Sat, 13 Apr 2019

New package tcie with initial version 0.2
Package: tcie
Type: Package
Title: Topologically Correct Isosurface Extraction
Version: 0.2
Date: 2019-04-10
Author: Lis Custódio
Maintainer: Lis Custodio <liscustodio.uerj@gmail.com>
URL: http://liscustodio.github.io/
Description: Isosurfaces extraction algorithms are a powerful tool in the interpretation of volumetric data. The isosurfaces play an important role in several scientific fields, such as biology, medicine, chemistry and computational fluid dynamics. And, for the data to be correctly interpreted, it is crucial that the isosurface be correctly represented. The Marching Cubes algorithm, proposed by Lorensen and Cline <doi:10.1145/37401.37422> in 1987, is clearly one of the most popular isosurface extraction algorithms, and an important tool for many visualization specialists and researchers. The generalized adoption of the Marching Cubes has resulted in many improvements in its algorithm, including, the establishment of the topological correctness of the generated mesh. In 2013, Custodio et al. <doi:10.1016/j.cag.2013.04.004> noted and corrected algorithmic inaccuracies that compromised the topological correctness of the mesh generated by the last version of the Marching Cubes Algorithm: the Marching Cubes 33 proposed by Chernyaev in 1995, implemented in 2003 by Lewiner et al. <doi:10.1080/10867651.2003.10487582>. In 2019, Custodio et al. (in the work An Extended Triangulation to the Marching Cubes 33 Algorithm) proposed an extended triangulation to the Marching Cubes 33 algorithm, in the proposed algorithm the grid vertex are labeled with "+", "-" and "=", according to the relationship between its scalar field value and the isovalue.The inclusion of the "=" grid vertex label naturally avoids degenerate triangles, a well-known issue in meshes generated by the Marching Cubes. The Marching Cubes algorithm has been implemented using many software programs and compilers: C++, proposed by Lewiner et al. (2003); 'MATLAB', proposed by Hammer (2011); and R, proposed by Feng and Tierney (2008). Marching Cubes is also integrated into many visualization toolkits. The complexity of an algorithm increases considerably when it aims to reproduce the topology of the trilinear interpolant correctly. This complexity can sometimes result in errors in the algorithm or in its implementation. During our experiments we observe that all the implementations mentioned have critical issues that compromise the continuity and the topological correctness of the generated mesh. The 'tcie' package is a toolkit with a topologically correct implementation of the Marching Cubes algorithm, based on the Custodio et al. work, which implements the most recent improvements of the algorithm.
LazyData: TRUE
LinkingTo: Rcpp,RcppArmadillo
Imports: nat(>= 1.8.11), rgl(>= 0.99.9), Rvcg (>= 0.17), geomorph (>= 3.0.5)
Depends: R (>= 3.2.3)
RoxygenNote: 6.1.1
License: GPL
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2019-04-11 22:22:21 UTC; lis
Repository: CRAN
Date/Publication: 2019-04-13 23:02:41 UTC

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Package sarima updated to version 0.8.0 with previous version 0.7.6 dated 2018-08-23

Title: Simulation and Prediction with Seasonal ARIMA Models
Description: Functions, classes and methods for time series modelling with ARIMA and related models. The aim of the package is to provide consistent interface for the user. For example, a single function autocorrelations() computes various kinds of theoretical and sample autocorrelations. This is work in progress, see the documentation and vignettes for the current functionality. Function sarima() fits extended multiplicative seasonal ARIMA models with trends, exogenous variables and arbitrary roots on the unit circle, which can be fixed or estimated.
Author: Georgi N. Boshnakov [aut, cre], Jamie Halliday [aut]
Maintainer: Georgi N. Boshnakov <georgi.boshnakov@manchester.ac.uk>

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Package qkerntool updated to version 1.19 with previous version 1.18 dated 2018-11-20

