This package provides tools for exploring the topography of 3d triangle meshes. The functions were developed with dental surfaces in mind, but could be applied to any triangle mesh of class mesh3d'. More specifically, doolkit allows to isolate the border of a mesh, or a subpart of the mesh using the polygon networks method; crop a mesh; compute basic descriptors (elevation, orientation, footprint area); compute slope, angularity and relief index (Ungar and Williamson (2000) <https://palaeo-electronica.org/2000_1/gorilla/issue1_00.htm>; Boyer (2008) <doi:10.1016/j.jhevol.2008.08.002>), inclination and occlusal relief index or gamma (Guy et al. (2013) <doi:10.1371/journal.pone.0066142>), OPC (Evans et al. (2007) <doi:10.1038/nature05433>), OPCR (Wilson et al. (2012) <doi:10.1038/nature10880>), DNE (Bunn et al. (2011) <doi:10.1002/ajpa.21489>; Pampush et al. (2016) <doi:10.1007/s10914-016-9326-0>), form factor (Horton (1932) <doi:10.1029/TR013i001p00350>), basin elongation (Schum (1956) <doi:10.1130/0016-7606(1956)67[597:EODSAS]2.0.CO;2>), lemniscate ratio (Chorley et al; (1957) <doi:10.2475/ajs.255.2.138>), enamel-dentine distance (Guy et al. (2015) <doi:10.1371/journal.pone.0138802>; Thiery et al. (2017) <doi:10.3389/fphys.2017.00524>), absolute crown strength (Schwartz et al. (2020) <doi:10.1098/rsbl.2019.0671>), relief rate (Thiery et al. (2019) <doi:10.1002/ajpa.23916>) and area-relative curvature; draw cumulative profiles of a topographic variable; and map a variable over a 3d triangle mesh.

Ecological Metadata Language or EML is a long-established format for describing ecological datasets to facilitate sharing and re-use. Because EML is effectively a modified xml schema, however, it is challenging to write and manipulate for non-expert users. delma supports users to write metadata statements in R Markdown or Quarto markdown format, and parse them to EML and (optionally) back again.

This package provides the dose transition pathways (DTP) to project in advance the doses recommended by a model-based design for subsequent patients (stay, escalate, deescalate or stop early) using all the accumulated toxicity information; See Yap et al (2017) <doi: 10.1158/1078-0432.CCR-17-0582>. DTP can be used as a design and an operational tool and can be displayed as a table or flow diagram. The dtpcrm package also provides the modified continual reassessment method (CRM) and time-to-event CRM (TITE-CRM) with added practical considerations to allow stopping early when there is sufficient evidence that the lowest dose is too toxic and/or there is a sufficient number of patients dosed at the maximum tolerated dose.

This package provides a collection of supervised discretization algorithms. It can also be grouped in terms of top-down or bottom-up, implementing the discretization algorithms.

Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm or Two-Step (2S) estimation, supporting datasets with missing data. Factors are assumed to follow a stationary VAR process of order p. The estimation options follow advances in the econometric literature: either running the Kalman Filter and Smoother once with initial values from PCA - 2S estimation as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012> - or via iterated Kalman Filtering and Smoothing until EM convergence - following Doz, Giannone and Reichlin (2012) <doi:10.1162/REST_a_00225> - or using the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>, allowing arbitrary patterns of missing data. The implementation makes heavy use of the Armadillo C++ library and the collapse package, providing for particularly speedy estimation. A comprehensive set of methods supports interpretation and visualization of the model as well as forecasting. Information criteria to choose the number of factors are also provided - following Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.

Statistical methods for retrospectively detecting changes in location and/or dispersion of univariate and multivariate variables. Data values are assumed to be independent, can be individual (one observation at each instant of time) or subgrouped (more than one observation at each instant of time). Control limits are computed, often using a permutation approach, so that a prescribed false alarm probability is guaranteed without making any parametric assumptions on the stable (in-control) distribution. See G. Capizzi and G. Masarotto (2018) <doi:10.1007/978-3-319-75295-2_1> for an introduction to the package.

Various kinds of designs for (industrial) experiments can be created. The package uses, and sometimes enhances, design generation routines from other packages. So far, response surface designs from package rsm', Latin hypercube samples from packages lhs and DiceDesign', and D-optimal designs from package AlgDesign have been implemented.

Create and customize interactive collapsible D3 trees using the D3 JavaScript library and the htmlwidgets package. These trees can be used directly from the R console, from RStudio', in Shiny apps and R Markdown documents. When in Shiny the tree layout is observed by the server and can be used as a reactive filter of structured data.

This package provides functions for the calculation and plotting of synchrony in tree growth from tree-ring width chronologies (TRW index). It combines variance-covariance (VCOV) mixed modelling with functions that quantify the degree to which the TRW chronologies contain a common temporal signal. It also implements temporal trends in spatial synchrony using a moving window. These methods can also be used with other kind of ecological variables that have temporal autocorrelation corrected.

This package contains data organized by topics: categorical data, regression model, means comparisons, independent and repeated measures ANOVA, mixed ANOVA and ANCOVA.

Models for detecting concreteness in natural language. This package is built in support of Yeomans (2021) <doi:10.1016/j.obhdp.2020.10.008>, which reviews linguistic models of concreteness in several domains. Here, we provide an implementation of the best-performing domain-general model (from Brysbaert et al., (2014) <doi:10.3758/s13428-013-0403-5>) as well as two pre-trained models for the feedback and plan-making domains.

