (described in Yapar (2016) <doi:10.15672/HJMS.201614320580>, Yapar et al. (2017) <doi:10.15672/HJMS.2017.493>), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend and seasonal).

Statistical methods to match feature vectors between multiple datasets in a one-to-one fashion. Given a fixed number of classes/distributions, for each unit, exactly one vector of each class is observed without label. The goal is to label the feature vectors using each label exactly once so to produce the best match across datasets, e.g. by minimizing the variability within classes. Statistical solutions based on empirical loss functions and probabilistic modeling are provided. The Gurobi software and its R interface package are required for one of the package functions (match.2x()) and can be obtained at <https://www.gurobi.com/> (free academic license). For more details, refer to Degras (2022) <doi:10.1080/10618600.2022.2074429> "Scalable feature matching for large data collections" and Bandelt, Maas, and Spieksma (2004) <doi:10.1057/palgrave.jors.2601723> "Local search heuristics for multi-index assignment problems with decomposable costs".

Easily analyze and visualize differences between samples (e.g., benchmark comparisons, nonresponse comparisons in surveys) on three levels. The comparisons can be univariate, bivariate or multivariate. On univariate level the variables of interest of a survey and a comparison survey (i.e. benchmark) are compared, by calculating one of several difference measures (e.g., relative difference in mean), and an average difference between the surveys. On bivariate level a function can calculate significant differences in correlations for the surveys. And on multivariate levels a function can calculate significant differences in model coefficients between the surveys of comparison. All of those differences can be easily plotted and outputted as a table. For more detailed information on the methods and example use see Rohr, B., Silber, H., & Felderer, B. (2024). Comparing the Accuracy of Univariate, Bivariate, and Multivariate Estimates across Probability and Nonprobability Surveys with Population Benchmarks. Sociological Methodology <doi:10.1177/00811750241280963>.

Highest averages & largest remainders allocating seats methods and several party system scores. Implemented highest averages allocating seats methods are D'Hondt, Webster, Danish, Imperiali, Hill-Huntington, Dean, Modified Sainte-Lague, equal proportions and Adams. Implemented largest remainders allocating seats methods are Hare, Droop, Hangenbach-Bischoff, Imperial, modified Imperial and quotas & remainders. The main advantage of this package is that ties are always reported and not incorrectly allocated. Party system scores provided are competitiveness, concentration, effective number of parties, party nationalization score, party system nationalization score and volatility. References: Gallagher (1991) <doi:10.1016/0261-3794(91)90004-C>. Norris (2004, ISBN:0-521-82977-1). Laakso & Taagepera (1979) <https://escholarship.org/uc/item/703827nv>. Jones & Mainwaring (2003) <https://kellogg.nd.edu/sites/default/files/old_files/documents/304_0.pdf>. Pedersen (1979) <https://janda.org/c24/Readings/Pedersen/Pedersen.htm>. Golosov (2010) <doi:10.1177/1354068809339538>. Golosov (2014) <doi:10.1177/1354068814549342>.

An extensible repository of accurate, up-to-date functions to score commonly used patient-reported outcome (PRO), quality of life (QOL), and other psychometric and psychological measures. PROscorer', together with the PROscorerTools package, is a system to facilitate the incorporation of PRO measures into research studies and clinical settings in a scientifically rigorous and reproducible manner. These packages and their vignettes are intended to help establish and promote best practices for scoring PRO and PRO-like measures in research. The PROscorer Instrument Descriptions vignette contains descriptions of each instrument scored by PROscorer', complete with references. These instrument descriptions are suitable for inclusion in formal study protocol documents, grant proposals, and manuscript Method sections. Each PROscorer function is composed of helper functions from the PROscorerTools package, and users are encouraged to contribute new functions to PROscorer'. More scoring functions are currently in development and will be added in future updates.

Analysis of pervasiveness of effects in correlational data. The Observed Proportion (or Percentage) of Concordant Pairs (OPCP) is Kendall's Tau expressed on a 0 to 1 metric instead of the traditional -1 to 1 metric to facilitate interpretation. As its name implies, it represents the proportion of concordant pairs in a sample (with an adjustment for ties). Pairs are concordant when a participant who has a larger value on a variable than another participant also has a larger value on a second variable. The OPCP is therefore an easily interpretable indicator of monotonicity. The pervasive functions are essentially wrappers for the arules package by Hahsler et al. (2025)<doi:10.32614/CRAN.package.arules> and serve to count individuals who actually display the pattern(s) suggested by a regression. For more details, see the paper "Considering approaches to pervasiveness in the context of personality psychology" now accepted at the journal Personality Science.

