This package provides a set of tools for writing and sharing interactive courses to be used with swirl.

This package provides a probability tree allows to compute probabilities of complex events, such as genotype probabilities in intermediate generations of inbreeding through recurrent self-fertilization (selfing). This package implements functionality to compute probability trees for two- and three-marker genotypes in the F2 to F7 selfing generations. The conditional probabilities are derived automatically and in symbolic form. The package also provides functionality to extract and evaluate the relevant probabilities.

This package provides a toolbox that allows the user to implement accessibility related concepts.

Convert text (and text in R objects) to Mocking SpongeBob case <https://knowyourmeme.com/memes/mocking-spongebob> and show them off in fun ways. CoNVErT TexT (AnD TeXt In r ObJeCtS) To MOCkINg SpoNgebOb CAsE <https://knowyourmeme.com/memes/mocking-spongebob> aND shOw tHem OFf IN Fun WayS.

This package provides a unique dataset of historical forest cover across all states in the United States, spanning from 1907 to 2017, along with 1630 as a reference year. This dataset is important for understanding environmental changes and land use trends over time. It includes functionality for easy access of the data.

This package provides functions to generate or sample from all possible splits of features or variables into a number of specified groups. Also computes the best split selection estimator (for low-dimensional data) as defined in Christidis, Van Aelst and Zamar (2019) <arXiv:1812.05678>.

We provide functionality to implement penalized PCA with an option to smooth the objective function using Nesterov smoothing. Two functions are available to compute a user-specified number of eigenvectors. The function unsmoothed_penalized_EV() computes a penalized PCA without smoothing and has three parameters (the input matrix, the Lasso penalty, and the number of desired eigenvectors). The function smoothed_penalized_EV() computes a smoothed penalized PCA using the same parameters and additionally requires the specification of a smoothing parameter. Both functions return a matrix having the desired eigenvectors as columns.

This package provides a programmatic interface to <http://sp2000.org.cn>, re-written based on an accompanying Species 2000 API. Access tables describing catalogue of the Chinese known species of animals, plants, fungi, micro-organisms, and more. This package also supports access to catalogue of life global <http://catalogueoflife.org>, China animal scientific database <http://zoology.especies.cn> and catalogue of life Taiwan <https://taibnet.sinica.edu.tw/home_eng.php>. The development of SP2000 package were supported by Biodiversity Survey and Assessment Project of the Ministry of Ecology and Environment, China <2019HJ2096001006>,Yunnan University's "Double First Class" Project <C176240405> and Yunnan University's Research Innovation Fund for Graduate Students <2019227>.

This package provides estimates for the bivariate and trivariate distribution functions and bivariate and trivariate survival functions for censored gap times. Two approaches, using existing methodologies, are considered: (i) the Lin's estimator, which is based on the extension the Kaplan-Meier estimator of the distribution function for the first event time and the Inverse Probability of Censoring Weights for the second time (Lin DY, Sun W, Ying Z (1999) <doi:10.1093/biomet/86.1.59> and (ii) another estimator based on Kaplan-Meier weights (Una-Alvarez J, Meira-Machado L (2008) <https://w3.math.uminho.pt/~lmachado/Biometria_conference.pdf>). The proposed methods are the landmark estimators based on subsampling approach, and the estimator based on weighted cumulative hazard estimator. The package also provides nonparametric estimator conditional to a given continuous covariate. All these methods have been submitted to be published.

Cluster user-supplied somatic read counts with corresponding allele-specific copy number and tumor purity to infer feasible underlying intra-tumor heterogeneity in terms of number of subclones, multiplicity, and allocation (Little et al. (2019) <doi:10.1186/s13073-019-0643-9>).

Implementation of the family of generalised age-period-cohort stochastic mortality models. This family of models encompasses many models proposed in the actuarial and demographic literature including the Lee-Carter (1992) <doi:10.2307/2290201> and the Cairns-Blake-Dowd (2006) <doi:10.1111/j.1539-6975.2006.00195.x> models. It includes functions for fitting mortality models, analysing their goodness-of-fit and performing mortality projections and simulations.

Routines to write, simulate, and validate stock-flow consistent (SFC) models. The accounting structure of SFC models are described in Godley and Lavoie (2007, ISBN:978-1-137-08599-3). The algorithms implemented to solve the models (Gauss-Seidel and Broyden) are described in Kinsella and O'Shea (2010) <doi:10.2139/ssrn.1729205> and Peressini and Sullivan (1988, ISBN:0-387-96614-5).

With satin functions, visualisation, data extraction and further analysis like producing climatologies from several images, and anomalies of satellite derived ocean data can be easily done. Reading functions can import a user defined geographical extent of data stored in netCDF files. Currently supported ocean data sources include NASA's Oceancolor web page <https://oceancolor.gsfc.nasa.gov/>, sensors VIIRS-SNPP; MODIS-Terra; MODIS-Aqua; and SeaWiFS. Available variables from this source includes chlorophyll concentration, sea surface temperature (SST), and several others. Data sources specific for SST that can be imported too includes Pathfinder AVHRR <https://www.ncei.noaa.gov/products/avhrr-pathfinder-sst> and GHRSST <https://www.ghrsst.org/>. In addition, ocean productivity data produced by Oregon State University can also be handled previous conversion from HDF4 to HDF5 format. Many other ocean variables can be processed by importing netCDF data files from two European Union's Copernicus Marine Service databases <https://marine.copernicus.eu/>, namely Global Ocean Physical Reanalysis and Global Ocean Biogeochemistry Hindcast.

Several functions and S3 methods to construct a super learner in the presence of censored times-to-event and to evaluate its prognostic capacities.

Perform the balanced (Scott and Knott, 1974) and unbalanced <doi:10.1590/1984-70332017v17n1a1> Scott & Knott algorithm.

