This package implements a local likelihood estimator for the dependence parameter in bivariate conditional copula models. Copula family and local likelihood bandwidth parameters are selected by leave-one-out cross-validation. The models are implemented in TMB', meaning that the local score function is efficiently calculated via automated differentiation (AD), such that quasi-Newton algorithms may be used for parameter estimation.

This is an extension package to logrx', which is a log creation program focused on Clinical Reporting within the Pharma Industry. This package enables a simple shiny-based Add-in that provides a point and click interface to produce a log for a single program.

Plots empty Lexis grids, adds lifelines and highlights certain areas of the grid, like cohorts and age groups.

Converts video files to mp3', merges multiple audio files and trims audio files using FFmpeg', which is dynamically downloaded to avoid bundling any third-party binaries. Users must ensure compliance with the license terms of FFmpeg when using the package. See <https://github.com/BtbN/FFmpeg-Builds/releases/download/latest/ffmpeg-master-latest-win64-gpl.zip> for details.

Back-end connections to LattE (<https://www.math.ucdavis.edu/~latte/>) for counting lattice points and integration inside convex polytopes and 4ti2 (<http://www.4ti2.de/>) for algebraic, geometric, and combinatorial problems on linear spaces and front-end tools facilitating their use in the R ecosystem.

This package performs power and sample size calculation for non-proportional hazards model using the Fleming-Harrington family of weighted log-rank tests. The sequentially calculated log-rank test score statistics are assumed to have independent increments as characterized in Anastasios A. Tsiatis (1982) <doi:10.1080/01621459.1982.10477898>. The mean and variance of log-rank test score statistics are calculated based on Kaifeng Lu (2021) <doi:10.1002/pst.2069>. The boundary crossing probabilities are calculated using the recursive integration algorithm described in Christopher Jennison and Bruce W. Turnbull (2000, ISBN:0849303168). The package can also be used for continuous, binary, and count data. For continuous data, it can handle missing data through mixed-model for repeated measures (MMRM). In crossover designs, it can estimate direct treatment effects while accounting for carryover effects. For binary data, it can design Simon's 2-stage, modified toxicity probability-2 (mTPI-2), and Bayesian optimal interval (BOIN) trials. For count data, it can design group sequential trials for negative binomial endpoints with censoring. Additionally, it facilitates group sequential equivalence trials for all supported data types. Moreover, it can design adaptive group sequential trials for changes in sample size, error spending function, number and spacing or future looks. Finally, it offers various options for adjusted p-values, including graphical and gatekeeping procedures.

This package provides classes and methods for objects, whose indexing naturally starts from zero. Subsetting, indexing and mathematical operations are defined naturally between lagged objects and lagged and base R objects. Recycling is not used, except for singletons. The single bracket operator doesn't drop dimensions by default.

Estimation of latent class models with individual covariates for capture-recapture data. See Bartolucci, F. and Forcina, A. (2022), Estimating the size of a closed population by modeling latent and observed heterogeneity, Biometrics, 80(2), ujae017.

Computes the implied weights of linear regression models for estimating average causal effects and provides diagnostics based on these weights. These diagnostics rely on the analyses in Chattopadhyay and Zubizarreta (2023) <doi:10.1093/biomet/asac058> where several regression estimators are represented as weighting estimators, in connection to inverse probability weighting. lmw provides tools to diagnose representativeness, balance, extrapolation, and influence for these models, clarifying the target population of inference. Tools are also available to simplify estimating treatment effects for specific target populations of interest.

The aim of the package is to create data objects which allow for accesses like x["test"] and x["test","test"].

This package provides tools to create an interactive web-based visualization of a topic model that has been fit to a corpus of text data using Latent Dirichlet Allocation (LDA). Given the estimated parameters of the topic model, it computes various summary statistics as input to an interactive visualization built with D3.js that is accessed via a browser. The goal is to help users interpret the topics in their LDA topic model.

We present a method based on filtering algorithms to estimate the parameters of linear, i.e. the coefficients and the variance of the error term. The proposed algorithms make use of Particle Filters following Ristic, B., Arulampalam, S., Gordon, N. (2004, ISBN: 158053631X) resampling methods. Parameters of logistic regression models are also estimated using an evolutionary particle filter method.

Palettes generated from limnology based field and laboratory photos. Palettes can be used to generate color values to be used in any functions that calls for a color (i.e. ggplot(), plot(), flextable(), etc.).

Lexical response data is a package that can be used for processing cued-recall, free-recall, and sentence responses from memory experiments.

This package creates a consensus genetic map by merging linkage maps from different populations. The software uses linear programming (LP) to efficiently minimize the mean absolute error between the consensus map and the linkage maps. This minimization is performed subject to linear inequality constraints that ensure the ordering of the markers in the linkage maps is preserved. When marker order is inconsistent between linkage maps, a minimum set of ordinal constraints is deleted to resolve the conflicts.

This package provides functions to calculate lunar and other related environmental covariates.

This package provides a wrapper around the LIBLINEAR C/C++ library for machine learning (available at <https://www.csie.ntu.edu.tw/~cjlin/liblinear/>). LIBLINEAR is a simple library for solving large-scale regularized linear classification and regression. It currently supports L2-regularized classification (such as logistic regression, L2-loss linear SVM and L1-loss linear SVM) as well as L1-regularized classification (such as L2-loss linear SVM and logistic regression) and L2-regularized support vector regression (with L1- or L2-loss). The main features of LiblineaR include multi-class classification (one-vs-the rest, and Crammer & Singer method), cross validation for model selection, probability estimates (logistic regression only) or weights for unbalanced data. The estimation of the models is particularly fast as compared to other libraries.

Outlier detection using leave-one-out kernel density estimates and extreme value theory. The bandwidth for kernel density estimates is computed using persistent homology, a technique in topological data analysis. Using peak-over-threshold method, a generalized Pareto distribution is fitted to the log of leave-one-out kde values to identify outliers.

Miscellaneous R functions (for graphics, data import, data transformation, and general utilities) and templates (for exploratory analysis, Bayesian modeling, and crafting scientific manuscripts).

This package provides a C++ header library for using the libsoda-cxx library with R. The C++ header reimplements the lsoda function from the ODEPACK library for solving initial value problems for first order ordinary differential equations (Hindmarsh, 1982; <https://computing.llnl.gov/sites/default/files/ODEPACK_pub1_u88007.pdf>). The C++ header can be used by other R packages by linking against this package. The C++ functions can be called inline using Rcpp'. Finally, the package provides an ode function to call from R.

Kernel regression smoothing with adaptive local or global plug-in bandwidth selection.

Create maps made of lines. The package contains one function: linemap(). linemap() displays a map made of lines using a raster or gridded data.

The Length-Biased Power Garima distribution for computes the probability density, the cumulative density distribution and the quantile function of the distribution, and generates sample values with random variables based on Kittipong and Sirinapa(2021)<DOI: 10.14456/sjst-psu.2021.89>.

This package provides a graph proposed by Rosenbaum is useful for checking some properties of various sorts of latent scale, this program generates commands to obtain the graph using dot from graphviz'.