Title: Q-Kernel-Based and Conditionally Negative Definite Kernel-Based Machine Learning Tools
Description: Nonlinear machine learning tool for classification, clustering and dimensionality reduction. It integrates 12 q-kernel functions and 15 conditional negative definite kernel functions and includes the q-kernel and conditional negative definite kernel version of density-based spatial clustering of applications with noise, spectral clustering, generalized discriminant analysis, principal component analysis, multidimensional scaling, locally linear embedding, sammon's mapping and t-Distributed stochastic neighbor embedding.
Author: Yusen Zhang [aut, cre] (<https://orcid.org/0000-0003-3842-1153>), Daolin Pang [ctb], Jinghao Wang [ctb], Jialin Zhang [ctb]
Maintainer: Yusen Zhang <yusenzhang@126.com>

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Package multicolor updated to version 0.1.3 with previous version 0.1.2 dated 2019-03-18

Title: Add Multiple Colors to your Console & RMarkdown Output
Description: Add multiple colors to text that is printed to the console.
Author: Amanda Dobbyn [aut, cre], Hernando Cortina [aut] (<https://orcid.org/0000-0001-6790-4870>)
Maintainer: Amanda Dobbyn <amanda.e.dobbyn@gmail.com>

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Package glmaag updated to version 0.0.4 with previous version 0.0.3 dated 2019-04-11

Title: Adaptive LASSO and Network Regularized Generalized Linear Models
Description: Efficient procedures for adaptive LASSO and network regularized for Gaussian, logistic, and Cox model. Provides network estimation procedure (combination of methods proposed by Ucar, et al. (2007) <doi:10.1093/bioinformatics/btm423> and Meinshausen and Buhlmann (2006) <doi:10.1214/009053606000000281>), cross validation and stability selection proposed by Meinshausen and Buhlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x> and Liu, Roeder and Wasserman (2010) <arXiv:1006.3316> methods. Interactive R app is available.
Author: Kaiqiao Li [aut, cre], Pei Fen Kuan [aut], Xuefeng Wang [aut]
Maintainer: Kaiqiao Li <kaiqiao.li@stonybrook.edu>

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Package ggroups updated to version 1.2.0 with previous version 1.1.4 dated 2019-03-31

Title: Pedigree and Genetic Groups
Description: Calculates additive genetic relationship matrix and its inverse, in matrix and tabular-sparse formats. It includes functions for checking and processing pedigree, as well as functions to calculate the matrix of genetic group contributions (Q), and adding those contributions to the genetic merit of animals (Quaas (1988) <doi:10.3168/jds.S0022-0302(88)79691-5>). Calculation of Q is computationally extensive. There are computationally optimized functions to calculate Q.
Author: Mohammad Ali Nilforooshan [aut, cre] (<https://orcid.org/0000-0003-0339-5442>)
Maintainer: Mohammad Ali Nilforooshan <m.a.nilforooshan@gmail.com>

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Package fastrtext updated to version 0.3.2 with previous version 0.3.1 dated 2019-03-10

Title: 'fastText' Wrapper for Text Classification and Word Representation
Description: Learning text representations and text classifiers may rely on the same simple and efficient approach. 'fastText' is an open-source, free, lightweight library that allows users to perform both tasks. It transforms text into continuous vectors that can later be used on many language related task. It works on standard, generic hardware (no 'GPU' required). It also includes model size reduction feature. 'fastText' original source code is available at <https://github.com/facebookresearch/fastText>.
Author: Michaël Benesty [aut, cre, cph], Facebook, Inc [cph]
Maintainer: Michaël Benesty <michael@benesty.fr>

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Package unine updated to version 0.2.0 with previous version 0.1.0 dated 2019-03-29

