Sun, 14 May 2023

Package spatstat.linnet updated to version 3.1-1 with previous version 3.1-0 dated 2023-04-14

Title: Linear Networks Functionality of the 'spatstat' Family
Description: Defines types of spatial data on a linear network and provides functionality for geometrical operations, data analysis and modelling of data on a linear network, in the 'spatstat' family of packages. Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks. Data types defined on a network include point patterns, pixel images, functions, and tessellations. Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Mont [...truncated...]
Author: Adrian Baddeley [aut, cre, cph] , Rolf Turner [aut, cph] , Ege Rubak [aut, cph] , Greg McSwiggan [aut, cph], Tilman Davies [ctb, cph], Mehdi Moradi [ctb, cph], Suman Rakshit [ctb, cph], Ottmar Cronie [ctb]
Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>

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Package RcppSimdJson updated to version 0.1.10 with previous version 0.1.9 dated 2023-01-21

Title: 'Rcpp' Bindings for the 'simdjson' Header-Only Library for 'JSON' Parsing
Description: The 'JSON' format is ubiquitous for data interchange, and the 'simdjson' library written by Daniel Lemire (and many contributors) provides a high-performance parser for these files which by relying on parallel 'SIMD' instruction manages to parse these files as faster than disk speed. See the <arXiv:1902.08318> paper for more details about 'simdjson'. This package parses 'JSON' from string, file, or remote URLs under a variety of settings.
Author: Dirk Eddelbuettel, Brendan Knapp, Daniel Lemire
Maintainer: Dirk Eddelbuettel <edd@debian.org>

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Package flimo updated to version 0.1.5 with previous version 0.1.4 dated 2023-04-13

Title: Fixed Landscape Inference Method
Description: Likelihood-free inference method for stochastic models. Uses a deterministic optimizer on simple simulations of the model that are performed with a prior drawn randomness by applying the inverse transform method. Is designed to work on its own and also by using the Julia package 'Jflimo' available on the git page of the project: <https://metabarcoding.org/flimo>.
Author: Sylvain Moinard [aut, cre]
Maintainer: Sylvain Moinard <sylvain.moinard@univ-grenoble-alpes.fr>

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Package hdiVAR updated to version 1.0.2 with previous version 1.0.1 dated 2020-10-07

Title: Statistical Inference for Noisy Vector Autoregression
Description: The model is high-dimensional vector autoregression with measurement error, also known as linear gaussian state-space model. Provable sparse expectation-maximization algorithm is provided for the estimation of transition matrix and noise variances. Global and simultaneous testings are implemented for transition matrix with false discovery rate control. For more information, see the accompanying paper: Lyu, X., Kang, J., & Li, L. (2023). "Statistical inference for high-dimensional vector autoregression with measurement error", Statistica Sinica.
Author: Xiang Lyu [aut, cre], Jian Kang [aut], Lexin Li [aut]
Maintainer: Xiang Lyu <xianglyu.public@gmail.com>

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Package Rbeast updated to version 0.9.9 with previous version 0.9.8 dated 2023-05-11

Title: Bayesian Change-Point Detection and Time Series Decomposition
Description: Interpretation of time series data is affected by model choices. Different models can give different or even contradicting estimates of patterns, trends, and mechanisms for the same data--a limitation alleviated by the Bayesian estimator of abrupt change,seasonality, and trend (BEAST) of this package. BEAST seeks to improve time series decomposition by forgoing the "single-best-model" concept and embracing all competing models into the inference via a Bayesian model averaging scheme. It is a flexible tool to uncover abrupt changes (i.e., change-points), cyclic variations (e.g., seasonality), and nonlinear trends in time-series observations. BEAST not just tells when changes occur but also quantifies how likely the detected changes are true. It detects not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is applicable to real-valued time series data of all kinds, be it for remote sensing, economics, climate sciences, ecology, and hydrology. Example applications in [...truncated...]
Author: Yang Li [aut], Tongxi Hu [aut], Xuesong Zhang [aut], Kaiguang Zhao [aut, cre], Jack Dongarra [ctb], Cleve Moler [ctb]
Maintainer: Kaiguang Zhao <zhao.1423@osu.edu>

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Package lessR updated to version 4.2.9 with previous version 4.2.8 dated 2023-03-22

Title: Less Code, More Results
Description: Each function accomplishes the work of multiple standard R functions. For example, two function calls, Read() and CountAll(), read the data and generate summary statistics for all variables in the data frame, plus histograms and bar charts as appropriate. Other functions provide for comprehensive summary statistics via pivot tables, a comprehensive regression analysis, ANOVA and t-test, visualizations including the Violin/Box/Scatter plot for a numerical variable, bar chart, histogram, box plot, density curves, calibrated power curve, reading multiple data formats with the same function call, variable labels, color themes, and Trellis graphics. Also includes a confirmatory factor analysis of multiple indicator measurement models, pedagogical routines for data simulation such as for the Central Limit Theorem, generation and rendering o regression instructions for interpretative output, and interactive visualizations.
Author: David Gerbing, The School of Business, Portland State University
Maintainer: David W. Gerbing <gerbing@pdx.edu>

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Package dslabs updated to version 0.7.5 with previous version 0.7.4 dated 2021-04-30

Title: Data Science Labs
Description: Datasets and functions that can be used for data analysis practice, homework and projects in data science courses and workshops. 26 datasets are available for case studies in data visualization, statistical inference, modeling, linear regression, data wrangling and machine learning.
Author: Rafael A. Irizarry, Amy Gill
Maintainer: Rafael A. Irizarry <rafael_irizarry@dfci.harvard.edu>

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Package nodbi updated to version 0.9.4 with previous version 0.9.3 dated 2023-04-23

