An n-gram is a sequence of n "words" taken, in order, from a body of text. This is a collection of utilities for creating, displaying, summarizing, and "babbling" n-grams. The tokenization and "babbling" are handled by very efficient C code, which can even be built as its own standalone library. The babbler is a simple Markov chain. The package also offers a vignette with complete example workflows and information about the utilities offered in the package.

This package provides tools for visual inference. Generate null data sets and null plots using permutation and simulation. Calculate distance metrics for a lineup, and examine the distributions of metrics.

Density, distribution function, quantile function and random generation for the Nakagami distribution of Nakagami (1960) <doi:10.1016/B978-0-08-009306-2.50005-4>.

Computes the nonlinear cointegrating autoregressive distributed lag model with automatic bases aic and bic lags selection of independent variables proposed by (Shin, Yu & Greenwood-Nimmo, 2014 <doi:10.1007/978-1-4899-8008-3_9>).

It names the R Markdown chunks of files based on the filename.

Efficiency and productivity indices are measured using this package. This package contains functions for measuring efficiency and productivity of decision making units (DMUs) under the framework of Data Envelopment Analysis (DEA) and its variations.

Fetch data from the National Oceanic and Atmospheric Administration Climate Data Online (NOAA CDO) <https://www.ncdc.noaa.gov/cdo-web/webservices/v2> API including daily, monthly, and yearly climate summaries, radar data, climatological averages, precipitation data, annual summaries, storm events, and agricultural meteorology.

Neighbour-balanced designs ensure that no treatment is disadvantaged unfairly by its surroundings. The treatment allocation in these designs is such that every treatment appears equally often as a neighbour with every other treatment. Neighbour Balanced Designs are employed when there is a possibility of neighbour effects from treatments used in adjacent experimental units. In the literature, a vast number of such designs have been developed. This package generates some efficient neighbour balanced block designs which are balanced and partially variance balanced for estimating the contrast pertaining to direct and neighbour effects, as well as provides a function for analysing the data obtained from such trials (Azais, J.M., Bailey, R.A. and Monod, H. (1993). "A catalogue of efficient neighbour designs with border plots". Biometrics, 49, 1252-1261 ; Tomar, J. S., Jaggi, Seema and Varghese, Cini (2005)<DOI: 10.1080/0266476042000305177>. "On totally balanced block designs for competition effects"). This package contains functions named nbbd1(),nbbd2(),nbbd3(),pnbbd1() and pnbbd2() which generates neighbour balanced block designs within a specified range of number of treatment (v). It contains another function named anlys()for performing the analysis of data generated from such trials.

K-nearest neighbor search for projected and non-projected sf spatial layers. Nearest neighbor search uses (1) C code from GeographicLib for lon-lat point layers, (2) function knn() from package nabor for projected point layers, or (3) function st_distance() from package sf for line or polygon layers. The package also includes several other utility functions for spatial analysis.

Network is a prevalent form of data structure in many fields. As an object of analysis, many distance or metric measures have been proposed to define the concept of similarity between two networks. We provide a number of distance measures for networks. See Jurman et al (2011) <doi:10.3233/978-1-60750-692-8-227> for an overview on spectral class of inter-graph distance measures.

Similarity measures for hierarchical clustering of objects characterized by nominal (categorical) variables. Evaluation criteria for nominal data clustering.

Set of functions implementing the algorithm described in Fernandez Torvisco et al. (2018) for fitting separable nonlinear regression curves. See Fernandez Torvisco, Rodriguez-Arias Fernandez and Cabello Sanchez (2018) <doi:10.2298/FIL1812233T>.

This package provides a graphical display of results from network meta-analysis (NMA). It is suitable for outcomes like odds ratio (OR), risk ratio (RR), risk difference (RD) and standardized mean difference (SMD). It also has an option to visually display and compare the surface under the cumulative ranking (SUCRA) of different treatments.

The field of immunology benefits from software that can predict which peptide sequences trigger an immune response. NetMHCIIpan is a such a tool: it predicts the binding strength of a short peptide to a Major Histocompatibility Complex class II (MHC-II) molecule. NetMHCIIpan can be used from a web server at <https://services.healthtech.dtu.dk/services/NetMHCIIpan-3.2/> or from the command-line, using a local installation. This package allows to call NetMHCIIpan from R.