Title: Unine Light Stemmer
Description: Implementation of "light" stemmers for French, German, Italian, Spanish, Portuguese, Finnish, Swedish. They are based on the same work as the "light" stemmers found in 'SolR' <https://lucene.apache.org/solr/> or 'ElasticSearch' <https://www.elastic.co/fr/products/elasticsearch>. A "light" stemmer consists in removing inflections only for noun and adjectives. Indexing verbs for these languages is not of primary importance compared to nouns and adjectives. The stemming procedure for French is described in (Savoy, 1999) <doi:10.1002/(SICI)1097-4571(1999)50:10%3C944::AID-ASI9%3E3.3.CO;2-H>.
Author: Michaël Benesty [aut, cre, cph], Jacques Savoy [cph]
Maintainer: Michaël Benesty <michael@benesty.fr>

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New package sos4R with initial version 0.3.0
Package: sos4R
Type: Package
Title: Client for OGC Sensor Observation Services
Version: 0.3.0
Date: 2019-04-13
Authors@R: c(person(given = "Daniel", family = "Nuest", role = c("cre", "aut"), email = "daniel.nuest@uni-muenster.de", comment = c(ORCID = "0000-0002-0024-5046")), person(given = "Edzer", family = "Pebesma", role = "ctb", comment = c(ORCID = "0000-0001-8049-7069")), person(given = "Ben", family = "Graeler", role = "ctb", comment = c(ORCID = "0000-0001-5443-4304")), person(given = "Benjamin", family = "Pross", role = "ctb"), person(given = "Eike Hinderk", family = "Juerrens", role = "ctb"), person(given = "52°North Initiative for Geospatial Open Source Software GmbH", email = "info@52north.org", role = "cph"))
Depends: R (>= 3.4.0)
Imports: httr, methods, sp, stringr, xml2,
Suggests: readr, spacetime, gstat, maps, maptools, mapdata, cshapes, xtable, xts, testthat, rgdal, knitr, rmarkdown
Description: A client for Sensor Observation Services (SOS, see <https://www.opengeospatial.org/standards/sos>) as specified by the Open Geospatial Consortium (OGC). With the package users can retrieve (meta)data from SOS instances and interactively create requests for near real-time observation data based on the available sensors, phenomena, observations etc. using thematic, temporal, and spatial filtering.
License: GPL-2
URL: https://github.com/52North/sos4R
Encoding: UTF-8
LazyLoad: TRUE
ByteCompile: TRUE
BugReports: https://github.com/52North/sos4R/issues
Collate: Constants.R R-Helper.R Class-XML.R Class-OWS.R Class-GML.R Class-SWE.R Class-OM.R Class-SA.R Class-SAMS.R Class-WML_200.R Class-OM_20.R Class-OGC.R Class-SOS.R Class-SOS_100.R Class-SOS_200.R Class-SOS_200_GDA.R Class-SML.R Generic-methods.R OWS-methods.R OWS-methods-parsing.R SOS-methods-parsing.R OM-methods.R OM-methods-coercion.R OM-methods-parsing.R OM_20-methods-parsing.R OM_20-methods.R SA-methods.R GML-methods.R SWE-methods.R SML-methods.R GML-methods-parsing.R SA-methods-parsing.R SWE-methods-parsing.R OGC-methods.R SOS-methods-accessor.R PrintShowStructureSummmary-methods.R SOS-methods-util.R SOS-methods.R SOS-methods-plotting.R SOS-methods-coercion.R SML-methods-util.R SML-methods-coercion.R SOS_200-methods-impl.R SOS_200-methods.R SOS_200-methods-parsing.R SOS_200_methods-gda.R WML_200-methods-parsing.R WML_200-methods.R SAMS-methods-parsing.R Defaults.R Development.R
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-04-13 14:08:40 UTC; daniel
Author: Daniel Nuest [cre, aut] (<https://orcid.org/0000-0002-0024-5046>), Edzer Pebesma [ctb] (<https://orcid.org/0000-0001-8049-7069>), Ben Graeler [ctb] (<https://orcid.org/0000-0001-5443-4304>), Benjamin Pross [ctb], Eike Hinderk Juerrens [ctb], 52°North Initiative for Geospatial Open Source Software GmbH [cph]
Maintainer: Daniel Nuest <daniel.nuest@uni-muenster.de>
Repository: CRAN
Date/Publication: 2019-04-13 22:42:42 UTC

