Since the introduction of persuasive design into the development of modern technologies, researchers have considered how aspects of persuasion may be incorporated to push the user of the technology to do good. One of the areas that has seen the application of such attempts is apps developed to tackle behaviours related to the ongoing climate crisis.
This project explored the use of persuasive strategies from a critical design perspective. Through the evaluation and redesign of the CERO Challenge app, developed to promote pro-environmental travel behaviours, we considered which strategies align with long term pro-environmental behaviour change and how they affect the design.
This project was a part of the Advanced Project Course in Interactive Media Technology, where the organisation CERO (Climate and Economic Research in Organizations) provided the project brief.
Working alongside classmates Diego Martin, Fredilyn Villarin, Marco Koivisto and Rafael Lucena, the project was conducted under the guidance of our supervisor Elina Eriksson, who brought expertise within ICT for sustainability.
While all five of us contributed in all aspects of the project, Diego and Marco took the design lead. Being highly engaged in behavioural psychology and its relation to modern app and web development, I took the scientific lead and conducted most of the research that the project was based on top of.
The mission of CERO is to help companies find economically sustainable ways of reducing their emissions, particularly with focus on the travel habits of the company’s workforce. CERO Challenge is an app developed by the team behind CERO, which had the purpose of tracking individual travel behaviour and to enable users of the app to challenge each other to reduce emissions. At its current state it does not have a connection to the CERO organisation and is no longer in use.
Our team was given the challenge to help the client resurrect the app, turning it to a bottom-up tool to help companies raise awareness and engagement in their CERO goals on employee level. And in doing so, exploring alternative motivations, besides the common gamification and engagement tactics, that could drive the application.
The previous application struggled with intuitive navigation and the user was repetitively asked to log the data of each trip. A first objective was therefore to make this process less cumbersome for the user, while still keeping the data accurate.
The app also struggled with displaying its data to the user in a meaningful way. We therefore sought to explore multiple ways in which travel data could be presented to not only making travel impact easier to understand, but to do so in a way that prompted the user to engage in reducing their travel impact. We interviewed several stakeholders that were involved in the implementation of CERO at their workplace, to gain an understanding of their ideal expectations on the app. Both as someone who wanted to spur their colleagues engagement, but also as with themselves as the user.
Being out of use, it also needed an aesthetic update to adhere to app design guidelines. The client also wished to more tightly connect the app with the organisations work, including a leaderboard of the top performing companies. But foremost, we needed to tackle the challenge of making the app promote and encourage pro-environmental behaviour change, and that was not an easy feat!
Making use of technology to promote pro-environmental behaviours is a difficult task for several reasons. Today, many modern apps make use of persuasive design to catch and keep the user’s attention. This includes features such as attention-grabbing colours, notifications, heavily personalised content and gamification. However, researchers exploring the field of persuasive design used for promoting sustainability [1, 2] have found that many of these features significantly clash with attempts to encourage pro-environmental behaviours.
As a part of our project we dealt with the challenges introduced by Schwartz’s circumplex structure of values [3], particularly self-enhancement vs. self-transcendence values and its implications for understanding human motivation.
Apps developed in Western societies, just like the these societies in general, are highly permeated by self-enhancement values, i.e. values related to the welfare of oneself. Such as pursuing one’s own interests and concern for one’s own success, where social comparison and recognition are highly valued. Self-enhancement values are also known to undermine pro-environmental behaviour change. On the other hand, self-transcendence values; the welfare and interest in others, are known for bringing about pro-environmental behaviours through for example social facilitation and cooperation. And it was with this as our perspective that we approached the design.
To reduce some of the repetitiveness of the app (the user having to personally log each trip) we decided to aim for easiness over perfect accuracy. Rather than aiming for automatic tracking solutions, we worked from the basis of us humans as highly habitual; most of us have very distinct travel patterns to and from work. Asking the user for their travel habits once (with the option to update this in the app settings later) makes the experience more convenient.
It was clear from the link to the clients work with the organisation that we would be working with carbon calculation of some sort. At the current standpoint in time sustainability is often presented to users as either side of a coin: this is the kilograms of CO2 you have wasted, or, this is the kilograms of CO2 you saved. But what difference does a kg of CO2 make? And what implications do the words “waste” versus “save” have on the user making the effort? We examined these questions with our interviewees and found positive reinforcement to be the way forward. Relating to nature’s natural carbon savers ‒ trees, was the preferred way of materialising carbon emission savings into something comprehensible.
We shifted the core of the app to be all about community. Joining challenges together and endorsing one another to bring in self-transcendence values; social facilitation and cooperation.
Prompting the user for the nitty-gritty details only once makes for a more pleasant experience.
Rather than the one-to-one competition style, we aimed for cooperation through enabling employees to join in on the company defined challenge of their choice.
While the old version made other people your competitors, the new one focuses on people working together towards a goal, contributing to each other’s successes.
To add a sense of community and enabling users to endorse each other, a feed was created for users’ achievements. Posts are autogenerated when a user contributes to a challenge, but can be edited and personalised, and it’s up to the user whether to post them or not.
"Alone we can do so little; together we can do so much”‒while we let some gamification aspects persist, we shifted the focus onto the collective contribution through displaying team effort first.
Leaderboards tend to encourage the ones at the very top, and discourage the ones at the bottom. By highlighting the total contribution we shift the focus toward communal effort.
For me, this project was particularly enthralling to take on because it gave us the opportunity to think about our design choices from a new perspective. It is so easy to go along with established design and persuasion tactics employed in most applications today, without critically evaluating what motivations they build on. And in extension to that, how they can shape our way of thinking and behaving.
This project was also extremely challenging because we had to find design solutions aiming to promote values which our Western society, to most part, does not regularly reflect over or attend to.
Our final design was very positively received by the client as well as the sustainable ICT research community at KTH. It was praised for its new approach to tackling pro-environmental behaviour change and for its attempt to balance out the “negative” drivers and motivators commonly employed in apps with the focus on communal efforts and social support.
Two members of our team continued working with the client in their master thesis projects, to further examine the potential of the app with users.


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import config.ApplicationConfig @import models.upscan.UpscanInitiateResponse @import views.html.helpers._ @import views.ViewHelpers @import uk.gov.hmrc.govukfrontend.views.html.components._ @this(gmpMain: gmp_main, viewHelpers: ViewHelpers) @(upscanInitiateResponse: UpscanInitiateResponse)(implicit request: Request[_], messages: Messages,ac:ApplicationConfig) @gmpMain(title = Messages("gmp.fileupload.header") + " - " + Messages("service.title") + " - " + Messages("gov.uk")) { @includes.back_link(Messages("gmp.fileupload.header"))
@Html(Messages("gmp.fileupload.hint", routes.AssetsController.at("csv/GMP_template_and_instructions.zip"), Messages("gmp.fileupload.header")))


{% extends "base.html" %} {% block pagetitle %}Astro-Coffee@Example{% end %} {% block pageheader %} {% include "topbar.html" %}
Every weekday, the {{ coffee_department }} at {{ coffee_institution }} gathers in {{ coffee_building }}'s {{ coffee_room }} to discuss the latest arXiv astro-ph posting at 10:30 AM over coffee. Visitors to the department are always welcome to join these discussions.
This astro-coffee server is meant to help organize the discussion. It automatically finds and highlights papers from local authors, and allows the community to vote for articles they want to talk about. New paper listings are fetched from the arXiv between 00:25 and 00:35 UTC Sunday through Thursday during the week.
Currently, voting on papers is open between 8:45 PM the night before and 10:28 AM the morning of Astro-coffee. Everyone gets five votes (one per paper) to use as they please.


{{ question.question_text }}
{% for choice in question.choice_set.all %}
{{ choice.choice_text }} -- {{ choice.votes }} vote{{ choice.votes|pluralize }}
{% endfor %}
Vote again?
Back to polls


HANNIBAL --------
Ten years ago, director Jonathan Demme gave us a serial killer movie that was a masterpiece of its genre in "The Silence of the Lambs." Taking over the reins, director Ridley Scott ("Gladiator") brings Thomas Harris' third novel to feature Hannibal Lecter to the screen. Julianne Moore ("The End of the Affair") takes over the role of Clarice Starling, Gary Oldman joins the cast as the series' ever-present, yet changing, second villain Mason Verger, and Oscar winner Anthony Hopkins returns as the titular "Hannibal."
LAURA:
In the thirty odd minutes of screen time it takes for Hannibal to make an appearance, we discover that prison orderly Barney (Frankie R. Faijon, "The Silence of the Lambs") has been making a buck or two selling Hannibal Lecter items to a lucrative underground market. The unfathomably wealthy (home is the Biltmore Estate, which served as Melvyn Douglas and Shirley Maclaine's mansion in "Being There") and hideously disfigured Verger is a client of Barney's, being the only surviving victim of Lecter's. Verger is obsessed with exacting revenge and is offering a private, three million dollar reward for Lecter.
Meanwhile, Clarice Starling has become the whipping girl of the FBI after her high profile drug ring bust results in five deaths, raining bad publicity upon the Bureau. Starling's woes have practically been engineered by Justice Department official Paul Krendler (Ray Liotta, "Goodfellas"), a corrupt man in cahoots with Verger who puts Starling back on the Lecter case to lure him out of the shadows.
Across the Atlantic, Inspector Rinaldo Pazzi (Giancarlo Giannini, "Swept Away") meets Dr. Fell, a new library curator, while investigating the disappearance of his predecessor. Clarice's work in DC causes a request to go through Pazzi's offices and he comes to realize the charming Fell is the notorious Lecter. Rather than cooperate with the FBI in his official capacity, Pazzi, the husband of a much younger, beautiful wife, makes a fatal mistake by attempting to reap the monetary reward.
Screenwriters David Mamet ("State and Main") and Steven Zaillian ("Schindler's List") have pretty faithfully adapted the Thomas Harris novel for the screen. Yes, the notorious ending of the novel has been modified somewhat, but the spirit is essentially the same and Starling's actions are better justified. Unlike the first film, this Hannibal is presented as a thinner of the herd, a killer of the rude and greedy.
Hopkins, an actor who can serve up ham (see Coppola's "Dracula"), is once again note perfect as Dr. Lecter. The performance accounts for ten years of laying low while enjoying life's beauty and pleasures. There's humor in this performance (listen to his delivery of 'Okee dokey') as well as menace (watch him approach Inspector Pazzi, barefoot).
Moore is in a difficult spot reprising Foster's role. "Silence" was about Clarice and "Hannibal" is not. Moore is OK, playing career disillusionment by seeming tired and washed out, although she does show spark with the backbone she presents to male colleagues looking to mow her down.
The uncreditted Oldman is unrecogniable beneath the work of a special effects makeup team which presents him with a reconstructed face resembling a Leatherface mask from "The Texas Chainsaw Massacre." Oldman does his job well, making Verger a more despicable character than Lecter, yet his diction is too clear for a man with no lips.
The best support comes from Italian mainstay Giannini, who gains sympathy for his actions and fear for his character as he treads too close, too often to a danger he does not comprehend. Liotta does his best work when he gets brainy.
The production is first rate across the board, if not at the masterpiece level of "Silence." Where that film used Hannibal's cells as descents into hell, this film uses motifs of pigeons, boars, and yes, even a lamb. Location work in Florence is notable, including an original outdoor opera of Dante's death.
As a sequel, "Hannibal" may not be up to "The Godfather: Part II" level, but director Ridley Scott and lead Hopkins have presented us with a buffet worth lingering over.
B+
For more Reeling reviews visit www.reelingreviews.com
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---
title: "PRUNING 101"
date: "2000-01-01 20:03:03 -06:00"
categories:
- adult
subtitle: ""
layout: "events"
event-image: "pruning.jpg"
event-dates: "TUESDAY, MARCH 3
& SATURDAY, MARCH 7"
event-location: "Red Butte Garden Classroom"
button-type: "register"
tickets-link: "https://continue.utah.edu/lifelong/class/llhg_505_pruning_101_workshop"
registration-info: "Contact Info:<PHONE_NUMBER>
Lifelong Learning: LLHG 505"
instructor-info: "Crystal Kim"
member-cost: "$69"
public-cost: "$75"
event-notes: ""
tickets-button: ""
published: false
---
REGISTRATION REQUIRED
TUESDAY, MARCH 3, 6 - 9PM and SATURDAY, MARCH 7, 10PM - 1PM
Proper pruning enhances the beauty of almost any landscape tree and shrub. It is an art and science, and when done well, pruning can turn a landscape plant into a thing of beauty. This is a pruning basics class and intended for those with limited pruning experience and will address basic pruning concepts associated with trees and shrubs, as well as the shaping of hedges. Classroom session is lecture and will cover pruning tools, when and why to prune, and the differences between thinning and heading back. The Saturday class will be in the field getting hands-on experience so please wear appropriate clothing and bring gloves and hand pruners. If you are interested in focused pruning techniques for roses, wisteria, or fruit trees, please see additional classes. Co-sponsored with Lifelong Learning. Members pay $69; register for section 002.