Title: 'NoSQL' Database Connector
Description: Simplified document database access and manipulation, providing a common API across supported 'NoSQL' databases 'Elasticsearch', 'CouchDB', 'MongoDB' as well as 'SQLite/JSON1', 'PostgreSQL', and 'DuckDB'.
Author: Ralf Herold [aut, cre] , Scott Chamberlain [aut] , Rich FitzJohn [aut], Jeroen Ooms [aut]
Maintainer: Ralf Herold <ralf.herold@mailbox.org>

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Package BranchGLM updated to version 2.1.1 with previous version 2.1.0 dated 2023-03-04

Title: Efficient Branch and Bound Variable Selection for GLMs using 'RcppArmadillo'
Description: Performs efficient and scalable glm best subset selection using a novel implementation of a branch and bound algorithm. To speed up the model fitting process, a range of optimization methods are implemented in 'RcppArmadillo'. Parallel computation is available using 'OpenMP'.
Author: Jacob Seedorff [aut, cre]
Maintainer: Jacob Seedorff <jwseedorff@uiowa.edu>

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Package pRepDesigns updated to version 1.1.0 with previous version 1.0.0 dated 2022-09-01

Title: Partially Replicated (p-Rep) Designs
Description: Early generation breeding trials are to be conducted in multiple environments where it may not be possible to replicate all the lines in each environment due to scarcity of resources. For such situations, partially replicated (p-Rep) designs have wide application potential as only a proportion of the test lines are replicated at each environment. A collection of several utility functions related to p-Rep designs have been developed. Here, the package contains six functions for a complete stepwise analytical study of these designs. Five functions pRep1(), pRep2(), pRep3(), pRep4() and pRep5(), are used to generate five new series of p-Rep designs and also compute average variance factors and canonical efficiency factors of generated designs. A fourth function NCEV() is used to generate incidence matrix (N), information matrix (C), canonical efficiency factor (E) and average variance factor (V). This function is general in nature and can be used for studying the characterization properti [...truncated...]
Author: Vinaykumar L.N. [aut, cre], Cini Varghese [aut, ctb], Mohd Harun [aut, ctb], Ashutosh Dalal [aut, ctb], Sayantani Karmakar [aut, ctb], Vinayaka [aut, ctb]
Maintainer: Vinaykumar L.N. <vinaymandya123@gmail.com>

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Package lehdr updated to version 1.1.1 with previous version 1.0.1 dated 2022-02-04

Title: Grab Longitudinal Employer-Household Dynamics (LEHD) Flat Files
Description: Designed to query Longitudinal Employer-Household Dynamics (LEHD) workplace/residential association and origin-destination flat files and optionally aggregate Census block-level data to block group, tract, county, or state. Data comes from the LODES FTP server <https://lehd.ces.census.gov/data/lodes/LODES7/>.
Author: Jamaal Green [cre, aut], Liming Wang [aut], Dillon Mahmoudi [aut], Matthew Rogers [ctb], Kyle Walker [ctb]
Maintainer: Jamaal Green <jamaal.green@gmail.com>

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Package annmatrix updated to version 0.1.2 with previous version 0.1.1 dated 2023-05-02

Title: Annotated Matrix: Matrices with Persistent Row and Column Annotations
Description: Implements persistent row and column annotations for R matrices. The annotations associated with rows and columns are preserved after subsetting, transposition, and various other matrix-specific operations. Intended use case is for storing and manipulating genomic datasets which typically consist of a matrix of measurements (like gene expression values) as well as annotations about rows (i.e. genomic locations) and annotations about columns (i.e. meta-data about collected samples). But 'annmatrix' objects are also expected to be useful in various other contexts.
Author: Karolis Koncevicius [aut, cre]
Maintainer: Karolis Koncevicius <karolis.koncevicius@gmail.com>

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Package povcalnetR (with last version 0.1.1) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2020-05-19 0.1.1
2019-10-02 0.1.0

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Package rdracor (with last version 0.7.2) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2023-01-23 0.7.2

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Package nimble updated to version 0.13.2 with previous version 0.13.1 dated 2022-12-14

Title: MCMC, Particle Filtering, and Programmable Hierarchical Modeling
Description: A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at <htt [...truncated...]
Author: Perry de Valpine [aut], Christopher Paciorek [aut, cre], Daniel Turek [aut], Nick Michaud [aut], Cliff Anderson-Bergman [aut], Fritz Obermeyer [aut], Claudia Wehrhahn Cortes [aut] , Abel Rodrìguez [aut] , Duncan Temple Lang [aut] , Sally Paganin [aut [...truncated...]
Maintainer: Christopher Paciorek <paciorek@stat.berkeley.edu>

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Package GInSARCorW updated to version 1.15.8 with previous version 1.15.6 dated 2020-06-26

Title: GACOS InSAR Correction Workflow
Description: A workflow for correction of Differential Interferometric Synthetic Aperture Radar (DInSAR) atmospheric delay base on Generic Atmospheric Correction Online Service for InSAR (GACOS) data and correction algorithms proposed by Chen Yu. This package calculate the Both Zenith and LOS direction (User Depend). You have to just download GACOS product on your area and preprocessed D-InSAR unwrapped images. Cite those references and this package in your work, when using this framework. References: Yu, C., N. T. Penna, and Z. Li (2017) <doi:10.1016/j.rse.2017.10.038>. Yu, C., Li, Z., & Penna, N. T. (2017) <doi:10.1016/j.rse.2017.10.038>. Yu, C., Penna, N. T., and Li, Z. (2017) <doi:10.1002/2016JD025753>.
Author: Subhadip Datta
Maintainer: Subhadip Datta <subhadipdatta007@gmail.com>

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