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Package REndo updated to version 2.2.0 with previous version 2.1.0 dated 2019-01-16

Title: Fitting Linear Models with Endogenous Regressors using Latent Instrumental Variables
Description: Fits linear models with endogenous regressor using latent instrumental variable approaches. The methods included in the package are Lewbel's (1997) <doi:10.2307/2171884> higher moments approach as well as Lewbel's (2012) <doi:10.1080/07350015.2012.643126> heteroscedasticity approach, Park and Gupta's (2012) <doi:10.1287/mksc.1120.0718> joint estimation method that uses Gaussian copula and Kim and Frees's (2007) <doi:10.1007/s11336-007-9008-1> multilevel generalized method of moment approach that deals with endogeneity in a multilevel setting. These are statistical techniques to address the endogeneity problem where no external instrumental variables are needed. Note that with version 2.0.0 sweeping changes were introduced which greatly improve functionality and usability but break backwards compatibility.
Author: Raluca Gui [cre, aut], Markus Meierer [aut], Rene Algesheimer [aut], Patrik Schilter [aut]
Maintainer: Raluca Gui <raluca.gui@business.uzh.ch>

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Package MetaLonDA updated to version 1.1.5 with previous version 1.1.0 dated 2018-05-26

Title: Metagenomics Longitudinal Differential Abundance Method
Description: Identify time intervals of differentially abundant metagenomic features in longitudinal studies (Metwally AA, et al., Microbiome, 2018 <doi:10.1186/s40168-018-0402-y>).
Author: Ahmed A. Metwally, Yang Dai, Patricia W. Finn, David L. Perkins
Maintainer: Ahmed A. Metwally <ametwall@stanford.edu>

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Package ctsem updated to version 2.9.0 with previous version 2.8.2 dated 2019-02-03

Title: Continuous Time Structural Equation Modelling
Description: A hierarchical, multivariate, continuous (and discrete) time dynamic modelling package for panel and time series data, using stochastic differential equations. Contains a faster frequentist set of functions using OpenMx for single subject and mixed-effects (random intercepts only) structural equation models, or a hierarchical Bayesian implementation using Stan that allows for random effects and non-linearity over all model parameters. Allows for modelling of multiple noisy measurements of multiple stochastic processes, time varying input / event covariates, and time invariant covariates used to predict the parameters. Bayesian formulation not available on 32 bit Windows systems.
Author: Charles Driver [aut, cre, cph], Manuel Voelkle [aut, cph], Han Oud [aut, cph]
Maintainer: Charles Driver <driver@mpib-berlin.mpg.de>

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Package cna updated to version 2.2.0 with previous version 2.1.1 dated 2018-06-13

Title: Causal Modeling with Coincidence Analysis
Description: Provides comprehensive functionalities for causal modeling with Coincidence Analysis (CNA), which is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009) <doi:10.1177/0049124109339369>, and generalized in Baumgartner & Ambuehl (2018) <doi:10.1017/psrm.2018.45>. CNA is related to Qualitative Comparative Analysis (QCA), but contrary to the latter, it is custom-built for uncovering causal structures with multiple outcomes and it builds causal models from the bottom up by gradually combining single factors to complex dependency structures until the requested thresholds of model fit are met. The new functionalities provided by this package version include functions for evaluating and benchmarking the correctness of CNA's output, a function determining whether a solution is an INUS model, a function bringing non-INUS expressions into INUS form, and a function for identifying cyclic models. The package vignette has been updated accordingly.
Author: Mathias Ambuehl [aut, cre, cph], Michael Baumgartner [aut, cph], Ruedi Epple [ctb], Veli-Pekka Parkkinen [ctb], Alrik Thiem [ctb]
Maintainer: Mathias Ambuehl <mathias.ambuehl@consultag.ch>

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Package censusapi updated to version 0.6.0 with previous version 0.4.1 dated 2018-08-19