Written by: Jeff Greenstein & Jeff Strauss
Transcribed by: Mindy Mattingly Phillips
With Minor Adjustments by: Dan Silverstein
[Scene: Central Perk, all six are there.]
Monica: Would you let it go? It's not that big a deal.
Ross: Not that big a deal? It's amazing. Ok, you just reach in there, there's one little maneuver, and bam, a bra right out the sleeve. All right, as far as I'm concerned, there is nothing a guy can do that even comes close. Am I right?
Rachel: Come on!
Chandler: We can? All right, I'm tryin' that.
Joey: Ok, you know what blows my mind? Women can see breasts any time they want.  How you get any work done is beyond me.
Phoebe: Oh, ok, you know what I don't get? The way guys can do so many mean things, and then not even care.
(Long pause.)
Ross: Multiple orgasms!
[Scene: Central Perk, all are there.]
Chandler: So, Saturday night, the big night, date night, Saturday night, Sat-ur-day night!
Joey: No plans, huh?
Chandler: Not a one.
Ross: Not even, say, breaking up with Janice?

Monica: Chandler, nobody likes breaking up with someone. You just gotta do it.

Joey: Why do you have to break up with her? Be a man, just stop calling.
Phoebe: You know, if you want, I'll do it with you.
Chandler: Oh, thanks, but I think she'd feel like we're gangin' up on her.
Phoebe: No, I mean you break up with Janice and I'll break up with Tony.
Ross: Tony?
Monica: Oh, you're breaking up with Tony?
Phoebe: Yeah, I know, he's sweet, but it's just not fun anymore, you know? I don't know if it's me, or his hunger strike, or, I don't know.
Rachel: (waitressing) Does anybody want anything else?
Ross: Oh, yeah, last week you had a wonderful, nutty, chocolatey kind of a cakey pie thing.
Phoebe: (to Rachel) What's the matter? Why so scrunchy?
Rachel: It's my father. He wants to give me a Mercedes convertible.
Ross: That guy, he burns me up.
Rachel: Yeah, well, it's a Mercedes if I move back home. Oh, it was horrible. He called me young lady.
Chandler: Ooh, I hate when my father calls me that.
Monica: Did he give you that whole "You're-not-up-to-this" thing again?
Rachel: Oh, yeah, yeah. Actually, I got the extended disco version, with three choruses of "You'll never make it on your own".
Phoebe: (rhythmically) Uh-huh, uh-huh.
(Angela, a beautiful woman in a tight dress, enters.)
Angela: Hi, Joey.
Joey: My god, Angela.
(Angela takes a seat at the counter.)
Monica: Wow, being dumped by you obviously agrees with her.

Joey: No, yeah, no, ok, but not yet. I don't wanna seem too eager. One Mississippi, two Mississippi, three Mississippi. That seems pretty cool. (he walks over to her) Hey, Angela.
Angela: (casually) Joey.
Joey: You look good.
Angela: That's because I'm wearing a dress that accents my boobs.
Joey: You don't say.
(Cut to Ross and Rachel, talking next to one of the tables.)
Ross: So, uh, Rachel, what are you, uh, what're you doing tonight?
Rachel: Oh, big glamour night. Me and Monica at Laundorama.
 Guess who's doing laundry there too?
Rachel: Who?
Ross: Me. Was that not clear? Hey, why don't, um, why don't I just join you both, here?
Rachel: Don't you have a laundry room in your building?
Ross: Yes, I do have a laundry room in my building, um, but there's a.... rat problem. Apparently they're attracted to the dryer sheets, and they're goin' in fine, but they're comin' out all.... fluffy. Anyway, say, sevenish?
Rachel: Sure.
(Cut back to Joey and Angela at the counter.)
Angela: Forget it Joey. I'm with Bob now.
Joey: Bob? Who the hell's Bob?
Angela: Bob is great. He's smart, he's sophisticated, and he has a real job.
Joey: Come on, we were great together. And not just at the fun stuff, but like, talking too.
Angela: Yeah, well, sorry, Joe. You said let's just be friends, so guess what?
Joey: What?
Angela: We're just friends.
Joey: Fine, fine, so, why don't the four of us go out and have dinner together tonight? You know, as friends?
Angela: What four of us?
Joey: You know, you and Bob, and me and my girlfriend, uh, uh, Monica.
[Scene: Monica and Rachel's apartment, Joey is there, trying to convince Monica to pose as his girlfriend. His plan is to hook Monica up with Angela's boyfriend Bob and then take Angela back for himself.]
Joey: Monica, I'm tellin' you, this guy is perfect for you.
Monica: Forget it. Not after your cousin who could belch the alphabet.
Joey: Come on. This guy's great. His name's Bob. He's Angela's... brother. He's smart, he's sophisticated, and he has a real job. Me, I go on three auditions a month and call myself an actor, but Bob is...
Monica: (looking out window) Oh, god help us.
Joey: What?
Monica: Ugly Naked Guy's laying kitchen tile. Eww!
Joey: Eww! Look, I'm asking a favor here. If I do this for her brother, maybe Angela will come back to me.
Monica: What's going on here?
Joey: (proud) I know, but, I made a huge mistake. I never should have broken up with her. Will you help me? Please?
[Scene: Ross' apartment, Chandler is over.]
Ross: (on phone) Ok, bye. (hangs up) Well, Monica's not coming, it's just gonna be me and Rachel.
Chandler: Oh. Well, hold on camper, are you sure you've thought this thing through?
Ross: It's laundry. The thinking through is minimal.
Chandler: It's just you and Rachel, just the two of you? This is a date. You're going on a date.
Ross: Nuh-uh.
Chandler: Yuh-huh.
Ross: So what're you saying here? I should shave again, pick up some wine, what?
Chandler: Well, you may wanna rethink the dirty underwear. This is basically the first time she's gonna see your underwearyou want it to be dirty?
Ross: (sheepish) No.
Chandler: Oh, and uh, the fabric softener?
Ross: Ok, ok, now what is wrong with my Snuggles? What, it says I'm a sensitive, warm kinda guy, you know, like a warm, fuzzy bear. Ok, I can pick something else up on the way.
Chandler: There you go.
[Scene: A fancy restaurant, Joey and Monica are there, meeting Angela and Bob, who Monica thinks is Angela's brother.]
Monica: Thank you. So what does this Bob guy look like? Is he tall? Short?
Joey: Yep.
Monica: Which?
Joey: Which what?
Monica: You've never met Bob, have you?
Joey: No, but he's...
Monica: Oh my god, Joey, for all we know this guy could be horribly...
(Angela and Bob walk in. Bob is good-looking.)
Angela: Hey, Joey.
Monica: ...horribly attractive. I'll be shutting up now.
[Scene: Central Perk, Chandler and Phoebe are there, both ready to break up with their significant others.]
Chandler: Where are they? Where are they?
Phoebe: This is nice. We never do anything just the two of us.
Chandler: It's great. Maybe tomorrow we can rent a car and run over some puppies.
Phoebe: Eww, I don't wanna do that.
(Janice and Phoebe's boyfriend, Tony, walk in.)
Chandler: Here we go.
Phoebe: Ok, have a good break-up.
Chandler: Hey, Janice.
 I had the most supremely awful day.
Chandler: Hey, that's not good. Can I get an espresso and a latte over here, please?
Janice: We got the proofs back from that photo shoot, you know, the one with the little vegetables.
(Chandler sees Phoebe breaking up with Tony. She talks to him for a few seconds, hugs him, and then he leaves. Chandler is amazed how easy it was for her.)
Chandler: What?
Janice: What?
Chandler: (covering) What... did you get me there?
Janice: I got you...these. (pulls out a pair of socks)
Chandler: Bullwinkle socks. That's so sweet.
Janice: Well, I knew you had the Rockys, and so I figured, you know, you can wear Bullwinkle and Bullwinkle, or you can wear Rocky and Rocky,or, you can mix and match, moose and squirrel. Whatever you want.
Chandler: That's great.
(The drinks arrive, and Chandler downs his espresso in one gulp.)
Chandler: Well, I'm gonna get another espresso. Can I get you another latte?
Janice: (holding the full cup) No, no, I'm still working on mine.
(Chandler walks over to the counter where Phoebe is, and is asking her about the break-up.)
Chandler: That's it?
Phoebe: Yeah, it was really hard.
Chandler: Oh, yeah, that hug looked pretty brutal.
Phoebe: Ok, you weren't there.
[Scene: The Launderama, Rachel is there, waiting for Ross. An old woman takes Rachel's clothes off the machine and begins loading it with her things.]
Woman: Comin' through. Move, move.
Rachel: Oh, 'scuse me. I was kinda using that machine.
Woman: Yeah, well, now you're kinda not.
Rachel: But I saved it. I put my basket on top.
Woman: Oh, I'm sorry, is that your basket? It's really pretty. Unfortunately, I don't see suds.
Rachel: What?
Woman: No suds, no save. Ok?
(Ross arrives.)
Ross: What's goin' on?
Rachel: Hi, uh, nothing. That horrible woman just took my machine.
Ross: Was your basket on top?
Rachel: Yeah, but, there were no suds.
Ross: So?

