Local linear hazard estimator and its multiplicatively bias correction, including three bandwidth selection methods: best one-sided cross-validation, double one-sided cross-validation, and standard cross-validation.

Mechanisms to parallelize dependent tasks in a manner that optimizes the compute resources available. It provides access to "delayed" computations, which may be parallelized using futures. It is, to an extent, a facsimile of the Dask library (<https://www.dask.org/>), for the Python language.

Compares the fit of alternative models of continuous trait differentiation between sister species and other paired lineages. Differences in trait means between two lineages arise as they diverge from a common ancestor, and alternative processes of evolutionary divergence are expected to leave unique signatures in the distribution of trait differentiation in datasets comprised of many lineage pairs. Models include approximations of divergent selection, drift, and stabilizing selection. A variety of model extensions facilitate the testing of process-to-pattern hypotheses. Users supply trait data and divergence times for each lineage pair. The fit of alternative models is compared in a likelihood framework.

Consider ambiguity in probabilistic descriptions by replacing a parametric probabilistic description of uncertainty by a non-parametric set of probability distributions in the form of a Density Ratio Class. This is of particular interest in Bayesian inference. The Density Ratio Class is particularly suited for this purpose as it is invariant under Bayesian inference, marginalization, and propagation through a deterministic model. Here, invariant means that the result of the operation applied to a Density Ratio Class is again a Density Ratio Class. In particular the invariance under Bayesian inference thus enables iterative learning within the same framework of Density Ratio Classes. The use of imprecise probabilities in general, and Density Ratio Classes in particular, lead to intervals of characteristics of probability distributions, such as cumulative distribution functions, quantiles, and means. The package is based on a sample of the distribution proportional to the upper bound of the class. Typically this will be a sample from the posterior in Bayesian inference. Based on such a sample, the package provides functions to calculate lower and upper class boundaries and lower and upper bounds of cumulative distribution functions, and quantiles. Rinderknecht, S.L., Albert, C., Borsuk, M.E., Schuwirth, N., Kuensch, H.R. and Reichert, P. (2014) "The effect of ambiguous prior knowledge on Bayesian model parameter inference and prediction." Environmental Modelling & Software. 62, 300-315, 2014. <doi:10.1016/j.envsoft.2014.08.020>. Sriwastava, A. and Reichert, P. "Robust Bayesian Estimation of Value Function Parameters using Imprecise Priors." Submitted. <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4973574>.

Decorrelates a set of summary statistics (i.e., Z-scores or P-values per SNP) via Decorrelation by Orthogonal Transformation (DOT) approach and performs gene-set analyses by combining transformed statistic values; operations are performed with algorithms that rely only on the association summary results and the linkage disequilibrium (LD). For more details on DOT and its power, see Olga (2020) <doi:10.1371/journal.pcbi.1007819>.

Estimates Two-way Fixed Effects difference-in-differences/event-study models using the approach proposed by Gardner (2021) <doi:10.48550/arXiv.2207.05943>. To avoid the problems caused by OLS estimation of the Two-way Fixed Effects model, this function first estimates the fixed effects and covariates using untreated observations and then in a second stage, estimates the treatment effects.

This package provides R bindings to the dockview JavaScript library <https://dockview.dev/>. Create fully customizable grid layouts (docks) in seconds to include in interactive R reports with R Markdown or Quarto or in shiny apps <https://shiny.posit.co/>. In shiny mode, modify docks by dynamically adding, removing or moving panels or groups of panels from the server function. Choose among 8 stunning themes (dark and light), serialise the state of a dock to restore it later.

This package provides methods to detect differential item functioning (DIF) in dichotomous and polytomous items, using both classical and modern approaches. These include Mantel-Haenszel procedures, logistic regression (including ordinal models), and regularization-based methods such as LASSO. Uniform and non-uniform DIF effects can be detected, and some methods support multiple focal groups. The package also provides tools for anchor purification, rest score matching, effect size estimation, and DIF simulation. See Magis, Beland, Tuerlinckx, and De Boeck (2010, Behavior Research Methods, 42, 847â 862, <doi:10.3758/BRM.42.3.847>) for a general overview.

First using dada2 R tools to analyse metabarcode data, the DBTC package then uses the BLAST algorithm to search unknown sequences against local databases, and then takes reduced matched results and provides best taxonomic assignments.

Computational tools for meta-analysis of diagnostic accuracy test. Bootstrap-based computational methods of the confidence interval for AUC of summary ROC curve and some related AUC-based inference methods are available (Noma et al. (2021) <doi:10.1080/23737484.2021.1894408>).