Title: Retrieve Data from the Census APIs
Description: A wrapper for the U.S. Census Bureau APIs that returns data frames of Census data and metadata. Available datasets include the Decennial Census, American Community Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, Population Estimates and Projections, and more.
Author: Hannah Recht [aut, cre]
Maintainer: Hannah Recht <hrecht2@gmail.com>

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Package BlockCov updated to version 0.1.1 with previous version 0.1.0 dated 2018-07-01

Title: Estimation of Large Block Covariance Matrices
Description: Computation of large covariance matrices having a block structure up to a permutation of their columns and rows from a small number of samples with respect to the dimension of the matrix. The method is described in the paper Perrot-Dockès et al. (2019) <arXiv:1806.10093>.
Author: M. Perrot-Dock\`es, C. Lévy-Leduc
Maintainer: Marie Perrot-Dockès <marie.perrocks@gmail.com>

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Package ANN2 updated to version 2.3.2 with previous version 2.3.1 dated 2019-03-30

Title: Artificial Neural Networks for Anomaly Detection
Description: Training of neural networks for classification and regression tasks using mini-batch gradient descent. Special features include a function for training autoencoders, which can be used to detect anomalies, and some related plotting functions. Multiple activation functions are supported, including tanh, relu, step and ramp. For the use of the step and ramp activation functions in detecting anomalies using autoencoders, see Hawkins et al. (2002) <doi:10.1007/3-540-46145-0_17>. Furthermore, several loss functions are supported, including robust ones such as Huber and pseudo-Huber loss, as well as L1 and L2 regularization. The possible options for optimization algorithms are RMSprop, Adam and SGD with momentum. The package contains a vectorized C++ implementation that facilitates fast training through mini-batch learning.
Author: Bart Lammers
Maintainer: Bart Lammers <bart.f.lammers@gmail.com>

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Package TSstudio updated to version 0.1.4 with previous version 0.1.3 dated 2018-11-25

Title: Functions for Time Series Analysis and Forecasting
Description: Provides a set of tools for descriptive and predictive analysis of time series data. That includes functions for interactive visualization of time series objects and as well utility functions for automation time series forecasting.
Author: Rami Krispin
Maintainer: Rami Krispin <rami.krispin@gmail.com>

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Package spatstat.local updated to version 3.6-0 with previous version 3.5-7 dated 2018-01-30

Title: Extension to 'spatstat' for Local Composite Likelihood
Description: Extension to the 'spatstat' package, enabling the user to fit point process models to point pattern data by local composite likelihood ('geographically weighted regression').
Author: Adrian Baddeley [aut, cre]
Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>

Diff between spatstat.local versions 3.5-7 dated 2018-01-30 and 3.6-0 dated 2019-04-13

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Package detrendr updated to version 0.6.1 with previous version 0.6.0 dated 2018-11-09

Title: Detrend Images
Description: Detrend fluorescence microscopy image series for fluorescence fluctuation and correlation spectroscopy ('FCS' and 'FFS') analysis. This package contains functionality published in a 2016 paper <doi:10.1093/bioinformatics/btx434> but it has been extended since then with the Robin Hood algorithm and thus contains unpublished work.
Author: Rory Nolan [aut, cre] (<https://orcid.org/0000-0002-5239-4043>), Luis Alvarez [ctb, cph] (<https://orcid.org/0000-0003-1316-1906>), Sergi Padilla-Parra [ctb, ths, cph] (<https://orcid.org/0000-0002-8010-9481>)
Maintainer: Rory Nolan <rorynoolan@gmail.com>

Diff between detrendr versions 0.6.0 dated 2018-11-09 and 0.6.1 dated 2019-04-13

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Package padr updated to version 0.4.2 with previous version 0.4.1 dated 2018-06-26

Title: Quickly Get Datetime Data Ready for Analysis
Description: Transforms datetime data into a format ready for analysis. It offers two core functionalities; aggregating data to a higher level interval (thicken) and imputing records where observations were absent (pad).
Author: Edwin Thoen
Maintainer: Edwin Thoen <edwinthoen@gmail.com>

