Estimates (and controls for) phylogenetic signal through phylogenetic eigenvectors regression (PVR) and phylogenetic signal-representation (PSR) curve, along with some plot utilities.

This package provides functions that facilitate the elaboration of population pyramids.

This package provides functions to read and write APE-compatible phylogenetic trees in NEXUS and Newick formats, while preserving annotations.

Design parameters of the optimal two-period multiarm platform design (controlling for either family-wise error rate or pair-wise error rate) can be calculated using this package, allowing pre-planned deferred arms to be added during the trial. More details about the design method can be found in the paper: Pan, H., Yuan, X. and Ye, J. (2022) "An optimal two-period multiarm platform design with new experimental arms added during the trial". Manuscript submitted for publication. For additional references: Dunnett, C. W. (1955) <doi:10.2307/2281208>.

Allows specification and fitting of some parameter estimation examples inspired by time-resolved spectroscopy via a Shiny GUI.

The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, 2021 <doi:10.3389/fevo.2021.588292>) can be used for a variety of multivariate problems when count data are at play, including principal component analysis for count data, discriminant analysis, model-based clustering and network inference. Implements variational algorithms to fit such models accompanied with a set of functions for visualization and diagnostic.

This package provides Partial least squares Regression for (weighted) beta regression models (Bertrand 2013, <https://ojs-test.apps.ocp.math.cnrs.fr/index.php/J-SFdS/article/view/215>) and k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.

It allows the user to determine sample sizes, select probabilistic samples, make estimates of different parameters for the total finite population and in studio domains, using the main design drawings.

This package provides a comprehensive library for colour vectors and colour palettes using a new family of colour classes (palettes_colour and palettes_palette) that always print as hex codes with colour previews. Capabilities include: formatting, casting and coercion, extraction and updating of components, plotting, colour mixing arithmetic, and colour interpolation.

Validation of risk predictions obtained from survival models and competing risk models based on censored data using inverse weighting and cross-validation. Most of the pec functionality has been moved to riskRegression'.

Simulate via Markov chain Monte Carlo (hit-and-run algorithm) a Dirichlet distribution conditioned to satisfy a finite set of linear equality and inequality constraints (hence to lie in a convex polytope that is a subset of the unit simplex).

An R implementation of the cross-platform, language-independent "port4me" algorithm (<https://github.com/HenrikBengtsson/port4me>), which (1) finds a free Transmission Control Protocol ('TCP') port in [1024,65535] that the user can open, (2) is designed to work in multi-user environments, (3), gives different users, different ports, (4) gives the user the same port over time with high probability, (5) gives different ports for different software tools, and (6) requires no configuration.

Create a word cloud using the abstract of publications from Pubmed'.

Market odds from from Pinnacle, an online sports betting bookmaker (see <https://www.pinnacle.com> for more information). Included are datasets for the Major League Baseball (MLB) 2016 season and the USA election 2016. These datasets can be used to build models and compare statistical information with the information from prediction markets.The Major League Baseball (MLB) 2016 dataset can be used for sabermetrics analysis and also can be used in conjunction with other popular Major League Baseball (MLB) datasets such as Retrosheets or the Lahman package by merging by GameID.

This package provides functions to generate ensembles of generalized linear models using a greedy projected subset gradient descent algorithm. The sparsity and diversity tuning parameters are selected by cross-validation.

Calculate the optimal vertex partition of a graph using the persistence as objective function. These subroutines have been used in Avellone et al. <doi:10.1007/s10288-023-00559-z>.

Calculation of the parametric, nonparametric confidence intervals for the difference or ratio of location parameters, nonparametric confidence interval for the Behrens-Fisher problem and for the difference, ratio and odds-ratio of binomial proportions for comparison of independent samples. Common wrapper functions to split data sets and apply confidence intervals or tests to these subsets. A by-statement allows calculation of CI separately for the levels of further factors. CI are not adjusted for multiplicity.

Computes the D', Wn, and conditional asymmetric linkage disequilibrium (ALD) measures for pairs of genetic loci. Performs these linkage disequilibrium (LD) calculations on phased genotype data recorded using Genotype List (GL) String or columnar formats. Alternatively, generates expectation-maximization (EM) estimated haplotypes from phased data, or performs LD calculations on EM estimated haplotypes. Performs sign tests comparing LD values for phased and unphased datasets, and generates heat-maps for each LD measure. Described by Osoegawa et al. (2019a) <doi:10.1016/j.humimm.2019.01.010>, and Osoegawa et. al. (2019b) <doi:10.1016/j.humimm.2019.05.018>.

Automatic estimation of number of principal components in PCA with PEnalized SEmi-integrated Likelihood (PESEL). See Piotr Sobczyk, Malgorzata Bogdan, Julie Josse "Bayesian dimensionality reduction with PCA using penalized semi-integrated likelihood" (2017) <doi:10.1080/10618600.2017.1340302>.

This package provides a toolbox to facilitate the calculation of political system indicators for researchers. This package offers a variety of basic indicators related to electoral systems, party systems, elections, and parliamentary studies, as well as others. Main references are: Loosemore and Hanby (1971) <doi:10.1017/S000712340000925X>; Gallagher (1991) <doi:10.1016/0261-3794(91)90004-C>; Laakso and Taagepera (1979) <doi:10.1177/001041407901200101>; Rae (1968) <doi:10.1177/001041406800100305>; HirschmaÅ (1945) <ISBN:0-520-04082-1>; Kesselman (1966) <doi:10.2307/1953769>; Jones and Mainwaring (2003) <doi:10.1177/13540688030092002>; Rice (1925) <doi:10.2307/2142407>; Pedersen (1979) <doi:10.1111/j.1475-6765.1979.tb01267.x>; SANTOS (2002) <ISBN:85-225-0395-8>.

Simulates judgments of frequency and duration based on the Probability Associator Time (PASS-T) model. PASS-T is a memory model based on a simple competitive artificial neural network. It can imitate human judgments of frequency and duration, which have been extensively studied in cognitive psychology (e.g. Hintzman (1970) <doi:10.1037/h0028865>, Betsch et al. (2010) <https://psycnet.apa.org/record/2010-18204-003>). The PASS-T model is an extension of the PASS model (Sedlmeier, 2002, ISBN:0198508638). The package provides an easy way to run simulations, which can then be compared with empirical data in human judgments of frequency and duration.

Visualizes panel data. It has three main functionalities: (1) it plots the treatment status and missing values in a panel dataset; (2) it visualizes the temporal dynamics of a main variable of interest; (3) it depicts the bivariate relationships between a treatment variable and an outcome variable either by unit or in aggregate. For details, see <doi:10.18637/jss.v107.i07>.

Village potential statistics (PODES) collects various information on village potential and challenges faced by villages in Indonesia. Information related to village potential includes economy, security, health, employment, communication and information, sports, entertainment, development, community empowerment, education, socio-culture, transportation in the village. Information related to challenges includes natural disasters, public health, environmental pollution, social problems and security disturbances that occur in the village.

Joint frailty models have been widely used to study the associations between recurrent events and a survival outcome. However, existing joint frailty models only consider one or a few recurrent events and cannot deal with high-dimensional recurrent events. This package can be used to fit our recently developed penalized joint frailty model that can handle high-dimensional recurrent events. Specifically, an adaptive lasso penalty is imposed on the parameters for the effects of the recurrent events on the survival outcome, which allows for variable selection. Also, our algorithm is computationally efficient, which is based on the Gaussian variational approximation method.