Over the couple of months there have been a bunch of smaller releases to packages in the tidyverse . This includes: forcats 0.2.0, for working with factors. readr 1.1.0, for reading flat-files from disk. stringr 1.2.0, for manipulating strings. tibble 1.3.0, a modern re-imagining of the data frame. This blog post summarises the most important new features, and points to the full release notes where you can learn more. (If you’ve never heard of the tidyverse before, it’s an set of packages that are designed to work together to help you do data science. The best place to learn all about it is R for Data Science .) forcats 0.2.0 forcats has three new functions: as_factor() is a generic version of as.factor(), which creates factors from character vectors ordered by appearance, rather than alphabetically. This ensures means that as_factor(x) will always return the same result, regardless of the current locale. fct_other() makes it easier to convert selected levels to “other”: See the full list of other changes in the release notes . stringr 1.2.0 This release includes a change to the API: str_match_all() now returns NA if an optional group doesn’t match (previously it returned “”). This is more consistent with str_match() and other match failures. There are three new features: In str_replace(), replacement can now be a function. The function is once for each match and its return value will be used as the replacement. New str_which() mimics grep() A new vignette ( vignette("regular-expressions") ) describes the details of the regular expressions supported by stringr. The main vignette ( vignette("stringr") ) has been updated to give a high-level overview of the package. See the full list of other changes in the release notes . readr 1.1.0 readr gains two new features: All write_*() functions now support connections. This means that that you can write directly to compressed formats such as .gz, bz2 or .xz (and readr will automatically do so if you use one of those suffixes). write_csv ( iris , "iris.csv.bz2" ) parse_factor(levels = NULL) and col_factor(levels = NULL) will produce a factor column based on the levels in the data, mimicing factor parsing in base R (with the exception that levels are created in the order seen). See the full list of other changes in the release notes . tibble 1.3.0 tibble has one handy new function: deframe() is the opposite of enframe(): it turns a two-column data frame into a named vector. df - tibble ( x = c ( "a" , "b" , "c" ) , y = 1 : 3 ) deframe ( df ) # a b c # 1 2 3
f77_to_f90 , a program which converts a FORTRAN77 file to FORTRAN90 format, by Michael MetcalfSe; f90 , programs which illustrate the use of various FORTRAN90 programming features; f90_calls_c , programs which illustrates how a FORTRAN90 program can call a C function. f90_calls_c_and_mpi , programs which illustrate how a FORTRAN90 program can call a C function while executing under the MPI parallel programming environment. f90_calls_c++ , programs which illustrate how a FORTRAN90 program can call a C++ function. f90_calls_c++_and_mpi , programs which illustrate how a FORTRAN90 program can call a C++ function while executing under the MPI parallel programming environment. f90_calls_matlab , programs which illustrate how a FORTRAN90 program can interact with MATLAB; f90_condor , programs which illustrate how a FORTRAN90 program can be run in batch mode using the condor queueing system. f90_files , programs which illustrate the use of files. f90_intrinsics , programs which illustrate the use of various FORTRAN90 intrinsic functions and subroutines; f90_module , programs which illustrate the use of FORTRAN90's module feature. f90_random , programs which illustrate FORTRAN90's random number generator (RNG) routines. f90_return , programs which illustrates how a FORTRAN90 program can return a program status value to the calling environment. f90_to_f77 , a program which assists in the conversion of a FORTRAN90 file to FORTRAN77 format.