Ross: No suds? Excuse me, hold on a second. (to woman) That's my friend's machine.
Woman: Hey, hey, hey, her stuff wasn't in it.
Ross: Hey, hey, hey, that's not the rule and you know it.
(The woman and Ross stare at each other. Finally she takes her stuff out of the machine and leaves.)
Ross: (to the crowd in the laundromat) All right, show's over. Nothing to see here. (to Rachel) Ok, let's do laundry.
Rachel: That was amazing. I can't even send back soup.
Ross: Well, that's because you're such a sweet, gentle, uh...Do you, uh, do you...Oh, hey, uh you must need detergent.
(Ross pulls out a huge box of laundry detergent.)
Rachel: What's that?
Ross: Uberveiss. It's new, it's German, it's extra-tough.
(Rachel starts to load her clothes.)
Ross: Rach, do you uh, are you gonna separate those?
Rachel: Oh god. Oh, am I being like a total laundry spaz? I mean, am I supposed to use like one machine for shirts and another machine for pants?
Ross: Rach, have you never done this before?
Rachel: Well, not myself, but I know other people that have. Ok, you caught me. I'm a laundry virgin.
Ross: Uh, well, don't worry, I'll use the gentle cycle. Ok, um, basically you wanna use one machine for all your whites, a whole nother machine for colors, and a third for your uh, your uh, delicates, and that would be your bras and your under-panty things.
 Would they go with whites or delicates?
Ross: (visibly nervous) Uh, that, that, that would be a judgment call.
[Scene: Fancy restaurant, Monica, Joey, Angela, and Bob are seated at the table.]
Monica: (to Joey) He is so cute. (to Angela and Bob) So, where did you guys grow up?
Angela: Brooklyn Heights.
Bob: Cleveland.
Monica: How, how did that happen?
Joey: Oh my god.
Monica: What?
Joey: I suddenly had the feeling that I was falling. But I'm not.
[Scene: Fancy restaurant, Joey and Bob are talking.]
Joey: So, you and Angela, huh?
Bob: Yep. Pretty much.
 You know what I miss the most about her? That cute nibbly noise when she eats. Like a happy little squirrel, or a weasel.
Bob: Huh, I never really noticed.
Joey: Oh, yeah, yeah, listen for it.
Bob: Monica, Monica is great.
Joey: Yeah, but it's not gonna last. She's too much for me in bed. Sexually.
[Scene: The ladies' bathroom at the restaurant, Monica and Angela are talking.]
Monica: I've gotta tell you, Bob is terrific.
Angela: Yeah, isn't he?
Monica: It is so great to meet a guy who is smart and funny, and has an emotional age beyond, like eight.
Angela: You know what else? He's unbelievable in bed.
Monica: Wow. My brother never even told me when he lost his virginity.
Angela: Huh. That's nice.
[Scene: Central Perk, Phoebe is coaching Chandler on how to break up with Janice.]
Phoebe: Ok, you can do this.  Just do it really fast, and then the wound is exposed.
(Chandler walks back to couch, where Janice is.)
Chandler: Janice. Hi, Janice. Ok, here we go. I don't think we should go out anymore. Janice.
Janice: All right. Well, there you go. (she gets extremely wound up, and begins to try and calm herself down) Stop it, stop it, stop it.
[Scene: The laundromat.]
Rachel: Ok, I know this is gonna sound really stupid, but I feel that if I can do this, you know, if I can actually do my own laundry, there isn't anything I can't do.
 You know, it's like the first time I had to make dinner for myself, after Carol left me? (the buzzer on the washer goes off) I'm sorry, that's all the time we have. Next on Ross...(opens up the washer) Uh-oh.
Rachel: What uh-oh?
Ross: (not wanting to tell her) Uh-oh, uh-oh, the laundry's done. It's, uh, it's a song. The laundry song that we sing. (singing) Uh-oh the laundry's done, uh-oh, uh-oh.
Rachel: Ross, what's the matter?
Ross: Nothing, nothing. Lee-lo, the laundry's done.
Rachel: Come on, show me.
Ross: All right, all right, it's just that you left a red sock in with all your whites, and now, everything's kinda pink.
Rachel: Oh, everything's pink.
Ross: Yeah, uh, except for the red sock, which is still red. I'm sorry, please don't be upset, it could happen to anyone.
Rachel: Except it didn't. It happened to me.  What am I doing? What am I doing? My father's right. I can't live on my own! I can't even do laundry!
(The woman who had tried to steal the washing machine walks by, and laughs.)
[Scene: The fancy restaurant, Angela has her hand in Bob's shirt, and Monica is very uncomfortable.]
Monica: Something went wrong with Underdog, and they couldn't get his head to inflate. So anyway, um, his head is like flopping down Broadway, right, and I'm just thinking... how inappropriate this is. Um, I've got something in my eye, uh, Joey, could we check it in the light, please?
(Her and Joey walk away from the table.)
Monica: Oh my god.
Joey: What?
Monica: Hello! Were we at the same table? It's like... cocktails in Appalachia.
Joey: Come on, they're close.
Monica: Close? She's got her tongue in his ear.

Monica: Joey, this is sick, it's disgusting, it's, it'snot really true, is it?
Joey: Well, who's to say what's true? I mean...

Joey: All right, look, I'm not proud of this, ok? Well, maybe I am a little.
Monica: (hits him lightly) Oh!
Joey: Ow!
Monica: (leaving) I'm outta here.
Joey: Wait, wait, wait. You want him, I want her. He likes you.
Monica: Really?
Joey: Yeah. I'm thinking, if we put our heads together, between the two of us, we can break them up.
[Time lapse, Monica accidentally spilled her drink on Bob's shirt and is wiping it off. Joey is making eyes at Angela.]
Monica: I'm so sorry, I can't believe I did this, but I couldn't stop laughing at your Norman Mailer story.
(Angela is eating chicken wings and making the weasel-like noise Joey had told Bob about.)
Joey: Uh, waiter, one more plate of chicken wings over here.
[Scene: Central Perk, Chandler is still trying to ease things over with Janice, and there are about a dozen empty Espresso cups in front of him. He is extremely wired.]
Chandler: Here's the thing, Janice.  I'm like the bing, bing, bing.
Janice: Ow!
Chandler: Oh, my god, I'm so sorry. Are you ok?
Janice: Ow. Um, it's just my lens. It's just my lens. I'll be right back.
(She leaves.)
Chandler: (to Phoebe) I hit her in the eye! I hit her in the eye! This is the worst break-up in the history of the world.
Phoebe: Oh my god. (Chandler downs another espresso.) How many of those have you had?
Chandler: Oh, I don't know, a million?
Phoebe: Chandler, easy, easy. Go to your happy place. La la la la la la la.
Chandler: I'm fine.
Phoebe: All right.
(Janice returns from the bathroom.)
Chandler: I'm not fine. Here she comes.
Phoebe: Wait here. Breathe.
(Phoebe goes over to speak to Janice. She talks to her for a few seconds, and then Janice immediately smiles, hugs her, waves to Chandler, and leaves.)
Chandler: How do you do that?
Phoebe: It's like a gift.
Chandler: We should always always break up together.
Phoebe: Oh, I'd like that.
[Scene: The Launderama. Rachel is sorting her now-pink clothes.]
Ross: You got the clothes clean. Now that's the important part.
Rachel: Oh, I guess. Except everything looks like jammies now.
(The same woman walks over and takes Rachel's laundry cart.)
Rachel: Whoa, I'm sorry. Excuse me. We had this cart.
Woman: Yeah, well, I had a 24-inch waist. You lose things. Now come on, get outta my way.
(Rachel looks at Ross, who motions to her to get the cart back.)
Rachel: I'm sorry, you know, maybe I wasn't being clear. Uh, this is our cart.
Woman: Hey, hey, hey there aren't any clothes in it.
Rachel: Hey, hey, hey, hey, quit making up rules!
Woman: Let go!
(They struggle for the cart. Finally, Rachel climbs inside of it.)
Rachel: All right, listen, missy. If you want this cart, you're gonna have to take me with it!
(She thinks it over, and then walks away.)
Rachel: (to Ross) Yes! Did you see that?
Ross: You were incredible! Brand new woman, ladies and gentlemen.
Rachel: I could not have done this without you.
(Rachel stands up and kisses Ross. He is stunned. A moment of silence follows.)
Ross: Ok, um, uh, more clothes in the dryer? (Ross turns and bangs his head on an open dryer door.) I'm fine, I'm fine.
Rachel: Are you sure?
Ross: No.
Closing Credits
[Scene: Central Perk, Ross, Rachel, and Phoebe are there. Ross has an icepack to his head.]
Rachel: Oh, are you sure you're ok?
Ross: Yeah.
Rachel: Does it still hurt?
Ross: Yeah.
Phoebe: (seeing Rachel's clothes) What a neat idea. All your clothes match. I'm gonna do this.
(Monica and Joey enter.)
Monica: Hi.
Phoebe: Hey, how'd it go?
Joey: Excellent.
Monica: We ripped that couple apart, and kept the pieces for ourselves.
Ross: What a beautiful story. Hey, I'm fine by the way.
Monica: (notices his head) Oh, I'm sorry.
Rachel: Where's Chandler?
Phoebe: Oh, he needed some time to grieve.
(Chandler runs by the window outside, joyous.)
Chandler: I'm free! I'm free!
Phoebe: That oughta do it.
End


Here's a list of film awards he's won over his career:
- 1972 British Academy TV Award for Best Actor - War and Peace
- 1976 Emmy Award for Outstanding Lead Actor - The Lindbergh Kidnapping Case
- 1981 Emmy Award for Outstanding Lead Actor - The Bunker
- 1985 CableACE Award for Actor in a Movie - Mussolini and I
- 1987 15th Moscow International Film Festival Award for Best Actor - 84 Charing Cross Road
- 1991 Academy Award for Best Actor - The Silence of the Lambs
- 1991 BAFTA Award for Best Actor in a Leading Role - The Silence of the Lambs
- 1991 Boston Society of Film Critics Best Supporting Actor - The Silence of the Lambs
- 1991 Chicago Film Critics Assoc Award for Best Actor - The Silence of the Lambs
- 1991 Dallas Film Critics Assoc Award for Best Actor - The Silence of the Lambs
- 1991 Kansas City Film Critics Award for Best Actor - The Silence of the Lambs
- 1991 National Board of Review Best Supporting Actor - The Silence of the Lambs
- 1991 NY Film Critics Circle Award for Best Actor - The Silence of the Lambs
- 1991 Saturn Award for Best Actor - The Silence of the Lambs
- 1993 BAFTA Award for Best Actor in a Leading Role - Shadowlands
- 1993 LA Film Critics Assoc Award for Best Actor - Shadowlands
- 1993 National Board of Review Award for Best Actor - Shadowlands
- 1993 SE Film Critics Assoc Award for Best Actor - Shadowlands
- 1997 Broadcast Film Critics Assoc Award for Best Supporting Actor - Amistad
- 2003 Hollywood Film Festival Best Male Performer - The Human Stain
- 2005 New Zealand Screen Award Best Leading Role - The World's Fastest Indian
- 2006 Hollywood Film Festival Award for Ensemble fo the Year - Bobby
- 2013 St. Louis Gateway Film Critics Assoc Special Merit - Hitchcock
"Still, it can be more effective to accomplish what you need to accomplish with the minimum effort. Watch Anthony Hopkins. He doesn't appear to be doing anything. He is so still that you can't see him working, but you are drawn into his character through his very stillness."
- Morgan Freeman
If you have time, you should read more about this incredible human being on his Wikipedia entry.