The implementation of a forecasting-specific tree-based model that is in particular suitable for global time series forecasting, as proposed in Godahewa et al. (2022) <arXiv:2211.08661v1>. The model uses the concept of Self Exciting Threshold Autoregressive (SETAR) models to define the node splits and thus, the model is named SETAR-Tree. The SETAR-Tree uses some time-series-specific splitting and stopping procedures. It trains global pooled regression models in the leaves allowing the models to learn cross-series information. The depth of the tree is controlled by conducting a statistical linearity test as well as measuring the error reduction percentage at each node split. Thus, the SETAR-Tree requires minimal external hyperparameter tuning and provides competitive results under its default configuration. A forest is developed by extending the SETAR-Tree. The SETAR-Forest combines the forecasts provided by a collection of diverse SETAR-Trees during the forecasting process.

The first stand-alone R package for computation of latent correlation that takes into account all variable types (continuous/binary/ordinal/zero-inflated), comes with an optimized memory footprint, and is computationally efficient, essentially making latent correlation estimation almost as fast as rank-based correlation estimation. The estimation is based on latent copula Gaussian models. For continuous/binary types, see Fan, J., Liu, H., Ning, Y., and Zou, H. (2017). For ternary type, see Quan X., Booth J.G. and Wells M.T. (2018) <doi:10.48550/arXiv.1809.06255>. For truncated type or zero-inflated type, see Yoon G., Carroll R.J. and Gaynanova I. (2020) <doi:10.1093/biomet/asaa007>. For approximation method of computation, see Yoon G., Müller C.L. and Gaynanova I. (2021) <doi:10.1080/10618600.2021.1882468>. The latter method uses multi-linear interpolation originally implemented in the R package <https://cran.r-project.org/package=chebpol>.

Several classes for moment-based models are defined. The classes are defined for moment conditions derived from a single equation or a system of equations. The conditions can also be expressed as functions or formulas. Several methods are also offered to facilitate the development of different estimation techniques. The methods that are currently provided are the Generalized method of moments (Hansen 1982; <doi:10.2307/1912775>), for single equations and systems of equation, and the Generalized Empirical Likelihood (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>). Some work is being done to add tools to deal with weak and/or many instruments. This includes K-Class estimators (Limited Information Maximum Likelihood and Fuller), Anderson and Rubin statistic test, etc.

This package provides new tools for analyzing discrete trait data integrating bio-ontologies and phylogenetics. It expands on the previous work of Tarasov et al. (2019) <doi:10.1093/isd/ixz009>. The PARAMO pipeline allows to reconstruct ancestral phenomes treating groups of morphological traits as a single complex character. The pipeline incorporates knowledge from ontologies during the amalgamation of individual character stochastic maps. Here we expand the current PARAMO functionality by adding new statistical methods for inferring evolutionary phenome dynamics using non-homogeneous Poisson process (NHPP). The new functionalities include: (1) reconstruction of evolutionary rate shifts of phenomes across lineages and time; (2) reconstruction of morphospace dynamics through time; and (3) estimation of rates of phenome evolution at different levels of anatomical hierarchy (e.g., entire body or specific regions only). The package also includes user-friendly tools for visualizing evolutionary rates of different anatomical regions using vector images of the organisms of interest.

An easy-to-use tool for working with presence/absence tests on pooled or grouped samples. The primary application is for estimating prevalence of a marker in a population based on the results of tests on pooled specimens. This sampling method is often employed in surveillance of rare conditions in humans or animals (e.g. molecular xenomonitoring). The package was initially conceived as an R-based alternative to the molecular xenomonitoring software, PoolScreen <https://sites.uab.edu/statgenetics/software/>. However, it goes further, allowing for estimates of prevalence to be adjusted for hierarchical sampling frames, and perform flexible mixed-effect regression analyses (McLure et al. Environmental Modelling and Software. <DOI:10.1016/j.envsoft.2021.105158>). The package is currently in early stages, however more features are planned or in the works: e.g. adjustments for imperfect test specificity/sensitivity, functions for helping with optimal experimental design, and functions for spatial modelling.

Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. Machine learning has enabled us to generate useful protein sequences on a variety of scales. Generative models are machine learning methods which seek to model the distribution underlying the data, allowing for the generation of novel samples with similar properties to those on which the model was trained. Generative models of proteins can learn biologically meaningful representations helpful for a variety of downstream tasks. Furthermore, they can learn to generate protein sequences that have not been observed before and to assign higher probability to protein sequences that satisfy desired criteria. In this package, common deep generative models for protein sequences, such as variational autoencoder (VAE), generative adversarial networks (GAN), and autoregressive models are available. In the VAE and GAN, the Word2vec is used for embedding. The transformer encoder is applied to protein sequences for the autoregressive model.