As specific features of the TALYS package we mention • In general, an exact implementation of many of the latest nuclear models for direct, compound,pre-equilibrium and fission reactions. • A continuous, smooth description of reaction mechanisms over a wide energy range (0.001- 200MeV) and mass number range (12 A 339). • Completely integrated optical model and coupled-channels calculations by the ECIS-06 code . • Incorporation of recent optical model parameterisations for many nuclei, both phenomenological (optionally including dispersion relations) and microscopical. • Total and partial cross sections, energy spectra, angular distributions, double-differential spectra and recoils. • Discrete and continuum photon production cross sections. • Excitation functions for residual nuclide production , including isomeric cross sections . • An exact modeling of exclusive channel cross sections, e.g. (n, 2np), spectra, and recoils. • Automatic reference to nuclear structure parameters as masses, discrete levels, resonances, level density parameters, deformation parameters, fission barrier and gamma-ray parameters , generally from the IAEA Reference Input Parameter Library . • Various width fluctuation models for binary compound reactions and, at higher energies, multiple Hauser-Feshbach emission until all reaction channels are closed. • Various phenomenological and microscopic level density models. • Various fission models to predict cross sections and fission fragment and product yields, and neutron multiplicities. • Models for pre-equilibrium reactions, and multiple pre-equilibrium reactions up to any order. • Generation of parameters for the unresolved resonance range. • Astrophysical reaction rates using Maxwellian averaging. • Medical isotope production yields as a function of accelerator energy and beam current. • Option to start with an excitation energy distribution instead of a projectile-target combination,helpful for coupling TALYS with intranuclear cascade codes or fission fragment studies. • Use of systematics if an adequate theory for a particular reaction mechanism is not yet available or implemented, or simply as a predictive alternative for more physical nuclear models. • Automatic generation of nuclear data in ENDF-6 format (not included in the free release). • Automatic optimization to experimental data and generation of covariance data (not included in the free release). • A transparent source program. • Input/output communication that is easy to use and understand. • An extensive user manual. • A large collection of sample cases.
by Lina Zeldovich Do you diligently read product labels and ask dozens of questions before buying a toothbrush? Do you need to know exactly how a new face cream will repair your skin while you sleep? You’re probably an “explanation fiend 魔鬼；能手；成癖者 :" a consumer who makes decisions based on comprehensive research. On the other hand, if endless descriptions of features and functionalities bog you down 使你陷入困境 , you’re probably an intuitive buyer, or an “explanation foe,” a new study suggests . A team led by Philip Fernbach , professor of marketing at the University of Colorado, and Steven Sloman , a cognitive psychologist at Brown, found that consumers fall into two distinctive groups: intuitive shoppers who prefer only basic information about a product before bagging it, and data-driven buyers who seek full details. To identify their “shopping style,” study participants were evaluated on something called the Cognitive Reflection Test (CRT) . They answered three tricky questions, each of which had an intuitive answer that was incorrect and a correct answer that required a bit more deliberation. Nearly half of the participants got all three questions wrong, and only ten percent thought the questions through enough to get all three right. Individuals with high CRT scores proved to be explanation fiends who analyze and question every bit of information, while those who picked the intuitive, but wrong, answers turned out to be explanation foes, who see too much data as a burden. Both types of shopper want to understand how a product works before deciding its worth, but their methods of building that understanding are different. When "explanation fiends" learned more about a product, they trusted it more and were more willing to buy it. But "explanation foes" felt confident in their assessment of a product—even when they were light on information about it. Both fiends and foes 敌人；反对者；危害物 also had to generate their own explanations of how products work, thus revealing what they actually understood. Data-driven explanation fiends began with a conservative assessment of their understanding, which didn’t change once they explained their perception of the product. On the contrary, the intuitive explanation foes demonstrated something called "the illusion of explanatory depth:" They started with a strong sense of understanding that plummeted as they tried to explain what they actually understood. Being an explanation fiend or foe is an indication of personality, not intelligence, Fernbach notes. However, marketers might be able to predict how certain types of consumers will respond to varying levels of detail. “In the general population there will be more explanation foes than among technical population,” for example, Fernbach explains. “Engineers will be more interested in how things work than an average consumer.” Such knowledge, he says, can help marketers tailor the explanation level for the community of consumers they are targeting, satiating the thinkers and shielding v. 保护（shield的ing形式） intuitive minds from information overload. 文献下载地址： http://www.jstor.org/stable/info/10.1086/667782 若您无法下载 可求助
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