Security Agents Stop Journalists Movement In Bayelsa Election
by George OgboluSecurity personnel are reportedly preventing newsmen from covering the ongoing governorship election in Bayelsa state.
Naija News reports that the Bayelsa State Governorship election is currently ongoing.
In parts of Yenagoa, the state capital, soldiers and policemen denied accredited journalists and election observers vehicular movement despite explanation and identification.
Asked by TheCable why the restriction, a soldier simply identified as Austin, said: “that is the order”.
Residents have also complained about the delay in starting the voting process.
In some parts of the town visited by TheCable, INEC officials and election materials were not on ground while voters were stranded.
A resident, who simply identified himself as Obi expressed concern that “the election may not hold in some areas as INEC is not around”.
“You can see there is nothing here, it’s not even looking like election is holding. They may not conduct it,” he said.
.


A matrix which contains four elements in a square pattern.
eg $ \left( \begin{array}{cc} 0 & 4 \\ 5 & 3 \end{array} \right) $


Coach of Kerala Express Train Derails in Andhra Pradesh, All Passengers Safe
As of now, the cause of the derailment is being ascertained.
New Delhi: A coach of Kerala Express which was going from Delhi to Trivandrum on Saturday derailed near Yerpedu railway station in Chittoor, Andhra Pradesh, news agency ANI reported.
No passenger was harmed during the incident. Everybody is safe.
As of now, the cause of the derailment is being ascertained.
Speaking to ANI, South Central Railway CPRO said, “Pantry car of the train derailed. All passengers safe. Officials&technical staff have reached the spot&checking cause of derailment.”
In an earlier which took place on November 11, at least twelve people were injured after coaches of two trains collided at the Kacheguda railway station in Hyderabad.
The incident took place after 3 coaches of Lingampalli-Falaknuma train and 4 coaches of Kurnool City-Secunderabad Hundry Express derailed. Reports claimed that he mishap took place due to a fault in the signal. All the injured were shifted to Osmania General Hospital.
A high-level inquiry was also ordered into the collision.
For breaking news and live news updates, like us on Facebook or follow us on Twitter and Instagram. Read more on India Latest News on India.com.
Story Timeline
- 06 Oct 2019 2 Coaches of Lucknow-Anand Vihar Double-Decker AC Express Derailed, No Casualties
- 02 Oct 2019 Mumbai: Train Coach Derails on Harbour Line Near Bandra Station, Services Disrupted
- 28 Aug 2019 UP: 4 Coaches of a Train Derail at Kanpur Central Railway Station, no Injuries Reported
- 11 Aug 2019 Mumbai: 2 Coaches of Thane-Vashi Local Derails, Buses Arranged For Stranded Passengers
Comments - Join the Discussion


Triangle Tracker
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Memory Game
A school project to make a Memory Game only using C
Oupsi Doupsi Wulubdub Life is just a giggle on the breath of the universe
I m a Software Engenering Student at Ensias.I'm very passionate about Math ,Physics and Computer Science
.
I'm majoring in Computer Science , I love making games ,Websites and automating my tasks..
I love Learning and building new ideas and making them into reality.
Junior Developer
A school project to make a Memory Game only using C
Built using Libgdx(java framework for gamedevelopement) and Java
Game of Life automata buid using java and javaswing libs
Maze Generation using DFS written in C++ check my Github for more insights
The Game's Concept blends with survival theme, you have less than a minute in a procedual generated planet to find the UFO and escape avoiding the astroids that are the destroying the planet and making it on fire
Here is the Link to play: Fire Planet
Built using Unity all Done by myself(The game Design the pixel Art , SFX ,Programing...)
Here is the Link to play: Franky
I also Realsed it in google play store by the name "Franky"(VultureGames)


Memorial service set for 1970 Marshall plane crash
HUNTINGTON, W.Va. (AP) - An annual memorial service is set this week to honor the 75 lives lost in a 1970 plane crash involving Marshall University’s football team.
The service is set for noon Thursday in Huntington, West Virginia. Each year, the water at the Marshall Memorial Student Center’s fountain is turned off to remain silent until spring.
On Nov. 14, 1970, 36 football players and 39 coaches, administrators, community leaders, fans and crew died when the team’s chartered jet returning from a game at East Carolina crashed into a hill just short of the airport.
It remains the deadliest crash involving a sports team in U.S. history.
Next year will mark the crash’s 50th anniversary.
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Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


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$tempfile
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tearDown
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tearDown
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test_Can_Change_delimiter
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test_Csv_Reader_Accepts_Custom_Dialect
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Csv_Reader should also be able to accept a csv dialect in its constructor or by setDialect()
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test_Csv_Reader_Count
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Csv_Reader is an array-like object, so you should be able to count it
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test_Csv_Reader_Escape_Characters_Get_Removed
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Tests that escape characters are removed from data
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test_Csv_Reader_File_Does_Not_Exist
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If the file doesn't exist, we should throw an exception
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test_Csv_Reader_Get_Path
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We should get back the path to the csv file if the csv file exists
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test_Csv_Reader_Is_Iterable
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Tests that you can loop through Csv_Reader as if it was an array
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test_Csv_Reader_Maintains_State
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Test that class is capable of maintaining its state / position
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test_Csv_Reader_Reads_Row
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Tests that if the delimiter is set properly, rows are counted properly
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test_Csv_Reader_Removes_Blank_Lines
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Check that blank lines are properly skipped
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test_Csv_Reader_Uses_Default_Dialect_If_None_Provided
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Csv_Reader should use the default dialect if none is provied (excel for now)
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test_Csv_Writer_Accepts_Custom_Dialect
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Csv_Writer should also be able to accept a csv dialect in its constructor or by setDialect()
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test_Csv_Writer_Accepts_Filename_In_Constructor
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Writer should create the file if it doesnt exist
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test_Csv_Writer_Constructor_Creates_File_If_Nonexistant
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Writer should create the file if it doesnt exist (the close method will write changes to the db)
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test_Csv_Writer_Does_Not_Throw_Exception_If_Closed_Then_Destroyed
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test_Csv_Writer_Throws_Exception_If_Cant_Write
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An exception should be thrown if there is an error writing to the csv file
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test_Csv_Writer_Uses_Default_Dialect
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Writer should create the file if it doesnt exist
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test_Csv_Writer_Writes_Row
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Test that writeRow writes one row to the file
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test_Csv_Writer_Writes_Row_When_Destroyed
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Test that all pending changes are written to the file when the object is destroyed
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test_Dialect_Quote_All
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test_Dialect_Quote_Minimum
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test_Dialect_Quote_None
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Test that dialects work correctly with quoting and escaping things
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test_Dialect_Quote_Nonnumeric
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Test_Of_Csv_Reader
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Csv Reader unit tests
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Test_Of_Csv_Writer
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Csv Writer unit tests
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test_quotes_get_removed_from_data
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Test that class is capable of maintaining its state / position
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test_Writer_Accepts_Array
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Test that writer accepts a multi-dimensional array of rows to write to disk
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test_Writer_Accepts_Handle
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test_Writer_WriteRows_Accepts_Reader
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Test that writer accepts a reader object to read from instead of an array


Benelux mission is to spread the importance of helping each other within our community and world wide. With Benelux we can find the exact resources that are needed and health issues that are occuring in different countries. To provide a much quicker and effective way of reaching out and providing the help and hope that is waiting for our come.
With Benelux you can navigate through the map and click on the country or area that you want to help out in. Once you click on the country, the resources that are needed in that specific country will appear. Along with the top diseases in that country. The purpose of this is to inform what exactly is needed and what we can do to make sure that the community is receiving the needed medical care for their illnesses. The names of other helping organizations will appear; that way you can see what their role is. It's important to bring awareness to these problems and find a solution, making it accessible for everyone to have the medications and resources that are out there.
With just a click, you will find the information that you need to create a better tomorrow for the lives of all these people.
"The help they get from us is amazing."
"Thanks so much for making these free resources available to us!"