Computationally efficient tools for comparing all pairs of profiles in a DNA database. The expectation and covariance of the summary statistic is implemented for fast computing. Routines for estimating proportions of close related individuals are available. The use of wildcards (also called F- designation) is implemented. Dedicated functions ease plotting the results. See Tvedebrink et al. (2012) <doi:10.1016/j.fsigen.2011.08.001>. Compute the distribution of the numbers of alleles in DNA mixtures. See Tvedebrink (2013) <doi:10.1016/j.fsigss.2013.10.142>.

An intuitive, cross-platform graphical data analysis system. It uses menus and dialogs to guide the user efficiently through the data manipulation and analysis process, and has an excel like spreadsheet for easy data frame visualization and editing. Deducer works best when used with the Java based R GUI JGR, but the dialogs can be called from the command line. Dialogs have also been integrated into the Windows Rgui.

In tumor tissue, underlying genomic instability can lead to DNA copy number alterations, e.g., copy number gains or losses. Sporadic copy number alterations occur randomly throughout the genome, whereas recurrent alterations are observed in the same genomic region across multiple independent samples, perhaps because they provide a selective growth advantage. This package implements the DiNAMIC procedure for assessing the statistical significance of recurrent DNA copy number aberrations (Bioinformatics (2011) 27(5) 678 - 685).

The automated clustering and quantification of the digital PCR data is based on the combination of DBSCAN (Hahsler et al. (2019) <doi:10.18637/jss.v091.i01>) and c-means (Bezdek et al. (1981) <doi:10.1007/978-1-4757-0450-1>) algorithms. The analysis is independent of multiplexing geometry, dPCR system, and input amount. The details about input data and parameters are available in the vignette.

Joint dimension reduction and spatial clustering is conducted for Single-cell RNA sequencing and spatial transcriptomics data, and more details can be referred to Wei Liu, Xu Liao, Yi Yang, Huazhen Lin, Joe Yeong, Xiang Zhou, Xingjie Shi and Jin Liu. (2022) <doi:10.1093/nar/gkac219>. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well.

Monthly download stats of CRAN and Bioconductor packages. Download stats of CRAN packages is from the RStudio CRAN mirror', see <https://cranlogs.r-pkg.org:443>. Bioconductor package download stats is at <https://bioconductor.org/packages/stats/>.

This package provides an implementation of a mixture of hidden Markov models (HMMs) for discrete sequence data in the Discrete Bayesian HMM Clustering (DBHC) algorithm. The DBHC algorithm is an HMM Clustering algorithm that finds a mixture of discrete-output HMMs while using heuristics based on Bayesian Information Criterion (BIC) to search for the optimal number of HMM states and the optimal number of clusters.

This package provides methods for fitting nonstationary Gaussian process models by spatial deformation, as introduced by Sampson and Guttorp (1992) <doi:10.1080/01621459.1992.10475181>, and by dimension expansion, as introduced by Bornn et al. (2012) <doi:10.1080/01621459.2011.646919>. Low-rank thin-plate regression splines, as developed in Wood, S.N. (2003) <doi:10.1111/1467-9868.00374>, are used to either transform co-ordinates or create new latent dimensions.

This package provides a wrapper on top of the Domino Command-Line Client'. It lets you run Domino commands (e.g., "run", "upload", "download") directly from your R environment. Under the hood, it uses R's system function to run the Domino executable, which must be installed as a prerequisite. Domino is a service that makes it easy to run your code on scalable hardware, with integrated version control and collaboration features designed for analytical workflows (see <http://www.dominodatalab.com> for more information).

Probability mass function, distribution function, quantile function, random generation and estimation for the skew discrete Laplace distributions.

Computes a new measure, DNSL betweenness, via the creation of a new graph from an existing one, duplicating nodes with self-loops. This betweenness centrality does not drop this essential information. Implements Merelo & Molinari (2024) <doi:10.1007/s42001-023-00245-4>.

This package contains the normalizing and variance stabilizing Data-Driven Haar-Fisz algorithm. Also contains related algorithms for simulating from certain microarray gene intensity models and evaluation of certain transformations. Contains cDNA and shipping credit flow data.

The Data Driven I-V Feature Extraction is used to extract Current-Voltage (I-V) features from I-V curves. I-V curves indicate the relationship between current and voltage for a solar cell or Photovoltaic (PV) modules. The I-V features such as maximum power point (Pmp), shunt resistance (Rsh), series resistance (Rs),short circuit current (Isc), open circuit voltage (Voc), fill factor (FF), current at maximum power (Imp) and voltage at maximum power(Vmp) contain important information of the performance for PV modules. The traditional method uses the single diode model to model I-V curves and extract I-V features. This package does not use the diode model, but uses data-driven a method which select different linear parts of the I-V curves to extract I-V features. This method also uses a sampling method to calculate uncertainties when extracting I-V features. Also, because of the partially shaded array, "steps" occurs in I-V curves. The "Segmented Regression" method is used to identify steps in I-V curves. This material is based upon work supported by the U.S. Department of Energyâ s Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Number DE-EE0007140. Further information can be found in the following paper. [1] Ma, X. et al, 2019. <doi:10.1109/JPHOTOV.2019.2928477>.