This package implements a suite of semiparametric and nonparametric kernel-smoothed estimation and testing procedures for continuous mark-specific stratified hazard ratio (treatment/placebo) models in a randomized treatment efficacy trial with a time-to-event endpoint. Semiparametric methods, allowing multivariate marks, are described in Juraska M and Gilbert PB (2013), Mark-specific hazard ratio model with multivariate continuous marks: an application to vaccine efficacy. Biometrics 69(2):328-337 <doi:10.1111/biom.12016>, and in Juraska M and Gilbert PB (2016), Mark-specific hazard ratio model with missing multivariate marks. Lifetime Data Analysis 22(4):606-25 <doi:10.1007/s10985-015-9353-9>. Nonparametric kernel-smoothed methods, allowing univariate marks only, are described in Sun Y and Gilbert PB (2012), Estimation of stratified markâ specific proportional hazards models with missing marks. Scandinavian Journal of Statistics

Flexible multidimensional scaling (MDS) methods and extensions to the package smacof'. This package contains various functions, wrappers, methods and classes for fitting, plotting and displaying a large number of different flexible MDS models. These are: Torgerson scaling (Torgerson, 1958, ISBN:978-0471879459) with powers, Sammon mapping (Sammon, 1969, <doi:10.1109/T-C.1969.222678>) with ratio and interval optimal scaling, Multiscale MDS (Ramsay, 1977, <doi:10.1007/BF02294052>) with ratio and interval optimal scaling, s-stress MDS (ALSCAL; Takane, Young & De Leeuw, 1977, <doi:10.1007/BF02293745>) with ratio and interval optimal scaling, elastic scaling (McGee, 1966, <doi:10.1111/j.2044-8317.1966.tb00367.x>) with ratio and interval optimal scaling, r-stress MDS (De Leeuw, Groenen & Mair, 2016, <https://rpubs.com/deleeuw/142619>) with ratio, interval, splines and nonmetric optimal scaling, power-stress MDS (POST-MDS; Buja & Swayne, 2002 <doi:10.1007/s00357-001-0031-0>) with ratio and interval optimal scaling, restricted power-stress (Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027>) with ratio and interval optimal scaling, approximate power-stress with ratio optimal scaling (Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027>), Box-Cox MDS (Chen & Buja, 2013, <https://jmlr.org/papers/v14/chen13a.html>), local MDS (Chen & Buja, 2009, <doi:10.1198/jasa.2009.0111>), curvilinear component analysis (Demartines & Herault, 1997, <doi:10.1109/72.554199>), curvilinear distance analysis (Lee, Lendasse & Verleysen, 2004, <doi:10.1016/j.neucom.2004.01.007>), nonlinear MDS with optimal dissimilarity powers functions (De Leeuw, 2024, <https://github.com/deleeuw/smacofManual/blob/main/smacofPO(power)/smacofPO.pdf>), sparsified (power) MDS and sparsified multidimensional (power) distance analysis aka extended curvilinear (power) component analysis and extended curvilinear (power) distance analysis (Rusch, 2024, <doi:10.57938/355bf835-ddb7-42f4-8b85-129799fc240e>). Some functions are suitably flexible to allow any other sensible combination of explicit power transformations for weights, distances and input proximities with implicit ratio, interval, splines or nonmetric optimal scaling of the input proximities. Most functions use a Majorization-Minimization algorithm. Currently the methods are only available for one-mode two-way data (symmetric dissimilarity matrices).

Efficient regression analysis under general two-phase sampling, where Phase I includes error-prone data and Phase II contains validated data on a subset.

The synchrosqueezed wavelet transform is implemented. The package is a translation of MATLAB Synchrosqueezing Toolbox, version 1.1 originally developed by Eugene Brevdo (2012). The C code for curve_ext was authored by Jianfeng Lu, and translated to Fortran by Dongik Jang. Synchrosqueezing is based on the papers: [1] Daubechies, I., Lu, J. and Wu, H. T. (2011) Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30. 243-261. [2] Thakur, G., Brevdo, E., Fukar, N. S. and Wu, H-T. (2013) The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications. Signal Processing, 93, 1079-1094.

Identifies a bicluster, a submatrix of the data such that the features and observations within the submatrix differ from those not contained in submatrix, using a two-step method. In the first step, observations in the bicluster are identified to maximize the sum of weighted between cluster feature differences. The method is described in Helgeson et al. (2020) <doi:10.1111/biom.13136>. SCBiclust can be used to identify biclusters which differ based on feature means, feature variances, or more general differences.

This package provides functions for stratified sampling and assigning custom labels to data, ensuring randomness within groups. The package supports various sampling methods such as stratified, cluster, and systematic sampling. It allows users to apply transformations and customize the sampling process. This package can be useful for statistical analysis and data preparation tasks.

Sensitivity analysis for tests, confidence intervals and estimates in matched observational studies with one or more controls using weighted or unweighted Huber-Maritz M-tests (including the permutational t-test). The method is from Rosenbaum (2014) Weighted M-statistics with superior design sensitivity in matched observational studies with multiple controls JASA, 109(507), 1145-1158 <doi:10.1080/01621459.2013.879261>.

Programmatic access to Flipside Crypto data via the Compass RPC API: <https://api-docs.flipsidecrypto.xyz/>. As simple as auto_paginate_query() but with core functions as needed for troubleshooting. Note, 0.1.1 support deprecated 2023-05-31.

This package implements Bayesian inference in accelerated failure time (AFT) models for right-censored survival times assuming a log-logistic distribution. Details of the variational Bayes algorithms, with and without shared frailty, are described in Xian et al. (2024) <doi:10.1007/s11222-023-10365-6> and Xian et al. (2024) <doi:10.48550/arXiv.2408.00177>, respectively.