Made gghistogram() and checkresiduals() robust to missing values
All documentation now generated using roxygen
Improved documentation for many functions
Added autoplot.msts() and autolayer.msts()
Added as.character methods for many models to generate model names
Added as.ts.forecast()
autoplot method for bats/tbats models
Better ARIMA trace output
Made accuracy an S3 method
Bug fixes
Version 8.0 (22 February 2017)
Added tips to start up message
Added pipe operator
Added tsCV() and CVar() functions
Added baggedETS()
Added head.ts() and tail.ts(), so head and tail now work properly on ts objects.
Added gghistogram() and checkresiduals()
Added ggseasonplot with polar coordinates
Modified defaults for gglagplot
Added autolayer.ts
Added type argument to residuals() for different types of residuals
Added support for seas objects from the seasonal package
Component extraction for seasonal decomposition methods
Range bars for decomposition autoplots
Added autoplot.StructTS()
Added vignette based on 2008 JSS article by Hyndman and Khandakar
Improved ggplot functions
mforecast objects re-structured
Added as.data.frame.mforecast()
autoplot functions now exported
Refit support for arfima() and stlm()
Better bias adjustment support after Box-Cox transformation
print.ARIMA has better labelling of constants
Bug fixes
Removed fortify method for forecast objects
Version 7.3 (12 October 2016)
Added prediction intervals and simulation for nnetar().
Documentation improvement
Bug fixes
Version 7.2 (8 September 2016)
Faceting for autoplot.mts
Box-Cox support for ses, holt, hw
ets() now works for tiny time series
Added h-step fitted values in fitted() function.
seasonal adjustment added to thetaf()
y now the standard first argument in all modelling functions
Added truncate argument to auto.arima()
seasadj() now an S3 method
series with frequency < 1 and non-integer seasonality now handled better
ggplot2 theme support
Added gglagplot, gglagchull
Arima() and auto.arima() now allow any argument to be passed to stats::arima().
Bug fixes and speed improvements
Version 7.1 (14 April 2016)
Fixed bug in auto.arima where the Box-Cox transformation was sometimes applied twice
Improved axes for ggseasonalplot()
Improved tslm() to avoid some problems finding data
nnetar() updated to allow subsets()
Modified initial values for ets()
Improved unit tests to avoid deprecated functions and to avoid data from fpp
Removed fpp from Suggests list
Version 7.0 (3 April 2016)
Added ggplot2 graphics
Bias adjustment option added for all functions that allow Box-Cox transformations
Added Ccf function, and rewrote Acf to handle multivariate series.
tslm() completely rewritten to be more robust and to handle fourier terms more easily
Support for multivariate linear models added
subset.ts() more robust, and captures some errors.
Added xreg argument to nnetar()
Improved labels in seasonplot()
More unit tests added
Documentation improvements
Bug fixes
Version 6.2 (20 October 2015)
Many unit tests added using testthat.
Fixed bug in ets when very short seasonal series were passed in a data frame.
Fixed bug in nnetar where the initial predictor vector was reversed.
Corrected model name returned in nnetar().
Fixed bug in accuracy() when non-integer seasonality used.
Made auto.arima() robust to non-integer seasonality.
Fixed bug in auto.arima where allowmean was ignored when stepwise=FALSE.
Improved robustness of forecast.ets() for explosive models with multiplicative trends.
Exogenous variables now passed to VAR forecasts
Increased maximum nmse in ets() to 30.
Made tsoutliers() more robust to weak seasonality
Changed tsoutliers() to use supsmu on non-seasonal and seasonally adjusted data.
Fixed bug in tbats() when seasonal period 1 is a small multiple of seasonal period 2.
Other bug fixes
Version 6.1 (11 May 2015)
Made auto.arima more robust
Version 6.0 (9 May 2015)
Modified dm.test to give error when variance is zero
Corrected help file for splinef().
Fixed typo in accuracy help file regarding RMSE
Fixed bug in accuracy() which occurred with Arima and ets objects.
Fixed arima.errors() to handle Box-Cox transformed models.
Modified auto.arima() to be stricter on near-unit-roots.
Added allowmean argument in auto.arima().
Improved handling of constant series in Arima() and forecast.Arima().
Added plot.Arima() and plot.ar() functions.
Added as.character.Arima()
Captured problem in bats/tbats where data are constant.
Modified TBATS and BATS estimation to avoid occasional instabilities.
Fixed bug in forecasts from bats which labelled them as TBATS.
Added allow.multiplicative.trend argument to ets().
Set allow.multiplictive.trend=FALSE in stlf(), stlm() and forecast.ts().
Simplified arguments in stlf().
Added taperedacf and taperedpacf functions
Added functions for bootstrapping time series
Version 5.9 (26 February 2015)
Improved documentation of accuracy() function.
Fixed occasional bug in accuracy() when test set is a single observation.
Improved Acf() to give better handling of horizontal axis for seasonal data or when … is passed.
Removed print.Arima and predict.Arima and added print.ARIMA
method argument now passed when re-fitting an ARIMA model.
Fixed error when CH test applied to short series
Version 5.8 (6 January 2015)
Fixed bug in versions of R before 3.10 when using fourier and fourierf.
Made BoxCox.lambda() robust to missing values.
Version 5.7 (17 December 2014)
Fixed bug in tbats/bats where optional arguments were not being passed to auto.arima().
Revised fourier() and fourierf() to avoid large orders, and to avoid zero columns.
Improved accuracy of fourier() and fourierf(), while simplifying the code.
Removed duplicate columns returned by fourier/fourierf with multiple seasonal periods.
Corrected some bugs in simulate.Arima for models involving xreg.
Centred simulations from simulate.Arima for non-stationary models by conditioning on first observation.
Added findfrequency() function.
Fixed error in computed residuals from forecast.stl().
Improved handling of very short series in auto.arima().
Fixed error in forecasting with additive damped models. Damping previously applied only from second forecast horizon.
Fixed misuse of abs() in two places in C code.
Added na.action argument to Acf() and fixed na.action argument in tsdisplay().
Version 5.6 (23 September 2014)
Improved tbats and bats by ensuring ARMA coefficients are not close to the boundary of invertibility and stationarity.
Improved nsdiffs() handling of degenerate series (e.g., all zeros).
Improved forecast.ar() when function buried within other functions.
Improved handling of degenerate ARIMA models when xreg used.
More robust ets() initialization.
Fixed problem in na.interp() with seasonal data having frequency <= 5.
Removed undocumented option to use Rmalschains for optimization of ets().
Version 5.5 (12 August 2014)
Improved documentation for croston()
Added stlm() and forecast.stlm() functions, and added forecastfunction argument as a way of specifying a forecast method in stlf() and forecast.stl().
Improved forecast.ar() so that it is more likely to work if ar() and forecast.ar() are embedded within other functions.
Improved handling of ARIMA models with seasonality greater than 48
Improved handling of some degenerate regression models in nsdiffs()
Changed AIC for poor models from 1e20 to Inf.
Update fourier() and fourierf() to work with msts object.
Added a new argument find.frequency to forecast.ts().
Added new arguments d and D to accuracy() for MASE.
Corrected bugs in accuracy().
Better handling of regression models with perfect fit in auto.arima().
Fixed bug in tbats.components() when there are no seasonal components.
Version 5.4 (8 May 2014)
Fixed bug in forecast.tbats() and forecast.bats() when ts.frequency does not match seasonal.periods.
Fixed bug in getResponse.lm() when there’s a logged dependent variable.
Modified ets() to avoid problems when data contains large numbers.
Modified ets() to produce forecasts when the data are constant.
Improved arima.errors() to find xreg more often, and to return an error if it can’t be found.
Version 5.3 (24 March 2014)
Unit tests added
Fixed bug in zzhw() which reversed the sign of the residuals.
Updated help file for CV() to specify it is only leave-one-out.
Fixed guer.cv() to allow non-integer periods without warning.
Added use.initial.values argument in ets().
Added arimaorder() function.
Modified warnings suppression by using suppressWarnings() throughout.
Version 5.2 (25 February 2014)
Changed default number of cores to 2 for all functions that use parallel processing.
Removed remaining call to bats() from examples that are run.
Version 5.1 (8 February 2014)
Fixed bug in tsoutliers() and tsclean() with very short seasonal series.
Fixed bug in Arima() when seasonal order is specified numerically instead of via a list.
Removed dimension attribution from output of arima.errors()
Improved handling of “test” in accuracy()
Changed parallel processing to parLapply for auto.arima()
Added timeDate dependency to avoid errors in easter() and link to Rcpp >= 0.11.0.
Version 5.0 (17 January 2014)
Added argument model to dshw().
Added bizdays() and easter() for calendar variables.
Added arguments max.D and max.d to auto.arima(), ndiffs() and nsdiffs().
Made several functions more robust to zoo objects.
Corrected an error in the calculation of AICc when using CV().
Made minimum default p in nnetar equal to 1.
Added tsoutliers() and tsclean() for identifying and replacing outliers
Improved na.interp() to handle seasonality and added argument lambda to na.interp()
Added robust option to forecast.ts() to allow outliers and missing values
Improved output from snaive() and naive() to better reflect user expectations
Allowed Acf() to handle missing values by using na.contiguous
Changed default information criterion in ets() to AICc.
Removed drift term in Arima() when d+D>1.
Added bootstrap option to forecast.Arima()
Version 4.8 (30 September 2013)
Fixed bug in rwf() that was introduced in v4.7
Version 4.7 (26 September 2013)
Added forecast.forecast() to simply return the object that is passed.
Removed leading zero in package number. i.e., 4.7 instead of 4.07.
better handling of nearly constant time series, and nearly linear time series
improved handling of missing values in rwf()
corrected fitted values and residuals in meanf() for time series data
bats() and tbats() now handle missing values in the same way as ets(). i.e., using longest contiguous portion.
better handling of very short time series
initial states for ets() modified for very short time series (less than 3 years).
nsdiffs with CH test now handles degenerate cases without returning an error.
nnetar now handles missing values
Fixed bug in forecast.varest() so residuals and fitted values computed correctly.
Added accuracy() calculation for VAR models
Fixed a bug in simulate.fracdiff() when future=TRUE. Sometimes the future argument was being ignored.
Version 4.06 (30 June 2013)
accuracy() was returning a mape and mpe 100 times too large for in-sample errors.
Version 4.05 (19 June 2013)
Fixed bug in hw() so it works when initial=“simple”
Allowed bats() and tbats() to take non-positive values.
ets() now calls optim direct via c code making ets() run much faster.
Added Rmalschains as a possible optimizer in ets(). Not documented.
Modified forecast.lm so it is more likely that the original data are stored in the returned object.
Corrected bug in forecast.Arima that occurred when a Box-Cox transformation was used with bootstrap=TRUE.
accuracy() updated so that it gives more information, and returns a matrix of both test and training measures.
Corrected training error measures for splinef() forecasts.
Version 4.04 (22 April 2013)
Added ylim argument to Acf()
Avoided clash with the signal package when using auto.arima().
Fixed problem in plot.forecast() when all historical data are NA or when there is no available historical data.
forecast.Arima() is now a little more robust if a zoo object is passed instead of a ts object.
CV() now handles missing values in the residuals.
Fixed bug in holt() and hw() so that the printed model no longer contains missing values.
Version 4.03 (14 March 2013)
forecast.lm now guesses the variable name if there is only one predictor variable.
Removed error trap in forecast.lm when no xreg variables passed as it was catching legitimate calls.
Version 4.02 (6 March 2013)
Fixed error in the prediction intervals returned by forecast.ets() when simulation was used and a Box-Cox transformation was specified.
Fixed bug in accuracy() when a numerical f vector was passed.
Fixed man file for Diebold-Mariano test.
Corrected references in nsdiffs() help page.
Added warning to nsdiffs when series too short for seasonal differencing.