Diff between padr versions 0.4.1 dated 2018-06-26 and 0.4.2 dated 2019-04-13

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New package fic with initial version 1.0.0
Package: fic
Title: Focused Information Criteria for Model Comparison
Version: 1.0.0
Date: 2019-04-09
Authors@R: c(person("Christopher","Jackson", role=c("cre","aut"), email="chris.jackson@mrc-bsu.cam.ac.uk", comment="package design and programming"), person("Gerda","Claeskens", role="aut", email="gerda.claeskens@kuleuven.be", comment="method development and design advice"), person("Howard","Thom",email="howard.thom@bristol.ac.uk", role="ctb") )
Description: Compares how well different models estimate a quantity of interest (the "focus") so that different models may be preferred for different purposes. Comparisons within any class of models fitted by maximum likelihood are supported, with shortcuts for commonly-used classes such as generalised linear models and parametric survival models. The methods originate from Claeskens and Hjort (2003) <doi:10.1198/016214503000000819> and Claeskens and Hjort (2008, ISBN:9780521852258).
Maintainer: Christopher Jackson <chris.jackson@mrc-bsu.cam.ac.uk>
Depends: R (>= 2.10)
Imports: stats, numDeriv, mvtnorm, ggplot2, scales, survival, tensor, abind
URL: https://github.com/chjackson/fic
BugReports: https://github.com/chjackson/fic/issues
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown, testthat, msm(>= 1.6.6), flexsurv, sn, gapminder, GGally
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-04-12 11:20:35 UTC; chris
Author: Christopher Jackson [cre, aut] (package design and programming), Gerda Claeskens [aut] (method development and design advice), Howard Thom [ctb]
Repository: CRAN
Date/Publication: 2019-04-13 08:32:39 UTC

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New package equivUMP with initial version 0.1.1
Package: equivUMP
Type: Package
Title: Uniformly Most Powerful Invariant Tests of Equivalence
Version: 0.1.1
Authors@R: person("Thoralf", "Mildenberger", email = "mild@zhaw.ch", role = c("aut", "cre"))
Description: Implementation of uniformly most powerful invariant equivalence tests for one- and two-sample problems (paired and unpaired) as described in Wellek (2010, ISBN:978-1-4398-0818-4). Also one-sided alternatives (non-inferiority and non-superiority tests) are supported. Basically a variant of a t-test with (relaxed) null and alternative hypotheses exchanged.
License: GPL (>= 2)
URL: https://github.com/thmild/equivUMP
BugReports: https://github.com/thmild/equivUMP/issues
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-04-12 09:45:27 UTC; thoralf
Author: Thoralf Mildenberger [aut, cre]
Maintainer: Thoralf Mildenberger <mild@zhaw.ch>
Repository: CRAN
Date/Publication: 2019-04-13 08:22:52 UTC

More information about equivUMP at CRAN
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New package CTAShiny with initial version 0.1.0
Package: CTAShiny
Type: Package
Title: Interactive Application for Working with Contingency Tables
Version: 0.1.0
Author: Kartikeya Bolar
Maintainer: Kartikeya Bolar <kartikeya.bolar@tapmi.edu.in>
Description: An interactive application for working with contingency Tables. The application has a template for solving contingency table problems like chisquare test of independence,association plot between two categorical variables. Runtime examples are provided in the package function as well as at <https://jarvisatharva.shinyapps.io/CategoricalDataAnalysis/>.
License: GPL-2
Encoding: UTF-8
LazyData: TRUE
Depends: R (>= 3.0.3)
Imports: shiny,shinyMatrix,epitools,rpivotTable
RoxygenNote: 6.1.0
NeedsCompilation: no
Packaged: 2019-04-12 08:47:04 UTC; KARTIKEYA
Repository: CRAN
Date/Publication: 2019-04-13 08:22:55 UTC

More information about CTAShiny at CRAN
Permanent link

Package wildlifeDI updated to version 0.3.0 with previous version 0.2 dated 2014-12-15