NeuroAnatomy Toolbox (nat) enables analysis and visualisation of 3D biological image data, especially traced neurons. Reads and writes 3D images in NRRD and Amira AmiraMesh formats and reads surfaces in Amira hxsurf format. Traced neurons can be imported from and written to SWC and Amira LineSet and SkeletonGraph formats. These data can then be visualised in 3D via rgl', manipulated including applying calculated registrations, e.g. using the CMTK registration suite, and analysed. There is also a simple representation for neurons that have been subjected to 3D skeletonisation but not formally traced; this allows morphological comparison between neurons including searches and clustering (via the nat.nblast extension package).

Computes various geospatial indices of socioeconomic deprivation and disparity in the United States. Some indices are considered "spatial" because they consider the values of neighboring (i.e., adjacent) census geographies in their computation, while other indices are "aspatial" because they only consider the value within each census geography. Two types of aspatial neighborhood deprivation indices (NDI) are available, including: (1) based on Messer et al. (2006) <doi:10.1007/s11524-006-9094-x> and (2) based on Andrews et al. (2020) <doi:10.1080/17445647.2020.1750066> and Slotman et al. (2022) <doi:10.1016/j.dib.2022.108002> who use variables chosen by Roux and Mair (2010) <doi:10.1111/j.1749-6632.2009.05333.x>. Both are a decomposition of multiple demographic characteristics from the U.S. Census Bureau American Community Survey 5-year estimates (ACS-5; 2006-2010 onward). Using data from the ACS-5 (2005-2009 onward), the package can also compute indices of racial or ethnic residential segregation, including but limited to those discussed in Massey & Denton (1988) <doi:10.1093/sf/67.2.281>, and additional indices of socioeconomic disparity.

This package provides functions for working with (grouped) multivariate normal variance mixture distributions (evaluation of distribution functions and densities, random number generation and parameter estimation), including Student's t distribution for non-integer degrees-of-freedom as well as the grouped t distribution and copula with multiple degrees-of-freedom parameters. See <doi:10.18637/jss.v102.i02> for a high-level description of select functionality.

This package provides nonparametric CUSUM tests for detecting changes in possibly serially dependent univariate or low-dimensional multivariate observations. Retrospective tests sensitive to changes in the expectation, the variance, the covariance, the autocovariance, the distribution function, Spearman's rho, Kendall's tau, Gini's mean difference, and the copula are provided, as well as a test for detecting changes in the distribution of independent block maxima (with environmental studies in mind). The package also contains a test sensitive to changes in the autocopula and a combined test of stationarity sensitive to changes in the distribution function and the autocopula. The latest additions are an open-end sequential test based on the retrospective CUSUM statistic that can be used for monitoring changes in the mean of possibly serially dependent univariate observations, as well as closed-end and open-end sequential tests based on empirical distribution functions that can be used for monitoring changes in the contemporary distribution of possibly serially dependent univariate or low-dimensional multivariate observations.

This package provides a nomogram can not be easily applied, because it is difficult to calculate the points or even the survival probability. The package, including a function of nomogramEx(), is to extract the polynomial equations to calculate the points of each variable, and the survival probability corresponding to the total points.

Fast and Accurate Trisomy Prediction in Non-Invasive Prenatal Testing.

Neighbourhood functions are key components of local-search algorithms such as Simulated Annealing or Threshold Accepting. These functions take a solution and return a slightly-modified copy of it, i.e. a neighbour. The package provides a function neighbourfun() that constructs such neighbourhood functions, based on parameters such as admissible ranges for elements in a solution. Supported are numeric and logical solutions. The algorithms were originally created for portfolio-optimisation applications, but can be used for other models as well. Several recipes for neighbour computations are taken from "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658).

Due to Rstudio's status as open source software, we believe it will be utilized frequently for future data analysis by users whom lack formal training or experience with R'. The NMVANOVA (Novice Model Variation ANOVA) a streamlined variation of experimental design functions that allows novice Rstudio users to perform different model variations one-way analysis of variance without downloading multiple libraries or packages. Users can easily manipulate the data block, and needed inputs so that users only have to plugin the four designed variables/values.

Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions modeling oriented towards flow-cytometry data preprocessing applications. Method is detailed in: Hejblum, Alkhassimn, Gottardo, Caron & Thiebaut (2019) <doi: 10.1214/18-AOAS1209>.

The implementation of Markov Model Multiple Imputation with the application to COVID-19 scale, NIAID OS.