Fixed problem in getResponse.Arima when Arima object created by stats::arima() from within a function.
Added tbats.components() and extended seasadj() to allow tbats objects.
Added undocumented functions for forecasting, printing and plotting output from vars::VAR.
Version 4.01 (22 January 2013)
Error now trapped when newxreg variables not passed to forecast.lm()
Corrected help file for dshw() to remove references to prediction intervals.
Improved help file for dm.test() to give more information about the alternative hypotheses.
Improved dm.test() performance for small samples by using a t-distribution instead of normal.
Modified bats() and tbats() examples to follow CRAN policies on parallel processing.
Moved some packages from Depends to Imports.
Added getResponse() function to return the historical time series from various time series model objects.
Modified accuracy() to use getResponse().
Allowed user-generated innovations in simulate.ets(), simulate.Arima(), etc.
Allowed xreg argument in forecast.stl() and stlf() when ARIMA model used.
Removed reliance on caret, and associated fitted and residuals functions.
Version 4.00 (27 November 2012)
More robust handling of degenerate ARIMA models.
New defaults for shaded colors used for prediction intervals in plots.
auto.arima() now remembers the name of the series when a Box-Cox transformation is used.
New function nnetar() for automatic neural network forecasting of time series.
arfima() now tries harder to ensure the ARMA part is stationary.
ts control added for forecast of linear models in forecast.lm().
Fixed bug in bats() which caused an error when use.box.cox=FALSE and use.trend=FALSE.
Added residuals and fitted methods for train and avNNet objects from caret package.
accuracy() can now figure out overlapping times for x and f.
rwf() now handles missing values.
Revised ses(), holt() and hw() so that they can optionally use traditional initialization.
Version 3.25 (11 September 2012)
Fixed bug in simulate.Arima.
Improved handling of short seasonal time series in auto.arima().
Added seasonal argument to auto.arima().
Fixed bug in splinef() and added gcv method for estimating smoothing parameter.
Version 3.24 (23 July 2012
Fixed bug in auto.arima() introduced in v3.23 which meant a ARIMA(0,0,0) model was returned about half the time.
Version 3.23 (18 July 2012)
Fixed bug in arfima() which meant the drange argument was being ignored.
Extended auto.arima() so it returns something sensible when the data are constant.
Version 3.22 (07 June 2012)
Increased maximum forecast horizon for ets models from 2000 to unlimited.
Corrected bug in Arima(). Previously include.constant=FALSE was ignored.
Some corrections to bats and tbats.
Modified parallel implementation in auto.arima for Windows.
Version 3.21 (26 April 2012)
Fixed bug in auto.arima() when lambda is non-zero and stepwise is FALSE.
Fixed bug in auto.arima() in selecting d when D>0.
Fixed bug in ets() when seasonal period is less than 1.
Turned off warnings in auto.arima() and ets() when seasonal period is less than 1.
Added plotting methods for bats and tbats objects.
Changed default forecast horizons for bats and tbats objects.
Modified bats and tbats so they now use seasonal.periods when ts and msts objects are being modelled.
Version 3.20 (2 April 2012)
Fixed bugs in forecast.lm().
Improved handling of newdata in forecast.lm() to provide more meaningful error messages.
Fixed bug in dm.test() that occurred when errors were very small.
Version 3.19 (22 February 2012)
Improved plotting of forecast objects from lm models
Added MASE for lm forecasts using insample mean forecasts for scaling.
Modified definition of MASE for seasonal time series to use seasonal naive insample scaling.
Modified meanf() to allow it to be used with cross-sectional data.
Updated accuracy() to allow it to be used with cross-sectional data, lm forecasts and lm objects.
Version 3.18 (17 February 2012)
Added method for plotting non-time-series forecasts to plot.forecast().
Removed partial arg matching.
Cleaned up some code, removing commented out sections, etc.
Added robust option to stlf().
Added naive and rwdrift options to stlf() and forecast.stl().
Improved handling of msts objects in BoxCox.lambda()
Fixed some minor bugs in tbats() and bats()
Improved speed of bats() and tbats().
Version 3.17 (2 February 2012)
Improved forecast.lm() so it is more likely to find the original data from an lm object.
Parallel processing now available in auto.arima() when stepwise=FALSE
Default model selection in auto.arima() changed to AICc rather than AIC. This may affect model selection for very short time series.
max orders in auto.arima() now restricted to be less than 1/3 of length of data.
Version 3.16 (24 December 2011)
Corrected problem with AIC computation in bats and tbats
Fixed handlng of non-seasonal data in bats()
Changed dependency to >= R 2.14.0 in order to ensure parallel package available.
Version 3.15 (22 December 2011)
New functions tbats() and forecast.tbats() for multiple seasonal time series modelling.
bats() and tbats() use parallel processing when possible.
Minor improvements to bats() and forecast.bats().
decompose() removed as the function in the stats package has now been fixed.
Version 3.14 (9 December 2011)
Improved documentation for forecast.ts()
Corrected bug in dshw() when applied to a non-ts object.
Added error message when dshw() applied to data containing zeros or negative values
Added checks when dshw() applied to time series with non-nested periods.
Added msts object class for multiple seasonal time series
Made taylor data set an msts object.
Added bats() function for multiple seasonal time series modelling
Added forecast.bats() function for forecasting BATS models
Byte compiling turned on
Depending on Rcpp and RcppArmadillo to speed some code up.
Version 3.13 (19 November 2011)
Bug fix for forecast.StructTS() due to changes in the StructTS object. The default h was being set to 0. Thanks to Tarmo Leinonen for reporting this problem.
Bug fix for forecast.stl() where h longer than one seasonal period sometimes returned missing forecasts. Thanks to Kevin Burton for reporting this problem.
forecast.stl() no longer allows a seasonal ETS model to be specified. Thanks to Stefano Birmani for the suggestion.
Version 3.12 (16 November 2011)
Added option to control ets model in stlf() and forecast.stl(). Thanks to Stefano Birmani for the suggestion.
Reordered arguments for forecast.lm() and stlf() to be consistent with other forecast functions.
Modified tslm() so that it is more likely to find the relevant data when it is not passed as an argument.
Fixed bug in forecast.ets which returned all zero forecasts for some models when seasonal period > 24.
Version 3.11 (2 November 2011)
Fixed bug in dshw() when smallest period is odd
Version 3.10 (27 October 2011)
Added lambda argument to naive() and snaive().
Fixed bug in ets() with high frequency data.
Fixed bug in rwf() where incorrect fitted values and residuals were sometimes returned.
Modified number of lags displayed by default in tsdisplay().
Version 3.09 (18 October 2011)
Fixed bug causing occasional problems in simulate.Arima() when MA order greater than 2 and future=TRUE.
Version 3.08 (15 October 2011)
Bug fix in forecast.stl() which occurred when forecast horizon is less than seasonal period.
Added lambda argument to forecast.stl().
Version 3.07 (11 October 2011)
Bug fix in ets() concerning non-seasonal models and high-frequency data. It sometimes returned all forecasts equal to zero.
Version 3.06 (4 October 2011)
Switched to useDynLib in preparation for Rv2.14.0.
Version 3.05 (3 October 2011)
Fixed bug in ets() which prevent non-seasonal models being fitted to high frequency data.
Version 3.04 (23 September 2011)
Fixed bug when drift and xreg used together in auto.arima() or Arima().
Version 3.03 (2 September 2011)
Bug fix in dshw() which was using slightly incorrect seasonal estimates for the forecasts
Bug fix in forecast.StructTS due to change in structure of StructTS object.
Better error capture in tslm when seasonal dummies are specified for non-seasonal data.
Re-formatted some help files to prevent viewing problems with the pdf manual.
Version 3.02 (25 August 2011)
Bug fixes
Version 3.00 (24 August 2011)
Added Box-Cox parameter as argument to Arima(), ets(), arfima(), stlf(), rwf(), meanf(), splinef()
Added Box-Cox parameter as argument to forecast.Arima(), forecast.ets(), forecast.fracdiff(), forecast.ar(), forecast.StructTS, forecast.HoltWinters().
Removed lambda argument from plot.forecast() and accuracy().
Added BoxCox.lambda() function to allow automatic choice for Box-Cox parameter using Guerrero’s method or the profile log likelihood method.
Modified BoxCox and InvBoxCox to return missing values when lambda < 0 and data < 0.
Add nsdiffs() function for selecting the number of seasonal differences.
Modified selection of seasonal differencing in auto.arima().
Better error message if seasonal factor used in tslm() with non-seasonal data.
Added PI argument to forecast.ets() to allow only point forecasts to be computed.
Added include.constant argument to Arima().
Added subset.ts() function.
Upgraded seasonplot() function to allow colors and to fix some bugs.
Fixed fitted values returned by forecast.HoltWinters()
Modified simulate.Arima() because of undocumented changes in filter() function in stats package.
Changed residuals returned by splinef() to be ordinary residuals. The standardized residuals are now returned as standardizedresiduals.
Added dshw() function for double-seasonal Holt-Winters method based on Taylor (2003).
Fixed further bugs in the decompose() function that caused the results to be incorrect with odd frequencies.
Version 2.19 (4 June 2011)
Added xreg information to the object returned by auto.arima().
Added Acf(), Pacf(), ma() and CV() functions.
Fixed bugs in re-fitting ARIMA models to new data.
Version 2.18 (19 May 2011)
Fixed bug in seasonplot() where year labels were sometimes incorrect.
Version 2.17 (6 April 2011)
Modified simulate.Arima() to handle seasonal ARIMA models.
Modified ets() to handle missing values. The largest continuous section of data is now modelled.
Improved plot.forecast() to handle missing values at the end of the observed series.
Added replacement decompose() to avoid truncation of seasonal term and seasonally adjusted series.
Fixed bug in seasadj() to handle multiplicative decomposition, and to avoid missing values at ends.
Version 2.16 (6 March 2011)
Changed the way missing values are handled in tslm()
Version 2.15 (5 March 2011)
Added fourier(), fourierf(), tslm()
Improved forecast.lm() to allow trend and seasonal terms.
Version 2.14 (4 March 2011)
Added forecast.lm()
Modified accuracy() and print.forecast() to allow non time series forecasts.
Fixed visibility of stlf().
Version 2.13 (16 February 2011)
Fixed bug in accuracy() when only 1 forecast is specified.
Added forecast.stl() and stlf() functions
Modified forecast.ts() to use stlf() if frequency > 12.
Made BoxCox() and InvBoxCox() robust to negative values
Fixed bug in simulate.Arima() when future=TRUE. There was a bias in the sample paths.
Version 2.12 (19 January 2011)
Added naive() and snaive() functions.
Improved handling of seasonal data with frequency < 1.
Added lambda argument to accuracy().
Version 2.11 (5 November 2010)
If MLE in arfima() fails (usually because the series is non-stationary), the LS estimate is now returned.
Version 2.10 (4 November 2010)
Fixed bug in arfima() where the MA parameters were of the wrong sign if estim=“mle” chosen.
arfima() now allowed to have a sequence of missing values at the start of the series and end of the series (but not within the series)
Version 2.09 (15 October 2010)
Fixed bug in forecast.fracdiff() which caused an error when h=1.
Added shadebars to plot.forecast().
Fixed bug in plot.forecast() to allow plotting when h=1.
Version 2.08 (22 September 2010)
Added pp test option for auto.arima() and ndiffs().
Fixed bug in simulate.ets() which was causing problems when forecasting from some ETS models including ETS(M,M,N).
Version 2.07 (9 September 2010)
Fixed bug in simulate.Arima(). Previous sample paths when d=2 and future=TRUE were incorrect.
Changed way color is implemented in plot.forecast() to avoid colour changes when the graphics window is refreshed.
Version 2.06 (29 July 2010)
Added MLE option for arfima().
Added simulate.Arima(), simulate.ar() and simulate.fracdiff()
Version 2.05 (11 May 2010)
Added arfima() and a forecast method to handle ARFIMA models from arfima() and fracdiff().
Added residuals and fitted methods for fracdiff objects.
Version 2.04 (16 April 2010)
Fixed bug in auto.arima() that occurred rarely.
Version 2.03 (23 December 2009)
Added an option to auto.arima() to allow drift terms to be excluded from the models considered.