Title: Calculate Indices of Dynamic Interaction for Wildlife Tracking Data
Description: Dynamic interaction refers to spatial-temporal associations in the movements of two (or more) animals. This package provides tools for calculating a suite of indices used for quantifying dynamic interaction with wildlife telemetry data. For more information on each of the methods employed see the references within. The package (as of version 0.3) also has new tools for automating contact analysis in large tracking datasets. The package draws heavily on the classes and methods developed in the 'adehabitat' packages.
Author: Jed Long [aut, cre] (<https://orcid.org/0000-0002-2815-0399>)
Maintainer: Jed Long <jed.long@uwo.ca>

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Package V8 updated to version 2.2 with previous version 2.1 dated 2019-03-16

Title: Embedded JavaScript Engine for R
Description: An R interface to Google's open source JavaScript engine. This package can now be compiled either with V8 version 6 or 7 (LTS) from nodejs or with the legacy 3.14/3.15 branch of V8.
Author: Jeroen Ooms [aut, cre] (<https://orcid.org/0000-0002-4035-0289>)
Maintainer: Jeroen Ooms <jeroen@berkeley.edu>

Diff between V8 versions 2.1 dated 2019-03-16 and 2.2 dated 2019-04-13

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Package seqCBS updated to version 1.2.1 with previous version 1.2 dated 2011-05-22

Title: Copy Number Profiling using Sequencing and CBS
Description: This is a method for DNA Copy Number Profiling using Next-Generation Sequencing. It has new model and test statistics based on non-homogeneous Poisson Processes with change point models. It uses an adaptation of Circular Binary Segmentation. Also included are methods for point-wise Bayesian Confidence Interval and model selection method for the change-point model. A case and a control sample reads (normal and tumor) are required.
Author: Jeremy J. Shen, Nancy R. Zhang
Maintainer: Jeremy J. Shen <jeremyjshen@gmail.com>

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Package phateR updated to version 0.4.0 with previous version 0.2.9 dated 2018-09-11

Title: PHATE - Potential of Heat-Diffusion for Affinity-Based Transition Embedding
Description: PHATE is a tool for visualizing high dimensional single-cell data with natural progressions or trajectories. PHATE uses a novel conceptual framework for learning and visualizing the manifold inherent to biological systems in which smooth transitions mark the progressions of cells from one state to another. To see how PHATE can be applied to single-cell RNA-seq datasets from hematopoietic stem cells, human embryonic stem cells, and bone marrow samples, check out our preprint on bioRxiv at <http://biorxiv.org/content/early/2017/03/24/120378>.
Author: Krishnan Srinivasan [aut], Scott Gigante [cre] (<https://orcid.org/0000-0002-4544-2764>)
Maintainer: Scott Gigante <scott.gigante@yale.edu>

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Package nodbi updated to version 0.2.0 with previous version 0.1.0 dated 2018-08-01

Title: 'NoSQL' Database Connector
Description: Simplified document database manipulation and analysis, including support for many 'NoSQL' databases, including document databases ('Elasticsearch', 'CouchDB', 'MongoDB'), 'key-value' databases ('Redis'), and other 'NoSQL' types ('etcd').
Author: Scott Chamberlain [aut, cre] (<https://orcid.org/0000-0003-1444-9135>), Rich FitzJohn [aut], Jeroen Ooms [aut]
Maintainer: Scott Chamberlain <sckott@protonmail.com>

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Package complmrob updated to version 0.6.2 with previous version 0.6.1 dated 2015-10-18

Title: Robust Linear Regression with Compositional Data as Covariates
Description: Robust regression methods for compositional data. The distribution of the estimates can be approximated with various bootstrap methods. These bootstrap methods are available for the compositional as well as for standard robust regression estimates. This allows for direct comparison between them.
Author: David Kepplinger <david.kepplinger@gmail.com>
Maintainer: David Kepplinger <david.kepplinger@gmail.com>

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