Efficiently and flexibly preprocess data using a set of data filtering, deletion, and interpolation tools. These data preprocessing methods are developed based on the principles of completeness, accuracy, threshold method, and linear interpolation and through the setting of constraint conditions, time completion & recovery, and fast & efficient calculation and grouping. Key preprocessing steps include deletions of variables and observations, outlier removal, and missing values (NA) interpolation, which are dependent on the incomplete and dispersed degrees of raw data. They clean data more accurately, keep more samples, and add no outliers after interpolation, compared with ordinary methods. Auto-identification of consecutive NA via run-length based grouping is used in observation deletion, outlier removal, and NA interpolation; thus, new outliers are not generated in interpolation. Conditional extremum is proposed to realize point-by-point weighed outlier removal that saves non-outliers from being removed. Plus, time series interpolation with values to refer to within short periods further ensures reliable interpolation. These methods are based on and improved from the reference: Liang, C.-S., Wu, H., Li, H.-Y., Zhang, Q., Li, Z. & He, K.-B. (2020) <doi:10.1016/j.scitotenv.2020.140923>.

Diff, patch and merge for data frames. Document changes in data sets and use them to apply patches. Changes to data can be made visible by using render_diff(). The V8 package is used to wrap the daff.js JavaScript library which is included in the package.

This package provides 2D and 3D tour animations as HTML widgets. The user can interact with the widgets using orbit controls, tooltips, brushing, and timeline controls. Linked brushing is supported using crosstalk', and widgets can be embedded in Shiny apps or HTML documents.

Data frame, tibble, or tbl objects are converted to data package objects using specific metadata labels (name, version, title, homepage, description). A data package object ('dpkg') can be written to disk as a parquet file or released to a GitHub repository. Data package objects can be read into R from online repositories and downloaded files are cached locally across R sessions.

This package provides a collection of functions to search and download Digital Surface Model (DSM) and Light Detection and Ranging (LiDAR) data via APIs, including OpenTopography <https://portal.opentopography.org/apidocs/> and TNMAccess <https://apps.nationalmap.gov/tnmaccess/#/>, and canopy tree height data.

The DYMO package provides tools for multi-feature time-series forecasting using a Dynamic Mode Decomposition (DMD) model combined with conformal predictive sampling for uncertainty quantification.

An implementation of data analytic methods in R for analyses for data with ceiling/floor effects. The package currently includes functions for mean/variance estimation and mean comparison tests. Implemented methods are from Aitkin (1964) <doi:10.1007/BF02289723> and Liu & Wang (in prep).

This package provides functions for (1) ranking, selecting, and prioritising genes, proteins, and metabolites from high dimensional biology experiments, (2) multivariate hit calling in high content screens, and (3) combining data from diverse sources.

We provide a comprehensive software to estimate general K-stage DTRs from SMARTs with Q-learning and a variety of outcome-weighted learning methods. Penalizations are allowed for variable selection and model regularization. With the outcome-weighted learning scheme, different loss functions - SVM hinge loss, SVM ramp loss, binomial deviance loss, and L2 loss - are adopted to solve the weighted classification problem at each stage; augmentation in the outcomes is allowed to improve efficiency. The estimated DTR can be easily applied to a new sample for individualized treatment recommendations or DTR evaluation.

Decompose a time series into seasonal, trend and irregular components using transformations to amplitude-frequency domain.

This package contains a robust set of tools designed for constructing deep neural networks, which are highly adaptable with user-defined loss function and probability models. It includes several practical applications, such as the (deepAFT) model, which utilizes a deep neural network approach to enhance the accelerated failure time (AFT) model for survival data. Another example is the (deepGLM) model that applies deep neural network to the generalized linear model (glm), accommodating data types with continuous, categorical and Poisson distributions.

An R interface to the Free Dictionary API <https://dictionaryapi.dev/>, <https://github.com/meetDeveloper/freeDictionaryAPI>. Retrieve dictionary definitions for English words, as well as additional information including phonetics, part of speech, origins, audio pronunciation, example usage, synonyms and antonyms, returned in tidy format for ease of use.

Three general demographic decomposition methods: Pseudo-continuous decomposition proposed by Horiuchi, Wilmoth, and Pletcher (2008) <doi:10.1353/dem.0.0033>, stepwise replacement decomposition proposed by Andreev, Shkolnikov and Begun (2002) <doi:10.4054/DemRes.2002.7.14>, and lifetable response experiments proposed by Caswell (1989) <doi:10.1016/0304-3800(89)90019-7>.

Build graph/network structures using functions for stepwise addition and deletion of nodes and edges. Work with data available in tables for bulk addition of nodes, edges, and associated metadata. Use graph selections and traversals to apply changes to specific nodes or edges. A wide selection of graph algorithms allow for the analysis of graphs. Visualize the graphs and take advantage of any aesthetic properties assigned to nodes and edges.