Version 2.02 (23 December 2009)
Fixed bug in auto.arima() that occurred when there was an xreg but no drift, approximation=TRUE and stepwise=FALSE.
Version 2.01 (14 September 2009)
Fixed bug in time index of croston() output.
Added further explanation about models to croston() help file.
Version 2.00 (7 September 2009)
Package removed from forecasting bundle
Version 1.26 (29 August 2009)
Added as.data.frame.forecast(). This allows write.table() to work for forecast objects.
Version 1.25 (22 July 2009)
Added argument to auto.arima() and ndiffs() to allow the ADF test to be used instead of the KPSS test in selecting the number of differences.
Added argument to plot.forecast() to allow different colors and line types when plotting prediction intervals.
Modified forecast.ts() to give sensible results with a time series containing fewer than four observations.
Version 1.24 (9 April 2009)
Fixed bug in dm.test() to avoid errors when there are missing values in the residuals.
More informative error messages when auto.arima() fails to find a suitable model.
Version 1.23 (22 February 2009)
Fixed bugs that meant xreg terms in auto.arima() sometimes caused errors when stepwise=FALSE.
Version 1.22 (30 January 2009)
Fixed bug that meant regressor variables could not be used with seasonal time series in auto.arima().
Version 1.21 (16 December 2008)
Fixed bugs introduced in v1.20.
Version 1.20 (14 December 2008)
Updated auto.arima() to allow regression variables.
Fixed a bug in print.Arima() which caused problems when the data were inside a data.frame.
In forecast.Arima(), argument h is now set to the length of the xreg argument if it is not null.
Version 1.19 (7 November 2008)
Updated Arima() to allow regression variables when refitting an existing model to new data.
Version 1.18 (6 November 2008)
Bug fix in ets(): models with frequency less than 1 would cause R to hang.
Bug fix in ets(): models with frequency greater than 12 would not fit due to parameters being out of range.
Default lower and upper bounds on parameters , and in ets() changed to 0.0001 and 0.9999 (instead of 0.01 and 0.99).
Version 1.17 (10 October 2008)
Calculation of BIC did not account for reduction in length of series due to differencing. Now fixed in auto.arima() and in print.Arima().
tsdiag() now works with ets objects.
Version 1.16 (29 September 2008)
Another bug fix in auto.arima(). Occasionally the root checking would cause an error. The condition is now trapped.
Version 1.15 (16 September 2008)
Bug fix in auto.arima(). The series wasn’t always being stored as part of the return object when stepwise=FALSE.
Version 1.14 (1 August 2008)
The time series stored in M3 in the Mcomp package did not contain all the components listed in the help file. This problem has now been fixed.
Version 1.13 (16 June 2008)
Bug in plot.ets() fixed so that plots of non-seasonal models for seasonal data now work.
Warning added to ets() if the time series contains very large numbers (which can cause numerical problems). Anything up to 1,000,000 should be ok, but any larger and it is best to scale the series first.
Fixed problem in forecast.HoltWinters() where the lower and upper limits were interchanged.
Version 1.12 (22 April 2008)
Objects are now coerced to class ts in ets(). This allows it to work with zoo objects.
A new function dm.test() has been added. This implements the Diebold-Mariano test for predictive accuracy.
Yet more bug-fixes for auto.arima().
Version 1.11 (8 February 2008)
Modifications to auto.arima() in the case where ML estimation does not work for the chosen model. Previously this would return no model. Now it returns the model estimated using CSS.
AIC values reported in auto.arima() when trace=TRUE and approximation=TRUE are now comparable to the final AIC values.
Addition of the expsmooth package.
Version 1.10 (21 January 2008)
Fixed bug in seasadj() so it allows multiple seasonality
Fixed another bug in print.Arima()
Bug fixes in auto.arima(). It was sometimes returning a non-optimal model, and occasionally no model at all. Also, additional stationarity and invertibility testing is now done.
Version 1.09 (11 December 2007)
A new argument ‘restrict’ has been added to ets() with default TRUE. If set to FALSE, then the unstable ETS models are also allowed.
A bug in the print.Arima() function was fixed.
Version 1.08 (21 November 2007)
AICc and BIC corrected. Previously I had not taken account of the sigma^2 parameter when computing the number of parameters.
arima() function changed to Arima() to avoid the clash with the arima() function in the stats package.
auto.arima now uses an approximation to the likelihood when selecting a model if the series is more than 100 observations or the seasonal period is greater than 12. This behaviour can be over-ridden via the approximation argument.
A new function plot.ets() provides a decomposition plot of an ETS model.
predict() is now an alias for forecast() wherever there is not an existing predict() method.
The argument conf has been changed to level in all forecasting methods to be consistent with other R functions.
The functions gof() and forecasterrors() have been replaced by accuracy() which handles in-sample and out-of-sample forecast accuracy.
The initialization method used for a non-seasonal ETS model applied to seasonal data was changed slightly.
The following methods for ets objects were added: summary, coef and logLik.
The following methods for Arima objects were added: summary.
Version 1.07 (25 July 2007)
Bug fix in summary of in-sample errors. For ets models with multiplicative errors, the reported in-sample values of MSE, MAPE, MASE, etc., in summary() and gof() were incorrect.
ARIMA models with frequency greater than 49 now allowed. But there is no unit-root testing if the frequency is 50 or more, so be careful!
Improvements in documentation.
Version 1.06 (15 June 2007)
Bug fix in auto.arima(). It would not always respect the stated values of max.p, max.q, max.P and max.Q.
The tseries package is now installed automatically along with the forecasting bundle, whereas previously it was only suggested.
Version 1.05 (28 May 2007)
Introduced auto.arima() to provide a stepwise approach to ARIMA modelling. This is much faster than the old best.arima().
The old grid-search method used by best.arima() is still available by using stepwise=FALSE when calling auto.arima().
Automated choice of seasonal differences introduced in auto.arima().
Some small changes to the starting values of ets() models.
Fixed a bug in applying ets() to new data using a previously fitted model.
Version 1.04 (30 January 2007)
Added include.drift to arima()
Fixed bug in seasonal forecasting with ets()
Version 1.03 (20 October 2006)
Fixed some DOS line feed problems that were bothering unix users.
Version 1.02 (12 October 2006)
Added AICc option to ets() and best.arima().
Corrected bug in calculation of fitted values in ets models with multiplicative errors.
Version 1.01 (25 September 2006)
Modified ndiffs() so that the maximum number of differences allowed is 2.
Version 1.0 (31 August 2006)
Added MASE to gof().
croston() now returns fitted values and residuals.
arima() no longer allows linear trend + ARMA errors by default. Also, drift in non-stationary models can be turned off.
This version is the first to be uploaded to CRAN.
Version 0.99992 (8 August 2006)
Corrections to help files. No changes to functionality.
Version 0.99991 (2 August 2006)
More bug fixes. ets now converges to a good model more often.
Version 0.9999 (1 August 2006)
Mostly bug fixes.
A few data sets have been moved from fma to forecast as they are not used in my book.
ets is now considerably slower but gives better results. Full optimization is now the only option (which is what slows it down). I had too many problems with poor models when partial optimization was used. I’ll work on speeding it up sometime, but this is not a high priority. It is fast enough for most use. If you really need to forecast 1000 series, run it overnight.
In ets, I’ve experimented with new starting conditions for optimization and it seems to be fairly robust now.
Multiplicative error models can no longer be applied to series containing zeros or negative values. However, the forecasts from these models are not constrained to be positive.
Version 0.999 (27 July 2006)
The package has been turned into three packages forming a bundle. The functions and a few datasets are still in the forecast package. The data from Makridakis, Wheelwright and Hyndman (1998) is now in the fma package. The M-competition data is now in the Mcomp package. Both fma and Mcomp automatically load forecast.
This is the first version available on all operating systems (not just Windows).
pegels has been replaced by ets. ets only fits the model; it doesn’t produce forecasts. To get forecasts, apply the forecast function to the ets object.
ets has been completely rewritten which makes it slower, but much easier to maintain. Different boundary conditions are used and a different optimizer is used, so don’t expect the results to be identical to what was done by the old pegels function. To get something like the results from the old pegels function, use forecast(ets()).
simulate.ets() added to simulate from an ets model.
Changed name of cars to auto to avoid clash with the cars data in the datasets package.
arima2 functionality is now handled by arima() and pegels2 functionality is now handled by ets.
best.arima now allows the option of BIC to be used for model selection.
Croston’s method added in function croston().
ts.display renamed as tsdisplay
mean.f changed to meanf, theta.f changed to thetaf, rw.f changed to rwf, seasonaldummy.f to seasonaldummyf, sindex.f to sindexf, and spline.f to splinef. These changes are to avoid potential problems if anyone introduces an ‘f’ class.
Version 0.994 (4 October 2004)
Fixed bug in arima which caused predict() to sometimes fail when there was no xreg term.
More bug fixes in handling regression terms in arima models.
New print.Arima function for more informative output.
Version 0.993 (20 July 2004)
Added forecast function for structural time series models obtained using StructTS().
Changed default parameter space for pegels() to force admissibility.
Added option to pegels() to allow restriction to models with finite forecast variance. This restriction is imposed by default.
Fixed bug in arima.errors(). Changes made to arima() meant arima.errors() was often returning an error message.
Added a namespace to the package making fewer functions visible to the user.
Version 0.99 (21 May 2004)
Added automatic selection of order of differencing for best.arima.
Added possibility of linear trend in arima models.
In pegels(), option added to allow parameters of an exponential smoothing model to be in the ‘admissible’ (or invertible) region rather than within the usual (0,1) region.
Fixed some bugs in pegels.
Included all M1 and M3 data and some functions to subset and plot them.
Note: This package will only work in R1.9 or later.
Version 0.98 (23 August 2003)
Added facilities in pegels. o It is now possible to specify particular values of the smoothing parameters rather than always use the optimized values. If none are specified, the optimal values are still estimated as before. o It is also possible to specify upper and lower bounds for each parameter separately.
New function: theta.f. This implements the Theta method which did very well in the M3 competition.
A few minor problems with pegels fixed and a bug in forecast.plot that meant it didn’t work when the series contained missing values.
Version 0.972 (11 July 2003)
Small bug fix: pegels did not return correct model when model was partially specified.
Version 0.971 (10 July 2003)
Minor fixes to make sure the package will work with R v1.6.x. No changes to functionality.
Version 0.97 (9 July 2003)
Fully automatic forecasting based on the state space approach to exponential smoothing has now been added. For technical details, see Hyndman, Koehler, Snyder and Grose (2002).
Local linear forecasting using cubic smoothing splines added. For technical details, see Hyndman, King, Pitrun and Billah (2002).
Version 0.96 (15 May 2003)
Many functions rewritten to make use of methods and classes. Consequently several functions have had their names changed and many arguments have been altered. Please see the help files for details.
Added functions forecast.Arima and forecat.ar
Added functions gof and seasadj
Fixed bug in plot.forecast. The starting date for the plot was sometimes incorrect.
Added residuals components to rw.f and mean.f.
Made several changes to ensure compatibility with Rv1.7.0.
Removed a work-around to fix a bug in monthplot command present in R v<=1.6.2.


;
Computer Architecture
Project
[X] Day 1: Get print8.ls8
running
- [X] Inventory what is here
- [X] Implement the
CPU
constructor
- [X] Add RAM functions
ram_read()
and ram_write()
- [X] Implement the core of
run()
- [X] Implement the
HLT
instruction handler
- [X] Add the
LDI
instruction
- [X] Add the
PRN
instruction
[X] Day 2: Add the ability to load files dynamically, get mult.ls8
running
- [X] Un-hardcode the machine code
- [X] Implement the
load()
function to load an .ls8
file given the filename passed in as an argument
- [X] Implement a Multiply instruction (run
mult.ls8
)
[X] Day 3: Stack
- [X] Implement the System Stack and be able to run the
stack.ls8
program
[X] Day 4: Get call.ls8
running
- [X] Implement the CALL and RET instructions
- [X] Implement Subroutine Calls and be able to run the
call.ls8
program
[ ] Stretch
- [ ] Add the timer interrupt to the LS-8 emulator
- [ ] Add the keyboard interrupt to the LS-8 emulator
- [ ] Write an LS-8 assembly program to draw a curved histogram on the screen


--- layout: page title: Video Abstract Submission author: Andrew permalink: /video/ --- {% if site.compass.logo %} {% endif %}


The packet_stats_callback class is an optional callback that can be registered with pisces_sender objects or packetizer objects to log events ocurring on particular packets.
More...
#include <pisces_stats.h>
|
| virtual void | collect_single_event (const pkt_arbitration_t &st) |
| | collect_single_event Collect stats associated with a single packet aribtration event. This is invokved by pisces_sender objects. More...
|
| |
| virtual void | collect_final_event (pisces_payload *pkt) |
| | collect_final_event Collects stats associated with flow-level packet event. This is attached to packetizer objecs the end of the flow (usually on a NIC) to log any important stats attached to a packet at the end of the path More...
|
| |
| int | id () const |
| | id Either a node or switch id, depending on the device More...
|
| |
|
|
| packet_stats_callback (sprockit::sim_parameters *params, event_scheduler *parent) |
| |
The packet_stats_callback class is an optional callback that can be registered with pisces_sender objects or packetizer objects to log events ocurring on particular packets.
| virtual void sstmac::hw::packet_stats_callback::collect_final_event |
( |
pisces_payload * |
pkt | ) |
|
|
virtual |
| virtual void sstmac::hw::packet_stats_callback::collect_single_event |
( |
const pkt_arbitration_t & |
st | ) |
|
|
virtual |
| int sstmac::hw::packet_stats_callback::id |
( |
| ) |
const |
|
inline |
id Either a node or switch id, depending on the device
- Returns
The documentation for this class was generated from the following file:


@(text: Option[Setting])(implicit req:Request[_], msgs:MessagesProvider, pcd:PageCustomizationData) @defaultText = {
This is a newly installed PolicyModels web server.
If you are a server admin, you can (and probably should) edit this page by logging in, and going to
@Messages("navbar.customize") > @Messages("navbar.customize.pages")


Objective
I would like to use my mad coding skillZ to save humanity from itself.
Bio
I was born a while ago, have done various things, and have lived in a number of different places.
Work History
Yes.
Skills
Too many to list. Seriously! I tried listing them all and it broke the web page.


6 steps to your
first process
with
JBoss BPM Suite starter kit
Have you used BPM and/or business rules solutions in the past?
- Never, this is all new to me
- No, just starting to investigate solutions
- Yes, made attempts to use in the past
- Yes, used solutions before, but not Open Source
- Yes, consider myself advanced solutions designer
BPM is not hard
Let's take it step by step...
The 6 steps
- Starter kit
- Intro rules, events & processes
- Finding products
- Quick tour video series
- First installation
- Hands on workshops
Step 1 - starter kit
JBoss BPM Suite product page
Demo
JBoss BPM Suite Getting Started...
Demo
Rules, events, processes...
Demo
Quick tour video series...
Step 5 - first install
- Install on your local machine
- Install with one-click on xPaaS (bpmPaas)
- Generate a containerized installation
Demo
- Get products...
- Get easy install project...
- Run installation...


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net session
Lists or disconnects sessions. Used with no options lists the current sessions.
Options:
\\<computer name> The computer of whose session to close
/delete Closes the session to the computer specified. Omitting a computer name will close all sessions


{% extends 'base.html' %} {% block content %}
Filter by location
Image Name
{{image.image_name}}
Image Description
{{image.image_description}}
Image Location
{{image.image_location.location}}
Image Category
{{image.image_category.category}}
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Hello there, I see you have stumbled upon my humble site. - Hon. Computer Science Class of 2024 - University of Waterloo ♠


| GEOS SDK TechDocs
|
| pvismon ...
| ret ...
read-reg
read-reg <register>
Examples:
-
"read-reg ax"
-
return the value of AX
-
"read-reg CC"
-
return the value of the conditional flags
Return the value of a register in decimal.
-
The <register> argument is the two letter name of a register in either upper or lower case.
See Also: frame,
assign,
setcc,
clrcc.
regs
regs
Print the current registers, flags, and instruction.
See Also: assign,
setcc,
clrcc,
read-reg.
regwin
regwin [off]
Examples: "regwin"
"regwin off"
Turn the continuous display of registers on or off.
-
If you give the optional argument "off", you will turn off any active register display.
-
If you give no argument, the display will be turned on.
-
Only one register display may be active at a time.
See Also: display.
repeatCommand
var repeatCommand <string>
Examples:
-
"var repeatCommand [list foo nil]"
-
Execute the command "foo nil" if the user just hits <Enter> at the next command prompt.
This variable holds the command Swat should execute if the user enters an empty command. It is used by all the memory-referencing commands to display the next chunk of memory, and can be used for other purposes as well.
-
repeatCommand
is emptied just before
top-level-read
returns the command the interpreter should execute and must be reset by the repeated command if it wishes to continue to be executed when the user just hits <Enter>.
-
The text of the current command is stored in
lastCommand
, should you wish to use it when setting up
repeatCommand
.
See Also: target,
focus,
mouse,
keyboard.
require
require <name> [<file>]
Examples:
-
"require fmtval print"
-
Ensure the procedure "fmtval" is defined, loading the file "print.tcl" if it is not.
This ensures that a particular function, not normally invoked by the user but present in some file in the system library, is actually loaded.
If no <file> is given, a file with the same name (possibly suffixed ".tcl") as the function is assumed.
See Also: autoload.
resize
resize <window> <numLines>
Examples:
-
"resize varwin 5"
-
Resizes the varwin to 5 lines.
-
"resize va 5"
- Resizes the varwin to 5 lines.
Resizes the source window or a varwin or localwin.
-
The
window
argument is the name of the window to be resized, or a unique abbreviation of that name. Possible names are: "varwin", "localwin", "view", "doc", and "srcwin"; the last three all refer to the source window.
-
The
numLines
argument is the desired window size.
-
This command will not resize a flagwin, regwin, or framewin, as the optimal size of those windows does not vary.
restore-state
restore-state
Examples:
-
"restore-state"
-
Set all registers for the current thread to the values saved by the most recent save-state.
Pops all the registers for a thread from the internal state stack.
-
This is the companion to the "save-state" command.
-
All the thread's registers are affected by this command.
See Also: save-state.
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Functions to calculate ADI (Average Dominance Index) and FDI (Frequency-Based Dominance Index). Functions to visualize the Data with Social Network Graphs with Dual Directions and Music Notation Graph.
| Version: | 1.1.0 |
| Depends: | R (≥ 3.1.0) |
| Imports: | igraph, chron, gdata, XLConnect |
| Published: | 2018-03-13 |
| Author: | Knut Krueger [aut, cre], Konstanze Krueger [aut] |
| Maintainer: | Knut Krueger <Knut.Krueger at equine-science.de> |
| License: | GPL-3 |
| URL: | NA |
| NeedsCompilation: | no |
| CRAN checks: | Dominance results |
| Reference manual: | Dominance.pdf |
| Package source: | Dominance_1.1.0.tar.gz |
| Windows binaries: | r-devel: Dominance_1.1.0.zip, r-release: Dominance_1.1.0.zip, r-oldrel: Dominance_1.1.0.zip |
| OS X binaries: | r-release: Dominance_1.1.0.tgz, r-oldrel: Dominance_1.1.0.tgz |
| Old sources: | Dominance archive |
Please use the canonical form https://CRAN.R-project.org/package=Dominance to link to this page.


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Thor: Ragnarok isn't just the best Thor movie. It's the most cosmic Marvel movie this side of the Guardians of the Galaxy series. And without any pesky, annoying Earthlings (ahem, Midgardians) hanging around to clog up the proceedings it's got more Marvel references and easter eggs per frame than any of its predecessors.
Thor: Ragnarok is a feast for Marvel fans, and it will probably take me a second viewing to catch everything in it, simply because in nearly every scene there's a design, a piece of architecture, or a background character who positively must have come from the comics page. So with the full understanding that I definitely missed something (or a few something), it's up to you, dear readers, to help me out. Spot something I didn't? Shout it out in the comments, or give me a holler on Twitter. If it checks out, I'll update this!
What is Ragnarok?
- Ragnarok, of course, is the Norse "twilight of the gods." Key points of the mythological Ragnarok that are explored here include the death of Odin and Thor losing an eye (although he usually has to do that to himself in order to gain knowledge, but we'll take the badass fight instead for cinematic purposes).
Marvel has touched on Ragnarok more than once in the comics, most notably in stories by Walt Simonson (whose influence is all over this movie) and more recently by Michael Avon Oeming and Andrea Di Vitto. It was in the latter that we see a couple of small elements in this movie, notably the shattering of Thor's hammer, the death of the Warriors Three, and Surtur's prominence.
The Villains
The first villain we meet in the movie is Surtur, and fans of Walt Simonson's take on Thor will be excited. While the character has been around since the Lee/Kirby days, it's really the Simonson stuff that made the character pop on the comics page, and the visual we get here is more in line with his vision.
- Surtur ends up kind of manifesting a giant snake, and I wonder if this is supposed to represent the Midgard Serpent, disturbance of which is another harbinger of the mythological Ragnarok.
Hela’s origin is different from both her comic and mythological versions. It's way too much to get into here, but we have a whole article about her comic origins for you.
- Her cinematic origins have a lot less in common with her comics counterpart than they do with Angela. Angela is...hoo boy...she's the lost daughter of Odin and Frigga killed in a secret war with the later hidden tenth realm, "heven." She was raised by the angels in the tenth realm after it was sealed off by Odin as punishment for their rebellion. She first appeared in the comics version of Age of Ultron and was revealed as Thor and Loki's sister in Original Sin. She's currently living out her best life, having conquered Hel to free the trapped soul of her girlfriend, then abdicating the throne.
Thor: Ragnarok - The Planet Hulk Connection
- So, Sakaar is, literally the "Planet Hulk" of the comics. Not that it's a planet full of Hulks or anything, but that is indeed where it all takes place. In the comics, though, Hulk didn't accidentally end up hurtling off into space, he was actually sent their by Tony Stark and Reed Richards because he's such a menace. It...it didn't end well for anyone involved.
But Hulk did indeed end up as a Gladiator on Sakaar (it had nothing to do with the Grandmaster). However, he didn't fight Thor in the arena...he fought the Silver Surfer!  This fight ALMOST happened in the animated adaptation of Planet Hulk, except there it was the Beta Ray Bill version of Thor in the arena with Hulk.
I'll be updating this throughout opening weekend, especially as I get more out of a second viewing. But in the meantime, if you've spotted something I missed, help us out in the comments or hit me up on Twitter!
Placeholder text by Denofgeek.com